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PG&E’s Emerging Technologies Program ET13PGE1021
Measurement and Verification for the Zero Net
Energy Stevens Library
ET Project Number: ET13PGE1021
‘
Project Manager: Peter Turnbull and Mananya Chansanchai Pacific Gas and Electric Company Prepared By: Jon Roberts, Shane Mason, Sarah Buddinger, Jim Maclay The Cadmus Group, Inc. 8105 Irvine Center Drive, Suite 150. Irvine, CA 92618
Issued: December 19, 2014
PG&E’s Emerging Technologies Program ET13PGE1021
Copyright, 2014, Pacific Gas and Electric Company. All rights reserved.
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PG&E’s Emerging Technologies Program ET13PGE1021
ACKNOWLEDGEMENTS
Pacific Gas and Electric Company’s Emerging Technologies Program is responsible for this project. It was developed as part of Pacific Gas and Electric Company’s Emerging Technology – Technology Assessments Program under internal project number ET13PGE1021. The Cadmus Group, Inc. conducted this technology evaluation for Pacific Gas and Electric Company with overall guidance and management from Mananya Chansanchai, Mangesh Basarkar and Peter Turnbull. Anna LaRue and Dr. Carrie Brown from Resource Refocus LLC and Loralyn Perry from Energy Matters provided ongoing technical support, guidance, and review. Sandy Dubinsky and Jeff Barton from Sacred Heart Schools provided were the primary client contacts. The architect was Pauline Souza, AIA, LEED Fellow from WRNS Studio. The MEP Engineer was Interface Engineering. The controls contractor was Environmental Systems Inc. Mark Groark, the Controls Project Manager, programmed trend logs and supported M&V efforts. The civil engineer was Sherwood Design Engineers, Inc. The structural engineer was Hohbach-Lewin, Inc. The landscape designer was Bellinger Foster Steinmetz, The acoustics consultant was Charles M Salter Associates, Inc. The general contractor was Herrero Contractors. We gratefully thank and acknowledge all for their effort and support to make this project successful. For more information on this project, contact Peter Turnbull at [email protected].
LEGAL NOTICE
This report was prepared for Pacific Gas and Electric Company for use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents:
(1) makes any written or oral warranty, expressed or implied, including, but not limited to those concerning merchantability or fitness for a particular purpose;
(2) assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or
(3) represents that its use would not infringe any privately owned rights, including, but not limited to, patents, trademarks, or copyrights.
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PG&E’s Emerging Technologies Program ET13PGE1021
ABBREVIATIONS AND ACRONYMS
BMS Building Management System – the controls that manage the various building equipment and provide trend logging
CEC California Energy Commission
CPUC California Public Utilities Commission
CT Current Transducer—Sensor used to measure electrical current
E-Mon D-Mon
Brand name of the electrical power submetering equipment installed in the library
EUI Energy Use Intensity – The amount of annual energy a building uses on a square foot basis, with units
of kBTU/ft2
FC Fan Coil – HVAC system
FDD Fault Detection and Diagnostics – Software tools that take the BMS data and provide automated analysis and fault detection diagnostics and provide actionable data to the building owner/operators
IEPR Integrated Energy Policy Report
IOU California Investor Owned Utilities
M&V Monitoring and Verification – The process of monitoring actual building energy performance and system performance, and verifying that it meets projected building performance goals. The specific monitoring and verification approach is defined in a monitoring and verification plan.
RTU Roof Top Unit
SHS Sacred Heart Schools
ZNE Zero Net Energy – A building that generates as much energy from on-site clean and renewable energy as it uses over the course of the year
ZNE Roadmap
Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in California.” December 2012. http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20Report.pdf
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURES
Figure 1: Monthly energy balance and YTD cumulative net energy ...... 2
Figure 2: 2013 Annual energy end use distribution (kwh/year, and
% of total building electricity use) .................................. 3
Figure 3: Stevens Library ............................................................ 11
Figure 4: Steven’s Library floor plan ............................................. 15
Figure 5: PV layout (from drawing E401B) ..................................... 16
Figure 6: EC1 Evaporative cooler schedule (from drawing MSK006) .. 17
Figure 7: EC1 evaporative cooling system BMS points and
sequence of operations (from drawing M602B) .............. 18
Figure 8: Split system mechanical schedule ................................... 19
Figure 9: Split system heat pump BMS points, and sequence of
operations ................................................................. 20
Figure 10: Exhaust mechanical schedule ....................................... 20
Figure 11: Exhaust fan controls.................................................... 21
Figure 12: Lighting layout (from Drawing E201B) ........................... 22
Figure 13: DHW specifications (from Drawing 501B) ....................... 23
Figure 14: Rainwater line diagram ................................................ 23
Figure 15: Graywater irrigation 1-line diagram ............................... 24
Figure 16: Graywater potable 1-line diagram ................................. 25
Figure 17: Monthly energy balance and year-to-date cumulative
net energy ................................................................ 33
Figure 18: 2013 vs 2014 monthly library electricity use .................. 34
Figure 19: Net energy balance ..................................................... 34
Figure 20: 2013 annual energy end use distribution ....................... 36
Figure 21: Monthly energy end use distribution .............................. 37
Figure 22: Stevens Library benchmarked against ZNE best practice
guidelines ................................................................. 38
Figure 23: EnergyIQ benchmark data for all California central coast
schools ..................................................................... 39
Figure 24: EnergyIQ benchmark data for all California central coast
elementary and middle schools .................................... 40
Figure 25: EnergyIQ benchmark data for all California central coast
office and school buildings ........................................... 41
Figure 26: Heating degree data during the M&V period ................... 44
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PG&E’s Emerging Technologies Program ET13PGE1021
Figure 27: Cooling degree data during the M&V period .................... 45
Figure 28: Average maximum monthly temperature during the
M&V period................................................................ 46
Figure 29: Average mean monthly temperature during the M&V
period ....................................................................... 47
Figure 30: Average minimum monthly temperature during the M&V
period ....................................................................... 47
Figure 31: Average relative humidity during the M&V period ............ 48
Figure 32: Average wind speed during the M&V period .................... 48
Figure 33: Average cloud cover during the M&V period ................... 49
Figure 34: Original PV performance projections for 30o tilt ............... 50
Figure 35: Revised PV performance projection for panels flat on
roof .......................................................................... 50
Figure 36: PV output (kW) during the initial system performance
analysis (November – December 2012) ......................... 51
Figure 37: October 2013 PV data showing gaps in logged data
(representative of 2/13 – 12/13 PV data) ...................... 52
Figure 38: Incident solar radiation versus PV output for November
2012 through January 2013 ......................................... 53
Figure 39: Comparison of PV output design projections to 2013 and
2014 (partial year) generation data .............................. 53
Figure 40: 2013 estimated and 2014 measured PV generation ......... 54
Figure 41: Average monthly cloud cover for March-August .............. 55
Figure 42: 2013 and 2014 measured HVAC electricity consumption .. 55
Figure 43: EC1 operating characteristics ....................................... 56
Figure 44: EC1 temperature details for November 2012 .................. 57
Figure 45: EC1 space CO2 levels for November 2012 ...................... 57
Figure 46: Average hourly HVAC kW for the initial M&V period
(October through December 2012) ............................... 58
Figure 47: Average hourly HVAC kW for a typical heating season
week (11/26 – 12/2 2012) .......................................... 58
Figure 48: Average hourly HVAC kW for a typical day (Friday
11/30/12) ................................................................. 59
Figure 49: Daily HVAC kWh for the initial M&V period and heating
degree days (HDD) for (October through December
2012) ....................................................................... 60
Figure 50: Daily HVAC kWh vs. heating degree days ....................... 60
Figure 51: EC1 HVAC unit function status (October 2013) ............... 61
Figure 52: EC1 HVAC unit CO2 and Temperatures (October 2013) .... 62
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PG&E’s Emerging Technologies Program ET13PGE1021
Figure 53: Daily temperature Ranges (October 2013 ...................... 63
Figure 54: Room 201 tutoring room temperature (November 2012) . 64
Figure 55: Room 204 tech room temperature (November 2012) ...... 64
Figure 56: Room 206 electrical room temperature (November
2012) ....................................................................... 64
Figure 57: Room 207 temperature (November 2012) ...................... 65
Figure 58: Room 211 office temperature (November 2012) ............. 65
Figure 59: Room 201 tutoring room temperatures (10/13 – 11/14) .. 66
Figure 60: Room 204 tech room temperatures (10/13 – 11/14) ....... 66
Figure 61: Room 206 electrical room temperatures (10/13 –
11/14) ...................................................................... 67
Figure 62: Room 207 meeting room temperatures (10/13 – 11/14) .. 67
Figure 63: Room 211 office temperatures (10/13 – 11/14) .............. 67
Figure 64: Monthly lighting energy use for the entire M&V period ..... 69
Figure 65: Measured lighting power density (LPD) for November
2012 ........................................................................ 70
Figure 66: Lighting power density for a typical day (Monday,
11/12) ...................................................................... 71
Figure 67: Monthly lighting energy use for the entire M&V period ..... 72
Figure 68: DHW measured electricity use for October and
November 2012 ......................................................... 72
Figure 69: Monthly rainwater and graywater system electricity use .. 73
Figure 70: 2013 and 2014 comparative monthly rainwater and
graywater system electricity use .................................. 74
Figure 71: Rainwater/graywater system average daily electricity
consumption (kWh, and % of rainwater/graywater
total) ........................................................................ 75
Figure 72: Monthly plug load and ceiling fan electricity use .............. 75
Figure 73: Design energy model end use breakout ......................... 81
Figure 74: As-built energy model end use breakout ........................ 82
Figure 75: Initial calibrated energy model end use breakout for
2013 ........................................................................ 86
Figure 76: Comparison of measured energy vs. initial calibrated
energy model projections ............................................ 88
Figure 77:plut calibrated energy model end use breakout ................ 90
Figure 78: Comparison of 2013 and 2014 measured building
energy vs. final calibrated energy model ....................... 91
Figure 79: E-Mon D-Mon panel labeling ......................................... 95
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PG&E’s Emerging Technologies Program ET13PGE1021
Figure 80: Updated calibration factor for the BMS’ lighting panel
(panel BH-1) submeter ............................................... 97
Figure 81: Comparison of actual (Dent), mis-calibrated (BMS) and
corrected BMS lighting power measurements (11/1/12
through 12/4/12) ....................................................... 98
Figure 82: Comparison of actual (Dent), mis-calibrated (BMS) and
corrected BMS lighting power measurements (11/26/12
through 11/30/12) ..................................................... 98
Figure 83: Comparison of measured total building power vs. BMS
reported building power .............................................. 99
Figure 84: Comparison of measured total building power vs.
corrected BMS power (corrected lighting) .................... 100
Figure 85: Example of SkySpark FDD software interface ............... 105
Figure 86: Building B electrical 1-line drawing .............................. 111
Figure 87: Mechanical equipment electrical connection summary ... 112
Figure 88: Distribution switchboard DP-BH schedule ..................... 112
Figure 89: Panel B-H1 schedule ................................................. 113
Figure 90: Panel LCP B-H1 schedule ........................................... 113
Figure 91: Panel B-L1 schedule (as-built) .................................... 114
Figure 92: Panel B-L2 schedule (as-built) .................................... 115
Figure 93: BMS 1-line diagram ................................................... 116
TABLES
Table 1: BMS trend data points .................................................... 28
Table 2: Data logger and spot measurement installation summary ... 30
Table 3: Data logger configuration details ..................................... 31
Table 4: Monthly building electricity consumption and generation
Data ......................................................................... 35
Table 5: annual and M&V period summaries of building electricity
consumption and generation Data ................................ 36
Table 6: Library equipment and plug loads count ........................... 43
Table 7: Heating degree data during the M&V period ...................... 45
Table 8: Cooling degree data during the M&V period ....................... 46
Table 9: Comparison of 2013 actual energy use and design energy
model ....................................................................... 81
Table 10: Comparison of 2013 actual energy use to design and as-
built energy models .................................................... 83
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Table 11: Comparison of actual Atherton to CACZ04 temperatures
for November 2012 .................................................... 84
Table 12: Initial calibrated energy model projections compared to
November 2012 M&V data ........................................... 85
Table 13: Comparison of 2013 actual energy use to design, as-built
and initial calibrated energy models .............................. 86
Table 14: Comparison of 2013 actual energy use to design, as-built
and initial calibrated energy models .............................. 92
Table 15: Comparison of measured vs. updated energy model total
building energy consumption (kWh) ............................. 92
Table 16: E-Mon D-Mon system CT placement ............................... 94
Table 17: Library equipment and plug loads count ........................ 121
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CONTENTS
EXECUTIVE SUMMARY 1
INTRODUCTION 11
BACKGROUND 12
EMERGING TECHNOLOGY 14
ASSESSMENT OBJECTIVES 26
TECHNOLOGY EVALUATION 26
TECHNICAL APPROACH 26
RESULTS 32
EVALUATIONS 101
RECOMMENDATIONS AND LESSONS LEARNED 101
APPENDIX A: ADDITIONAL ELECTRICAL AND MECHANICAL SYSTEM DETAILS 110
APPENDIX B: OPERATIONAL SURVEY 117
APPENDIX C: SCHOOL SCHEDULE 122
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PG&E’s Emerging Technologies Program ET13PGE1021
EXECUTIVE SUMMARY
PROJECT GOAL
The objective of this project is to evaluate how the Sacred Heart Schools Stevens Library
performed with respect to its zero net energy design goal, and to identify issues, challenges,
problems, and lessons learned to inform and guide the design of future ZNE buildings.
PROJECT DESCRIPTION
Sacred Heart Schools completed construction of its Stevens Library for lower and middle
school students in August 2012. The library is a 6,300 ft2 all-electric building. It has been
designed and constructed to achieve Zero Net Energy (ZNE) performance (based on total
site energy), meet the Living Building Challenge and attain LEED Platinum certification. The
project includes many energy efficiency and high performance features to achieve these
goals, including mixed mode natural ventilation design, high performance glazing, efficient
lighting and controls, high thermal resistance cool roof, variable speed split system
conditioning, direct/ indirect evaporative cooling, ceiling fans, and significant energy sub-
metering. Note that the library was originally designed with a natural gas heated,
direct/indirect evaporatively cooled roof top unit (RTU). This unit was replaced with an
electric heat pump RTU (also with direct/indirect evaporative cooling) due in part to
concerns about meeting the ZNE goals. The original ZNE projections were based on an
optimally tilted PV array, but the final design had the PV system installed horizontally on the
roof which reduced PV output. To compensate for the reduced PV output, the more efficient
(from a site-energy perspective) electric heat pump unit was specified.
The project has received technical assistance from Pacific Gas and Electric Company (PG&E)
to evaluate how well it met its ZNE design goals. PG&E has contracted the Cadmus Group to
provide Measurement and Verification (M&V) services. Initial M&V was provided for October
through December 2012 through the PG&E ZNE Pilot Program. Additional M&V was provided
through the PG&E Emerging Technologies Program to extend the M&V period through
September 2014.
PROJECT FINDINGS/RESULTS
The building is exceeding its ZNE performance goals by a significant margin. For 2013 the
PV system generated 54% more electricity than the building consumed. The trend is similar
for 2014. For the entire monitoring period (12/12 – 9/14), the PV system has generated
73% more electricity than the building has consumed. Figure 1 plots the monthly energy
balance (building electricity use and PV generation) and year-to-date (YTD) cumulative net
energy1. There is significant excess solar energy generation during the summer. This is due
to a combination of reduced summer occupancy and increased solar generation. The
building is very efficient and has an energy use intensity (EUI) of 18.0 ± 0.6 kBTU/
1 Net energy = PV generation – building use; positive means net electricity generation,
negative means net electricity use.
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PG&E’s Emerging Technologies Program ET13PGE1021
ft2/year2. This is significantly lower than the New Building Institute (NBI) “Getting to Zero”
Report3’s definition of “ZNE Capable” buildings4.
FIGURE 1: MONTHLY ENERGY BALANCE AND YTD CUMULATIVE NET ENERGY
Figure 2 summarizes the overall energy end use distribution for 2013 (for which a complete
year’s worth of data is available). Space conditioning consumes just over 60% of the
building’s energy and is the biggest load. Of the space conditioning load, fans account for
32%, heating for 48%, cooling for 15%, and pumps and controls for the remaining 5%.
Lighting and non-regulated loads primarily under occupant control (e.g., plug loads, ceiling
fans) both consume equal amounts at ~15% of the total building load each. The
rainwater/graywater system consumes 8% of the building energy use. These loads include
a variety of pumps, ozone generators and related equipment.
2 Average annual EUI based on the nearly 12/12 – 9/14 data. 3 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs and
Features of Zero Energy Commercial Buildings.” March 2012.
http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf 4 NBI defines ZNE Capable buildings as buildings with EUIs ≤ 35 kBTU/ft2/year. They do not
differentiate by use type.
-5,000
0
5,000
10,000
15,000
20,000
25,000
30,000
-2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
YTD
Cu
mu
lati
ve N
et
Ene
rgy
(kW
h)
Mo
nth
ly E
ne
rgy
(kW
h)
2013 YTD Cumulative Net Energy
2014 YTD Cumulative Net Energy
Building Electricity Consumption
PV Generation
Estimated PV Generation
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 2: 2013 ANNUAL ENERGY END USE DISTRIBUTION (KWH/YEAR, AND % OF TOTAL BUILDING ELECTRICITY USE)
PROJECT RECOMMENDATIONS This M&V project has resulted in a number of recommendations and lessons learned that
may be useful to a variety of stakeholders. The following discussion is organized by topic
with by specific recommendations for key stakeholder groups.
SUB-METERING
Metering and submetering are critical to ZNE buildings. Without proper, accurate and
sufficient metering it is impossible to track building performance and manage the building to
achieve and maintain ZNE status. This is particularly critical at this facility as the building is
part of a larger master-metered campus and does not have its own utility meter.
Stevens Library is equipped with six electric submeters to monitor and manage electricity
use. There were several metering related issues that this M&V project identified which would
likely have otherwise gone unnoticed. First, two of the current transducers (CTs) in
submetering panel did not match the submeters’ requirements and resulted in incorrect
readings. The CT measuring the lighting loads was a 200 Amp CT, but the submeter was
configured to read a 100 Amp CT. This mismatch resulted in inaccurate readings. The
lighting loads are being recorded by the BMS at 4.86 times lower than actual loads. This
error is compounded because the BMS adds all of the submetered loads to get total building
energy use. The incorrect lighting panel reading resulted in total building energy being
reported as ~20% lower than actual energy use. It is possible that the error could have
gone the other way and reported higher than actual energy use. This has the potential to
jeopardize a building’s ability to demonstrate that is has achieved ZNE. This is problematic
to all parties involved, particularly if an important sustainability program performance rating
is at stake (i.e., loss of LEED energy and atmosphere credit 1 points for energy efficiency;
inability to meet the living building challenge). The M&V team flagged the CT/submeter
Space Conditioning, 21,367 , 61%
Lighting, 5,211 , 15%
Water Heating, 80 ,
0.2%
Rainwater/ Graywater
System, 2,750 , 8%
Ceiling Fans and Plug
Loads (Panel B-L2), 5,715 ,
16%
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PG&E’s Emerging Technologies Program ET13PGE1021
mismatch issue on the lighting panel during the initial M&V period and had thought that the
issue had been resolved. During the data review and QC for the final report, unusually low
lighting power densities were observed and investigated, and it was determined that the
lighting panel CT had not been changed. This was flagged and Sacred Heart Schools has
replaced the sensor.
Metering problems have been a common issue for several recent ZNE buildings. Metering is
a specialty field that, depending on the metering equipment involved, can require special
expertise to install and calibrate. It is recommended that all metering and submetering
equipment in ZNE and high performing buildings receive appropriate commissioning and
verification to ensure it is providing accurate data.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Note that historical BMS lighting and total building power data prior to the
12/2014 CT change-out is inaccurate. The calibration multiplier provided in
this report can be used to update historical data if needed. Note that all data
presented in this report has been updated.
One submeter is currently unused. It would be valuable to operations to
connect this and pick up power or current data for EC1 and the duct heaters.
The submeter readings for the other new buildings should be spot checked
with a hand-help multimeter to confirm their accuracy.
RECOMMENDATIONS TO DESIGN TEAMS
Metering and submetering is often left out of commissioning scopes and
metering problems often go unnoticed. The design team should ensure that
the commissioning scope includes commissioning for submeters. This is
particularly critical for ZNE buildings and other high performing buildings
where having accurate data is vital to achieving and maintaining performance
goals. It is not inconceivable to envision a situation where a building would
“fail” to achieve ZNE or properly document performance due to a simple
metering issue and miss out on LEED points or miss out on achieving a rating
(i.e., the Living Building Challenge).
The design team on this project did a great job designing the electrical
circuits to facilitate easy submetering. However, many building electrical
layouts present significant challenges and preclude easy and inexpensive
submetering. Design teams should make sure to include submetering
requirements in the project and ensure that appropriate design team
personnel are aware of these requirements.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Metering and submetering issues are a common theme observed on multiple
projects. There are opportunities to encourage projects to improve the
submetering and metering process and make this data more useable and
useful for building owners to achieve and maintain ZNE or similar energy
performance goals.
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PG&E’s Emerging Technologies Program ET13PGE1021
BMS TREND DATA
This project had excellent BMS trend data to work with. This was invaluable to the M&V
efforts and will be extremely useful for the school to help manage and maintain its ZNE
status. Part of the success was due to the fact that the M&V consultant was able to
coordinate with the controls contractor before the BMS programming was finished, and it
was easy to set up the desired trend logs. Unfortunately, this is not always the case on
projects. More often than not the rich BMS data vital to managing ZNE and deep energy
efficiency is very difficult for building operators, M&V personnel, and others to access, and
therefore it is not used to the extent it could be.
Another issue encountered in the BMS trend data is that the PV power and energy points
were configured to log data on a change in value, rather than a fixed time increment. This
resulted in a massive amount of data (tens of thousands of records per year) that is very
difficult to utilize. Each time increment is different, so it is difficult to overlay this data with
other data (i.e., building performance data). It is possible that the very heavy
communication load placed on the inverter’s communication module to report data on such
a frequent basis may have contributed to the problems it experienced.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Reviewing the BMS trend logs on a monthly basis through the M&V program
has been very useful in identifying issues and spotting problems early and is
critical to maintaining long term ZNE status.
Explore opportunities to automate the routine monthly BMS trend log
downloads and include key performance indicators on the building dashboard.
Examples include plotting monthly energy use against calibrated modeled
energy use. Any significant deviation from monthly expectations could help to
identify and respond to significant issues early.
RECOMMENDATIONS TO DESIGN TEAMS
It would be valuable to develop a coordinated M&V approach that outlines key
BMS data to trend, time increment for trending the data, defining which
circuits need to be submetered, and give thought to how various stakeholders
charged with meeting ZNE performance goals will be able to access the data
in a quick and easy way. Leaving these as ad-hoc decisions that the controls
contractor has to make on the fly is not optimal for leveraging the usefulness
of the BMS data for meeting ZNE goals.
Downloading and processing BMS trend data remains a complex and time
consuming job for building O&M personnel. The design team and controls
contractor should jointly consider opportunities in specifications and control
system selection that would help facilitate ready use of appropriate BMS data
vital to managing and attaining ZNE and related performance goals.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Design teams are becoming increasingly accountable for building performance
and will have an increased stake in how well buildings perform. There are
numerous opportunities for the design team to enhance the effectiveness of
monitoring, controls, and related systems through thoughtful design. There
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PG&E’s Emerging Technologies Program ET13PGE1021
are significant opportunities to further explore and promote these
opportunities from the utility and policy side.
AUTOMATED FAULT DETECTION AND DIAGNOSTICS
The BMS system produces a large amount of very useful data that are being stored in trend
logs. Unfortunately, this data is not always easily accessible to typical building owners and
operators. The data must be manually downloaded to a spreadsheet and processed, which is
a time consuming process. Typically, this data is rarely reviewed and therefore provides
little actionable information to inform building owners/operators on an ongoing basis.
Making better use of this data will be crucial for ensuring ongoing ZNE performance by the
Library and other ZNE buildings.
An emerging set of complementary automated “Fault Detection and Diagnostic” (FDD)
software tools and related building dashboard tools are coming onto the market which will
facilitate use of this detailed BMS data and automate much of the labor-intensive review and
processing. While the building automation system is capable of controlling equipment, data
display, alarming and trending, it is not capable of detailed fault detection and
troubleshooting. Fault detection and diagnostics software is capable of conducting custom
detailed analysis on the data handled by the building automation system and serving it in a
graphical method that is intuitive to the user. The appropriate fault detection system, much
like the automation system, is flexible enough to be modified and updated to accommodate
future changes to systems and sequences of operation. This software package gives the
user the capability to run analytics across the entire range of control points within the
automation system, generate and distribute alarms, display data graphically and make
corrections to setpoints and schedules accordingly.
Ongoing commissioning of the building systems is the primary intent of the automated fault
detection system. While commissioning and re-commissioning of systems is effective for
instantaneous verification of correct system operation, fault detection systems continue to
watch building systems long after start up and initial testing is complete. The combination of
ongoing monitoring and custom analytics provides a platform for continued system
optimization and a real-time view of the buildings energy consumption.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Explore opportunities to expand the Lucid Designs dashboard to include some
of the automated diagnostics and fault detection reporting that will help the
Steven’s library maintain ZNE status and minimize facilities impact for
downloading and processing BMS data. This could include things such as
comparing monthly building EUI to predicted EUI (from calibrated model) and
reporting significant deviations.
RECOMMENDATIONS TO DESIGN TEAMS
Design teams will want to watch this nascent field carefully. There are some
very exciting developments that may be useful to incorporate into high
performing building projects to help ensure challenging performance targets
are met.
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PG&E’s Emerging Technologies Program ET13PGE1021
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
AFDD is an exciting development in the building industry and has significant
potential to help improve long term building performance. Emerging
technology studies and similar efforts to document performance impacts and
best practices with AFDD systems would be very valuable.
ENERGY MODELING
Building energy modeling is used and applied in different ways depending on what one is
trying to accomplish. Each use has a unique set of practitioners, goals, and established
approaches to building energy modeling. Traditionally there has been limited cross-over
between each of these different building energy modeling domains, their practitioners, their
targeted building lifecycle phase. Each domain requires a niche expertise, and involves
different stakeholders, customers, team-members and building phases. The rise of ZNE
buildings creates very interesting cross-over opportunities between the different energy
modeling domains. The building energy model now becomes a critical tool for (1) optimizing
building energy performance in the early design phase, (2) documenting compliance, (3)
accurately projecting actual building performance during operations to size the onsite
renewable system and meet ZNE performance requirements, (4) verifying ZNE performance
and “correcting” for atypical weather, occupancy, and other operational issues as is done for
guaranteed energy savings projects, and (5) facilitating building operations personnel to
maintain ZNE operations. There is need for increased education about the different ways
energy modeling can and needs to be applied to ZNE buildings.
RECOMMENDATIONS TO DESIGN TEAMS
ZNE building projects will require a higher level of modeling accuracy and
applying energy modeling for different purposes. Increased design team use
of building energy modeling is required and team energy modeling expertise
must generally increase as well..
Design teams need to understand that there are different uses for energy
modeling throughout the project life cycle, and effectively use energy
modeling at each phased.
Design teams should be very careful to understand the difference between a
“compliance energy model” and an energy model used to estimate actual
building operational energy for ZNE renewable energy system sizing.
A final “as built” energy model should be developed and used to confirm ZNE
estimates.
Appropriate safety margins should be built into ZNE renewable energy system
sizing to account for weather, occupancy, schedule, space use intensity, and
plug load variance that are likely to occur.
Standard assumptions for plug loads, DHW, and other loads which do not
typically matter as much in compliance modeling (since they are assumed
equal in both the design and base-case and do not typically appreciably
impact compliance energy savings projections) should be very carefully
evaluated. These loads are often significantly different from actual building
loads and poor estimates can jeopardize a building’s ability to achieve ZNE.
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RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
There is a significant need to develop modeling guidelines and best practices
for practitioners to transition from “compliance” modeling to “performance”
modeling. The National Renewable Energy Laboratory’s Building America
Program, for example, developed a set of energy modeling guidelines and
data for residential energy modeling that were very useful to practitioners.
Similar approaches could be taken for commercial building modeling. Existing
databases (i.e., CUESS) could be leverage to help develop guidelines for
water heating energy use and other relevant loads. Water heating energy use
was significantly over-estimated for this project.
PLUG LOADS
Plug loads comprise an increasingly large percentage of the total building energy use as
HVAC and other regulated loads are reduced. It is not uncommon for plug loads to represent
25% - 50% of a ZNE building’s total load. The Library’s plug loads are relatively small
compared to typical buildings, accounting for 16% of the total building energy use in 2013.
The original energy model over-estimated plug load energy by ~50%.
Note that a significant upward trend in plug load energy began in July 2014 and continued
through the end of the M&V period in September 2014. Refer to Figure 72. The reasons for
this increased consumption is unclear, but could include significant new equipment
additions, equipment not being turned off, some type of equipment malfunction, the use of
a portable electric heater(s), or similar issues.. The reasons for this should be investigated
and corrected if needed by Library staff.
The key lessons learned are that plug loads represent a large portion of building energy use
and focusing on opportunities to reduce these loads will be important for future ZNE
buildings. Furthermore, it is important to refine energy modeling efforts to estimate these
as accurately as possible. There may be opportunities for PG&E and other organizations to
support projects to improve the modeling of plug loads. As a starting point, it would be
useful to document how well plug loads are currently being modeled (e.g., a study
comparing LEED building energy modeled data vs. actual plug loads).
ELECTRICAL ROOM TEMPERATURE SETPOINT The electrical room, which contains a number of servers, is maintained between 66 oF and
69.5oF. Typically, servers have higher permissible operating temperature ranges. The
servers’ temperature specifications should be reviewed, and the temperature setpoint
relaxed accordingly to reduce air-conditioning energy use.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Investigate equipment temperature operating limits and increase temperature
setpoints if possible.
RECOMMENDATIONS TO DESIGN TEAMS
Explore opportunities to specify equipment with robust temperature operating
ranges, and make sure this information is communicated to building owners,
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control contractors who set initial temperature setpoints, commissioning
agents, and other building operations stakeholders.
LIGHTING
The Library uses linear fluorescent lighting with daylighting and occupancy controls to
reduce peak lighting power density (LPD). LED lighting is becoming increasingly cost
effective and can be merged with advanced control strategies, individually controlled
luminaires, and advanced control strategies to minimize lighting energy.
RECOMMENDATIONS TO DESIGN TEAMS
Design teams should specify LED lighting and advanced control strategies that
are well matched to LED lighting technology.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Programs such as the Emerging Technologies Program provide invaluable
information to the design community on what works and what does not,
costs, and other barriers and opportunities related to the installation and
performance of emerging products and technologies. There is an ongoing
need for this information regarding emerging lighting technologies and
practices (i.e., “occupant specific lighting”).
ONSITE WATER RECYCLING AND RAINWATER CAPTURE
Nearly 10% of the library’s energy is spent on the rainwater and graywater systems.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
No electricity use was measured on the graywater system’s UV system. The
facility should check to ensure the UV system is operating correctly.
RECOMMENDATIONS TO DESIGN TEAMS
Consider the energy impacts of onsite water systems. Make sure to include
these loads in the relevant ZNE and PV array sizing calculations if they are to
be included in the “ZNE” load. Specify efficient and appropriate equipment
and systems and ensure they are performing as expected.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Water/energy/carbon nexus issues are increasingly becoming a part of
building-level design. This is an area where designers could use guidance on
best practices.
VENTILATION AND AIR QUALITY
The operational survey indicated there is a tendency for the building to feel hot or stuffy
during high occupancy periods or hot weather. Occupants use windows (natural ventilation)
for supplemental ventilation. There are a number of potential reasons for this condition,
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which are beyond the scope of this M&V effort to fully investigate. The M&V efforts did note
that there is limited compressor use for the main reading room, and it is possible that this
system is not providing adequate humidity control. Room humidity is not one of the trend
logs available on the BMS. It is possible that fine-tuning of the controls could address this
(e.g., CO2 level setpoints, evaporative cooling staging, supply air humidity control
setpoints), or it could be that the building is operated per its design intent, and that some
occupant education on the building design and use of the natural ventilation and ceiling fan
features to provide additional airflow could address the issues.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Investigate humidity levels if building occupants continue to note hot and
stuffy conditions. Fine-tuning of minimum ventilation rates and demand
controlled ventilation controls sequences may be required.
CEILING FANS
The operational survey indicates that the ceiling fans are noisy and are not used often.
RECOMMENDATIONS TO DESIGN TEAMS
Issues such as noise have a demonstrated impact on occupants use of the
ceiling fans and other equipment. Designers should carefully consider noise
and related issues which may impact user acceptance and use of equipment
and strategies.
In summary, Stevens Library is performing very well and meeting its ZNE goals. The most
important recommendations to the facility is to make sure that the incorrectly sized CT’s on
the BMS electricity submeters are replaced with the correct sized CTs, or have the updated
calibration factor programmed into the BMS. We also strongly recommend that Sacred Heart
Schools continue some type of M&V for not just the library, but all of its buildings to ensure
efficient and cost effective operations. For the design team, the most significant
recommendations for future projects would be to continue refining the energy modeling
process. Plug load, rainwater/graywater system, and DHW heating energy projections were
significantly off. This does not significantly impact this building, but these mis-estimates
could significantly impact ZNE attainment for another building type. Also, the design team
did an excellent job designing the electrical system to be well metered, and included a front
end dashboard. At this point however, it will most likely take strong design team leadership
to ensure that the data logging capabilities are translated into useful and actionable data on
the dashboard that will help building managers maintain long-term ZNE performance. From
the utility perspective, there are significant opportunities more effectively incorporate
submetering into buildings and work with controls contractors, building dashboard
developers, and building operators to make this data useful and actionable. Automated fault
detection and diagnostics will play an important role in managing the massive amounts of
data that submeters and BMS systems generate.
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INTRODUCTION This report presents the results of a detailed 24 month M&V of the Sacred Heart Schools
Stevens Library. The Stevens Library is for lower and middle school students, and was
designed to be zero net energy (ZNE), meet the Living Building Challenge, and achieved a
LEED Platinum Rating.
FIGURE 3: STEVENS LIBRARY5
The library consists of a main reading room with library stacks, and six smaller rooms
(librarian’s office, a classroom/meeting room, a technology/media storage room, technology
coordinator’s office, tutoring room, and tutoring office). The library also contains two single
occupant staff bathrooms, a janitor closet, a storage closet, an electrical room, and a
mechanical room for the rainwater and graywater system equipment.
The library is typically open between 7am-4:30pm weekdays during the school year. Peak
occupancy is typically around 30 students, although up to 50 students may be present
during standardized testing. The librarian’s estimate of total daily occupancy is between 75
to 100 students per day. Some minor weekend occupancy occurs for meetings and other
5 Photo used by permission of the Architect Pauline Souza from WRNS Studios.
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activities. The school year typically runs from August 28 through December 21 (fall
semester) and January 8 through June 7 (spring semester). Refer to Appendix C for the
detailed academic calendar during the M&V period. The library receives minimal student use
during the summer, but is open and used by staff.
The monitoring and verification (M&V) period was from October 2012 through September
2014. The start of the M&V period coincided with building completion and initial occupancy.
Complete and consistent performance data for the first two months of the M&V period
(October and November 2012) are not available due to BMS and trend logs programming,
metering installation, and various startup-related issues. Full energy performance data is
available for December 2012 through September 2014. The library was occupied during this
period per routine school schedules and activities. Analysis includes evaluation of ZNE
performance, energy end use break-outs, comparison of the design building energy model
to actual data, and building energy model recalibration. Issues and lessons learned are
documented.
BACKGROUND The concept of ZNE buildings began to gain popularity in the 2000’s. Although still few in
number, ZNE buildings have rapidly caught both public and policy-maker attention.
Currently, ZNE buildings are being built on a voluntary basis. However, green building
certification programs are generally moving towards performance based building energy
requirements (i.e., ZNE), with a few programs including the Living Building Challenge
requiring ZNE. Moreover, ZNE requirements are rapidly becoming embedded in a wide
range of energy policy goals.
At the federal level, Executive Order 13514 requires all new Federal buildings that enter the
planning process in 2020 and thereafter to be designed to achieve ZNE standards by 2030.
Some federal agencies have established more ambitious ZNE goals. The Department of
Defense has goals to reduce building energy intensity 37.5% by 2020 and to have 20% of
all facility electricity supplied by renewable energy6. The Navy has a goal of producing at
least 50% of shore-based energy requirements from alternative sources, and for 50% of
Navy installations to be ZNE7. The Department of State currently requires new embassy and
consulates to go through a rigorous comprehensive sustainability planning process to
identify strategies, costs, and benefits for making new facilities ZNE, zero net water and
carbon neutral8.
ZNE building goals are deeply embedded in California Energy Policy. The 2008 California
Long Term Energy Efficiency Strategic Plan (Strategic Plan) included “big bold” goals that all
new residential construction in California be zero net energy (ZNE) by 2020, and all new
commercial construction be ZNE by 2030. The California Public Utilities Commission (CPUC)
has been working with stakeholders to help achieve these ZNE goals through various
6 Department of Defense. “Strategic Sustainability Performance Plan, FY 2011.”
http://www.denix.osd.mil/sustainability/upload/DoD-SSPP-FY11-FINAL_Oct11.pdf 7 Department of the Navy. “Energy Program for Energy and Independence”
http://greenfleet.dodlive.mil/files/2010/04/Naval_Energy_Strategic_Roadmap_100710.pdf 8 Department of State. “2011 U.S. Department of State Agency Sustainability Plan: FY 2010
– FY 2020”. http://www.state.gov/m/pri/rls/plans/176092.htm
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regulatory and voluntary processes. The California Energy Commission (CEC) has adopted
the ZNE goals as part of their long term planning through the 2007 Integrated Energy Policy
Report (IEPR)9. The California Investor Owned Utilities (IOUs) are pursuing the ZNE goals
outlined in the Strategic Plan and the IEPR. This includes supporting ZNE demonstration
projects, providing energy efficiency incentives and conducting studies to assess the ZNE
goals. PG&E’s ZNE Pilot Program was a part of these efforts.
The PG&E ZNE Pilot Program launched in 2010 and was active through 2012. It focused on
achieving maximal energy efficiency and load reduction by leveraging advanced design,
construction and building operations before the addition of on-site renewable energy
generation, such as solar PV. The ZNE Pilot Program promoted California’s long term energy
goals through a portfolio of research, development, and demonstration (RD&D) projects
around ZNE buildings together with complementary education, outreach and information
activities. After 2012, PG&E has continued its ZNE activities through other programs,
including the Emerging Technologies Program, which supported this project.
There are a number of challenges associated with designing ZNE buildings. ZNE is inherently
performance based—a building is ZNE based on its actual energy use over the course of a
year. However, as part of the design process, buildings are often designed to ZNE goals.
Similar to the current process of documenting building energy code compliance (i.e., Title
24 Building Energy Efficiency Standards) and LEED energy performance, where building
energy consumption is estimated through building energy simulation and compared to a
hypothetical code compliant building’s estimated energy use using a common set of
schedules and assumptions, designers must use a set of assumptions to estimate how well
the building will likely be able to achieve ZNE, once it is constructed and occupied. However,
achieving ZNE is dependent upon many factors outside of the design team’s control, such as
occupant behavior, operations and maintenance (O&M) practices, facility use intensity,
scheduling, and setpoints.
ZNE buildings also typically include deep energy conservation measures. Some designers
rely on state-of-the art systems and controls, whereas others utilize simpler passive and
low-energy design strategies. Regardless of approach, achieving the energy performance
necessary to achieve ZNE is challenging and requires all systems to work as intended.
Occupant behavior and their interaction with the building is a critical aspect to achieving
ultimate ZNE performance in ZNE buildings. As regulated loads (e.g., HVAC, lighting) are
made more efficient through energy standards, appliance standards, and other policy levers,
they are becoming smaller and smaller portions of total building energy use. Unregulated,
occupant driven loads are becoming much more significant and must be addressed to
achieve ZNE. Improved understanding of how occupants drive building energy use, and how
design can help minimize these loads, is increasingly important.
Another challenge associated with creating ZNE buildings is that there are so few actual ZNE
buildings in operation. As part of its ongoing ZNE efforts, PG&E has been performing
detailed performance M&V of new ZNE buildings. This helps fill a critical data gap. For more
information on these and related ZNE issues, refer to “The Road to ZNE: Mapping Pathways
to ZNE Buildings in California10” (ZNE Roadmap) — a project funded and supported by
PG&E’s ZNE Pilot Program. From the final study report:
9 California Energy Commission. http://www.energy.ca.gov/2007_energypolicy/ 10 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in
California.” December 2012.
http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20
Report.pdf
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“The essential challenge for achieving the ZNE goals is to learn from the experiences
of the early adopters and apply those lessons learned to motivate, and if needed,
mandate changes. We say this because achieving the ZNE goals will require the type
of rapid changes in current industry practices for design, construction and operation
that cannot be achieved through incentives alone. Relative to many other industries
the construction industry as a whole is not an industry that innovates at a fast pace
on a large scale. Our interviews with stakeholders demonstrate that the majority of
the construction industry will only adopt any ZNE metric as a construction practice
once two things are clear: (1) there is a sustained market demand for that metric of
ZNE; and (2) the resulting buildings are deemed cost-effective and ‘feasible’ by
market actors and building owners/operators.”
This project helps learn from the early adopters by providing well-documented ZNE building
performance data and lessons learned.
EMERGING TECHNOLOGY Sacred Heart Schools completed construction of its Lower and Middle School Library in
August 2012. The library is 6,300 ft2. It has been designed and constructed to achieve Zero
Net Energy (based on site energy), meet the Living Building Challenge and attain LEED
Platinum certification. The project includes many energy efficiency and high performance
features to achieve these goals, including mixed mode natural ventilation design, high
performance glazing, efficient lighting and controls, high thermal resistance cool roof,
variable speed split system conditioning, direct/ indirect evaporative cooling, ceiling fans,
and significant energy sub-metering. The library was originally designed with a natural gas
heated, direct/indirect evaporatively cooled roof top unit (RTU). The design team replaced
this unit in the design phase with an electric heat pump RTU (also with direct/indirect
evaporative cooling) due in part to concerns about meeting the ZNE goals. The original ZNE
projections were based on an optimally tilted PV array, but the final design had the PV
system installed horizontally on the roof which reduced PV output (refer to the PV section
below for more detail). To compensate for the reduced PV output, the more efficient (from a
site-energy perspective) electric heat pump unit was specified.
Due in part to design team concerns about meeting the ZNE goals, the gas heated RTU was
replaced with an electric heat pump RTU (also with direct/indirect evaporative cooling). Key
energy efficient and renewable energy system details are provided below.
The library consists of a main reading room with library stacks, and six smaller rooms
(librarian’s office, a classroom/meeting room, a technology/media storage room, technology
coordinator’s office, tutoring room, and tutoring office). The library also contains two single
occupant staff bathrooms, a janitor closet, a storage closet, an electrical room, and a
mechanical room for the rainwater and graywater system equipment. The floor plan is
shown in Figure 4.
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FIGURE 4: STEVEN’S LIBRARY FLOOR PLAN
The library is typically open between 7am-4:30pm weekdays during the school year. Peak
occupancy is typically around 30 students, although up to 50 students may be present
during standardized testing. The librarian’s estimate of total daily occupancy is between 75
to 100 students per day. Some minor weekend occupancy occurs for meetings. The school
year typically runs from August 28 through December 21 (fall semester) and January 8
through June 7 (spring semester). Refer to Appendix C for the detailed academic calendar
during the M&V period. The library receives minimal student use during the summer, but is
open and used by staff.
The library is part of a larger campus expansion project that included a number of other
buildings. The library does not have its own utility meter, but is served by a campus master
meter.
The project has received technical assistance from Pacific Gas and Electric Company (PG&E)
to evaluate how well it met its ZNE design goals. PG&E has contracted the Cadmus Group to
provide Measurement and Verification (M&V) services. Initial M&V was provided for October
through December 2012 through the PG&E ZNE Pilot Program. Additional M&V was been
provided through the PG&E Emerging Technologies Program to extend the M&V period
through September 2014.
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PV SYSTEM The library has a 40 kWDC rated, grid-connected Photovoltaic (PV) array. The analysis
performed by the design team for the LEED submittal assumed a 0.770 de-rate factor
(accounting for inverter losses, collector soiling, and other losses) and a design AC power
output of 30.8 kWAC. Figure 3 and Figure 5 illustrate the rooftop PV arrangement. The
collectors take up most of the available roof area, once required fire and mechanical access
is accounted for. Note that the LEED analysis in PV Watts assumes the PV panels are tilted
to the South at a 30o angle. During design, the PV system was changed from a tilted
installation to being installed flat on the roof. Installing the collectors flat reduces their
power output. Refer to the Results Section, “Detailed System Performance Analysis” for
detailed discussion and analysis of power output and impacts of the changed PV array
installation angle.
FIGURE 5: PV LAYOUT (FROM DRAWING E401B)
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INDIRECT EVAPORATIVE COOLER/HYBRID HEAT PUMP RTU
(EC1) The main library reading area is served by a single Speakman direct/indirect evaporative
cooler/heat pump roof top unit (RTU) (unit EC1). Note this replaces two earlier specified
Coolerado units with natural gas duct heaters. Figure 6 summarize the RTU technical
specifications. The unit has 5 tons of cooling capacity with an energy efficiency ratio (EER)
of 22. It has a 1.5 HP fan with an efficient electronically commutated motor (ECM)11. The
fan has a 2100 CFM maximum airflow capacity and can be modulated down to 450 CFM via
the ECM. The system uses a demand controlled ventilation (DCV) strategy that monitors
space CO2 levels and automatically adjusts airflow to provide enough ventilation air for
varying occupant loads. Heating is provided by a heat pump with a 46 MBH capacity and
COP of 3.9. There is 5 kW of supplemental electric resistance heating.
FIGURE 6: EC1 EVAPORATIVE COOLER SCHEDULE (FROM DRAWING MSK006)
Figure 7 shows the sequence of operations and control points for EC1. The key energy
efficiency control strategies incorporated in the sequence of operations include night
temperature setback, economizer, night purge, and demand controlled ventilation.
11 An ECM is a high efficiency brushless DC motor with permanent magnets and a built in
inverter. It is more energy efficient than traditional AC motor.
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FIGURE 7: EC1 EVAPORATIVE COOLING SYSTEM BMS POINTS AND SEQUENCE OF OPERATIONS (FROM DRAWING M602B)
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PACKAGED HEAT PUMP UNITS The study rooms and classrooms are served by high efficiency variable speed heat pump
units. Specifications for these units are summarized in Figure 8. Figure 9 shows the controls
diagram, which indicates BMS control points and the sequence of operations. The units have
a Seasonal Energy Efficiency Ratio (SEER)12 of 15 to 16.5, and a Heating Seasonal
Performance Factor (HSPF)13 of 8.6 to 9. Each unit is controlled by its own internal controls
and a zonal thermostat. The BMS only has enable/disable control (i.e., scheduling) over
these units, along with space temperature setpoint control.
Split Heat Pump Indoor Fan Coil Schedule
Outdoor Condensing Unit Schedule
FIGURE 8: SPLIT SYSTEM MECHANICAL SCHEDULE
12 The SEER is the ratio of the cooling output during a typical cooling-season (in BTUs)
divided by the total electric energy input during the same period (in Watt-hours). The higher
the unit's SEER rating the more energy efficient it is. 13 The HSPF is the ratio of BTU heat output over the heating season to watt-hours of
electricity used, and it has units of BTU/watt-hr. The higher the HSPF rating of a unit, the
more energy efficient it is.
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FIGURE 9: SPLIT SYSTEM HEAT PUMP BMS POINTS, AND SEQUENCE OF OPERATIONS
EXHAUST FANS The exhaust fan details are summarized in Figure 10. The exhaust fan has a capacity of 350
CFM and a 0.167 HP motor. The exhaust fan consumes minimal energy; energy
consumption for the exhaust fan was not directly measured due to space constraints in the
electrical panel, but was included in total panel logged data. Figure 11shows the exhaust fan
controls drawing. The exhaust fan is controlled by the BMS and scheduled to run when the
main HVAC unit (EC1) is on.
FIGURE 10: EXHAUST MECHANICAL SCHEDULE
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FIGURE 11: EXHAUST FAN CONTROLS
LIGHTING Figure 12 illustrates the lighting layout. Lighting in the main reading area is through
direct/indirect linear fluorescent fixtures with F54T5HO lamps (the long strips in Figure 12,
fixture type F2 and F3) and has daylighting control. The daylighting control system uses
multi-level switching via a Wattstopper LCO-203 Daylighting Control Module. The front part
of the reading room has 16 recessed CFL fixtures (fixture type C2 in Figure 12), with two 32
W triple tube CFL lamps and a total fixture power of 69 W. Lighting in the tutoring and
other rooms is by linear fluorescent fixtures with F32T8 lamps and daylighting and
occupancy sensor controls.
Lighting is wired into its own subpanel, lighting control panel (LCP) B-H1. There is an
electric submeter on this panel that tracks lighting power via the BMS. Note that the current
transducer (CT) installed on the submeter is the wrong size and reads incorrect data (the
submeter is programmed for a specific size CT and its corresponding calibration factor). This
also impacts the total energy use (kWh) reported on the BMS system, as the BMS total
building energy use is the sum of the individual submeter data. Cadmus used temporary
data loggers to obtain actual lighting power data and develop a calibration curve to correct
the reading during the initial M&V period (October – December 2012). Refer to the
“Results” section, “Lighting” and “Data Validation and Quality Control” sections for a
detailed discussion and data. The facility was notified of this issue in the November 2012
monthly report. It was Cadmus’ understanding that the lighting CT was to be replaced with
the appropriate CT. However, upon quality control analysis for the final report (November
2014), it was found that CT for the lighting power submeter had not been corrected. All
lighting power and total building power data presented in this report is corrected using the
updated calibration factor.
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FIGURE 12: LIGHTING LAYOUT (FROM DRAWING E201B)
DHW The building is equipped with three tankless electric water heaters, with technical specs
summarized in Figure 13. EWH-1 has an 8 kW power rating and serves the bathrooms, and
EWH-2A and EWH-2B have 8.32 kW power ratings and serve the janitor closet.
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FIGURE 13: DHW SPECIFICATIONS (FROM DRAWING 501B)
RAINWATER AND GRAYWATER SYSTEMS The school is equipped with both rainwater and graywater recovery systems. The rainwater
system details are shown below in Figure 14. Gutters direct rainwater from the roof into a
rainwater storage tank. Water is pumped from the tank, through a self-flushing filter,
through a flow meter and out to irrigate the “eco orchard” via drip irrigation. The rainwater
irrigation system is controlled by the irrigation controller, in conjunction with a water level
sensor located in the rainwater collection tank and a pump controller. The system can also
be controlled via the BMS. Excess rainwater overflows to the storm drain system.
FIGURE 14: RAINWATER LINE DIAGRAM
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The graywater recovery system consists of both a graywater irrigation system, and a
potable graywater reuse system. The graywater irrigation system is shown in Figure 15, and
the potable graywater system is shown in Figure 16. Incoming graywater from site buildings
(including other nearby buildings constructed at the same time as the library as part of a
larger campus construction project) is collected in a 5,000 gallon graywater recovery tank.
Well water supplements the graywater when needed based on tank level. Overflow goes to
the sanitary sewer. An ozone generator supplies ozone to the tank for treatment.
For the graywater irrigation system, water is pumped from the graywater tank, through a
self-flushing filter, through a flow meter and to the landscape via subsurface irrigation. The
system is controlled by the irrigation controller and BMS.
FIGURE 15: GRAYWATER IRRIGATION 1-LINE DIAGRAM
For the potable graywater reuse system, graywater is pumped from the graywater tank,
through a sand filter, 5 micron, 1 micron filter, and then a UV filter. The system is kept
pressurized, and supplies the library’s toilets. Domestic water supplements the system if
needed.
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FIGURE 16: GRAYWATER POTABLE 1-LINE DIAGRAM
An electric submeter is installed on the feed to the rainwater and graywater systems and is
monitored by the BMS system.
BUILDING MANAGEMENT SYSTEM (BMS)
The library is controlled by a Delta Controls BMS system, with a front end accessible online.
Control points and sequences for key equipment are listed above for each system. Cadmus
worked with the controls contractor to set up trend logs that would be valuable to M&V and
provide useful data to facility personnel tasked with long term operations and maintenance
of the building’s ZNE performance.
LUCID DESIGNS BUILDING DASHBOARD The building also has a Lucid Designs building dashboard. The dashboard uses a stand-along
web-server. This web server pulls trend data from the BMS system and then processes and
stores the data internally to serve the dashboard. Cadmus originally anticipated it would be
utilize the data from the dashboard’s webserver. However, the dashboard was not
completed in time for M&V use. The controls contractor was able to provide the M&V team
with access to the underlying BMS system and programmed the needed trend logs for M&V
use. The building dashboard is currently operational, but the M&V team does not have
access.
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ASSESSMENT OBJECTIVES The objectives of this project are to:
1. Document whole building and end-use energy consumption for a new ZNE building,
the Sacred Heart Schools Stevens Library;
2. Conduct an operational survey of building occupants and staff to obtain space usage
data to correlate with M&V data, and to identify any related issues that affect
performance or comfort (e.g., temperature control problems);
3. Recalibrate the original design building energy model to actual conditions;
4. Compare modeled vs. actual building energy performance; and,
5. Identify issues, challenges, problems, and opportunities to inform and guide design
of future ZNE buildings.
TECHNOLOGY EVALUATION Cadmus monitored whole building and end use energy consumption and onsite PV
generation for the 6,300 ft2 Sacred Heart Schools Lower and Middle School Library,
designed to be ZNE, over nearly two years of operation. The library is all-electric.
Key end uses monitored include lighting, water heating, space conditioning, plug loads and
miscellaneous, and a grey water and rainwater catchment system. Furthermore, additional
trend logs were set up on the BMS, and additional temporary loggers were installed on
specific pieces of equipment to aid in diagnostics if needed.
M&V was conducted in two discrete phases and provided performance data for a nearly two
year period from December 2012 through September 2014.
The monitored data was used to determine whether the library achieved its ZNE design
goals. The design model was recalibrated to represent existing conditions. The recalibrated
model was used to help aid assessment of how accurate the original energy model was, and
to help assess how well the building was doing in meeting its ZNE goals over the course of
the M&V period. An occupant survey was also conducted to identify user satisfaction with
the building and identify issues that might affect building performance or be indicators of
equipment problems.
TECHNICAL APPROACH
FIELD TESTING OF TECHNOLOGY The project began with the creation of a detailed M&V plan developed in close coordination
with Sacred Heart Schools, PG&E, the architect, the MEP engineer (who also performed the
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PG&E’s Emerging Technologies Program ET13PGE1021
LEED energy modeling), the controls contractor, the commissioning agent, and other key
members of the design and construction team. This M&V Plan is provided in Appendix C.
Once the M&V plan was finalized, Cadmus worked with the controls contractor to set up
trend logs for key points for the library, including HVAC equipment, ceiling fans, lighting,
the E‐Mon D‐Mon submeters and the PV system.
Temporary data loggers were also installed to obtain additional detailed electricity
performance data on key circuits and equipment (refer to Table 2 under “Instrumentation”
below for details) to aid in diagnostics and to confirm BMS calibrations.
During data logger installation, spot electricity measurements were taken on accessible
pieces of equipment. The BMS submeter installation was also reviewed (e.g., CT sizes and
placement, submeter CT setpoints).
Initial M&V was conducted from October through December 2012. An additional year of M&V
was later authorized for September 2013 through September 2014. The temporary data
loggers were left in place during the interim period of October 2013 through August 2013
with the hopes that this data would be available. Unfortunately some of the loggers’
batteries died, and the loggers ran out of memory, so this data is not as complete as
desired. The Dent data loggers used for the initial M&V period required monthly physical
downloading. To facilitate data access for QC purposes and minimize the need to access
customer electric panels (mitigating risk), Cadmus replaced the original Dent data loggers
with Hobo data loggers equipped with cellular communications. Note that due to physical
space constraints inside the electrical cabinet a new temporary data logger could not be
installed on the feed to the lighting subpanel (panel B-H1). This was not deemed to be an
issue as this panel was already being logged by the BMS, and Cadmus was not yet aware
that the mis-sized CT on this panel identified in the initial M&V period had not been
replaced.
The BMS trend logs were able to provide a continuous record of performance. This M&V
report contains performance data for a nearly two year period from December 2012 through
September 2014. Note that October and November 2012 data is available from the initial
M&V period, but these initial occupancy months were atypical and not included in the final
reported energy consumption data as the building was still undergoing final commissioning,
the BMS programming was being fine-tuned, and related initial occupancy issues were being
addressed.
During the M&V period, monthly reports were submitted to and reviewed with PG&E. The
monthly reports tracked performance and identified issues that needed to be addressed.
An occupancy survey was administered to the library’s staff to observe and document use
patterns, system performance, and related data. The purpose of this survey is to obtain
space usage data to correlate with M&V data, identify any related issues that affect
performance or comfort (e.g., temperature control problems), and to assist in the building
energy model calibration.
The design team’s building energy simulation model developed for the LEED submittal was
reviewed and calibrated using meter and BAS data along with operational survey results for
the initial M&V period of October through December 2012. The energy model was
recalibrated at the end of September 2014 using the additional M&V data.
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PG&E’s Emerging Technologies Program ET13PGE1021
INSTRUMENTATION PLAN The M&V activities were designed to leverage the school’s existing metering and the Delta
Controls BMS to the greatest extent possible. The BMS provides rich performance and
operational data necessary to track and diagnose system performance and keep the building
on track to maintain ZNE for years to come. It is hoped that this project will serve as a
template to assist the school’s facilities personnel to continue tracking long term
performance. BMS trend data was downloaded monthly and on an as-needed basis (to aid in
diagnostics) via remote read-only log-in. Table 1 summarizes the primary and supplemental
BMS trend data used in the M&V project. The primary points are the electricity submeter
data that was relied upon for energy tracking and which were tracked on a monthly basis.
The supplemental points are additional points that were reviewed as needed to interpret
performance data, confirm correct operation, and diagnose issues.
TABLE 1: BMS TREND DATA POINTS
TREND POINT NAME UNIT PRIMARY/
SECONDARY NOTES
Total Building Electricity Submeter kWh Primary
E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.
Electric Water Heating Submeter kWh Primary
E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.
Lighting Panel Submeter kWh Primary E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.
Water Heating Submeter kWh Primary E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.
Plug And Misc. Loads Submeter kWh Primary
E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs
PV Output kWh Primary
Provided to BMS by Digital communication
card on inverter. “Change of value” recording interval.
PV Power kW Primary
Provided to BMS by Digital communication
card on inverter. “Change of value” recording interval.
Room 201 Tutoring Room Temp oF Secondary
Room 204 Tech Room Temp oF Secondary
Electrical Room Temp oF Secondary
Room 207 Meeting Room Temp oF Secondary
Room 211 Office Room Temp oF Secondary
Supply Air Temp East oF Secondary EC1 Multi-trend
Supply Air Temp West oF Secondary EC1 Multi-trend
West Room Temp oF Secondary EC1 Multi-trend
Incoming Air Temp oF Secondary EC1 Multi-trend
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PG&E’s Emerging Technologies Program ET13PGE1021
East Room Temp oF Secondary EC1 Multi-trend
East CO2 Sensor CO2 PPM Secondary EC1 Multi-trend
West CO2 Sensor CO2 PPM Secondary EC1 Multi-trend
EC-1 Compressor Status On/Off Secondary Main RTU Unit; EC2 Multi-trend
EC-1 Fan Status On/Off Secondary Main RTU Unit; EC2 Multi-trend
EC-1 Direct Evap. Cooling Pump Status On/Off Secondary Main RTU Unit; EC2 Multi-trend
EC-1 Indirect Evap Cooling Pump Status On/Off Secondary Main RTU Unit; EC2 Multi-trend
Total Water Usage Gal Secondary
Irrigation Graywater Usage Gal Secondary
Domestic Water Usage Toilets Gal Secondary
Well water Makeup Usage Gal Secondary
Bldg. Graywater Usage Gal Secondary
Rainwater Usage Gal Secondary
In addition to the BMS, an array of portable Dent Elite-pro power meters/data loggers was
installed in the electrical panels to measure individual equipment and loads. These were
installed for two primary purposes: (1) during M&V plan development it was not clear how
much data would be available on the BMS and its utility for M&V, and (2) the additional
temporary meters provide a means to cross check BMS electric submeter data, and provide
additional data to diagnose and interpret building operational issues if needed.
Key Dent ElitePro Power Meter/Data Logger specifications are summarized below.
Measurement Type: True RMS using high - speed digital signal processing (DSP)
Waveform Sampling: 12 kHz
Channel Sampling Rate (internal sampling): 200 samples /cycle at 60Hz
Data Interval: The default integration period (used here) is fifteen minutes.
Accuracy: Better than 1% (<0.5% typical) for V, A, kW, kVAR, kVA, PF
Resolution: 0.01 Amp, 0.1 Volt, 0.1 Watt, 0.1 VAR, 0.1 VA, 0.01 Power Factor
Cadmus installed the Dent dataloggers in the Stevens Library on October 25, 2012. Trained
metering specialists deployed the meters. Prior to meter deployment, all meters were
programmed, appropriately sized CTs connected, electrical configurations programmed,
batteries charged, and logger operation confirmed. Table 2 summarizes the temporary data
logger locations and circuit details. It also documents key spot measurements that were
made during logger deployment. Table 3 documents data logger details, including CT size,
meter ID, and available memory. The loggers recorded 15 minute integrated interval data.
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PG&E’s Emerging Technologies Program ET13PGE1021
TABLE 2: DATA LOGGER AND SPOT MEASUREMENT INSTALLATION SUMMARY
PANEL DESCRIPTION CIRCUIT
MEASURE
MENT
UNIT NOTES
B-L1
Total Building Electricity - Entire Panel B-L1 Feed
Panel
Feed kW
Exhaust Fan: EF-1B 9
Amps Spot measurement of 1 Amp BMS provides on/off data
Speakman Unit 1, 3 Amps
East duct heater 23, 25 Amps
West duct heater 5, 7 Amps
Split system units: CU-1B/3B, FC-1B, FC-3B 11, 13 Amps
Split system units: CU-2B/FC-2B 15, 17 Amps
Split system units: CU-4B/5B, FC-4B, FC-5B 19, 21 Amps
Electric water heaters 2, 4, 6,
8, 10, 12
Amps
R-Ozone generator 14 Amps
Pump P-2 16 Amps
Drainage Pump
20
Amps Drainage Pump was not
installed (found after installation)
UV Filter 18 Amps
Irrigation/graywater pump 24, 26 Amps
Rainwater Irrigation Pump 29 Amps
B-L2 Panel B-L2 Feed (plug loads, ceiling fan, misc.)
1, 3 kW kW
B-H1
Ltg - Reading Room Fluorescent Pendants Panel Feed kWh kWh
Ltg - Reading Room Downlights & Nook Panel
Feed kWh kWh
Ltg - Exterior Above Doors Panel
Feed kWh kWh
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PG&E’s Emerging Technologies Program ET13PGE1021
TABLE 3: DATA LOGGER CONFIGURATION DETAILS
At the end of the initial M&V period (December 2012) it was anticipated that the M&V would
be extended, and therefore the Dent dataloggers were left in situ. The second M&V phase
took effect in October 2013. The Dent loggers were downloaded. Some of the loggers
experienced battery failure and lost their data. The loggers that were still in operation had
run out of memory and had overwritten earlier data. Fortunately, a continuous record of the
BMS trend points, used as the primary M&V data source, was available during this period.
New Hobo U30 data loggers14 with cellular communications and Wattnode kWh
transducers15 were installed to replace the Dent dataloggers. The remote access capabilities
improved team access to the data and eliminated to eliminate the need for routine opening
of the electrical panels to access and download the data loggers. This provides improved
14 http://www.onsetcomp.com/products/data-loggers/U30-data-loggers 15 http://www.onsetcomp.com/products/sensors/t-wnb-3d-480
Logger ID
Installation
Location
Installation/ Panel
Location Circuit to be logged CT Desc
Expected
Max Amps CT Amps
Max
Logging
Duration
Ph A 342 500 55 days
Ph B 292 500 55 days
Ph C 307 500 55 days
Ph A 17 500 55 days
Ph B 7 500 55 days
Ph C 1 500 55 days
Ckt 37: Ph A 96 150 55 days
Ckt 39: Ph B 78 150 55 days
Ckt 41: Ph C 90 150 55 days
ckt 1 40 50 77 days
ckt 3 40 50 77 days
ckt 5 <50 50 77 days
ckt 7 <50 50 77 days
ckt 23 <50 50 77 days
ckt 25 <50 50 77 days
ckt 11 <50 50 77 days
ckt 13 <50 50 77 days
ckt 15 <50 50 77 days
ckt 17 <50 50 77 days
Ckt 19 <50 50 77 days
ckt 21 <50 50 77 days
ckt 2 <50 50 77 days
ckt 4 <50 50 77 days
ckt 6 <50 50 77 days
ckt 10 <50 50 77 days
ckt 14 - Ozone <50 50 55 days
ckt 16 Pmp P2 <50 50 55 days
ckt 18 UV Filter <50 50 55 days
ckt1 - Clg fans <50 50 77 days
ckt 3 - clg fans <50 50 77 days
DL26Electrical Room from B-L1 EWH2a/2b
DL-1Electrical Room from B-L1 CU2b/FC2b
DL15Electrical Room from B-L1 CU 4b/58
DL13Electrical Room from B-L1 EWH1
West Duct Heater
Speakman UnitDL24
from B-L1
DL2Electrical Room from B-L1 East Duct Heater
DL3Electrical Room from B-L1
Split HP CU1b 3b & FC
1b 3b
Feed to B-L1
Feed to B-H1
(alternately can be
main bldg feed if
From panel B-L1
Electrical Room
Electrical Room
Electrical Room
from B-L1DL7
Electrical Room
Electrical Room
DL10
Electrical Room From B-L1Graywater/Rainwate
r Equipment
DL18Electrical Room from B-L2 Ceiling Fans
DL8
DL6
DL19
Panel B-H1 Feed (or
main bldg feed)
Panel B-L1 feed
Panel B-L2 feed
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PG&E’s Emerging Technologies Program ET13PGE1021
risk management. Specifications are similar to the Dent specifications. All meters were
installed by Cadmus’ metering specialists.
RESULTS Key results of the Stevens Library M&V activities are reported here. Data is reported for the
period of 12/1/2012 through 9/30/2014, for which a complete and consistent set of energy
consumption data is available. Data for October and November 2012 is not displayed. Due
to building startup, occupant move-in, BMS programming, and related issues, there are
some data inconsistencies and the data is not representative of typical building use. Also
note that per discussion under the Data Validation section, the BMS’s reported lighting
power is incorrect due to a CT mismatch on the submeter. The BMS also incorrectly reports
the total facility electricity use, which is the sum of all the submeters. All of the data
presented below in the Data Analysis section use corrected values for both lighting and total
consumption.
DATA ANALYSIS
ENERGY BALANCE
The building is exceeding its ZNE performance goals by a significant margin. For 2013 the
PV system generated 54% more electricity than the building consumed. The trend is similar
for 2014. For the entire monitoring period (12/12 – 9/14), the PV system has generated
73% more electricity than the building has consumed.
Figure 17 plots the monthly building electricity use, PV generation and net energy, and
year-to-date (YTD) cumulative net energy16. There is significant excess solar energy
generation during the summer. This is due to a combination of reduced summer occupancy
and increased solar generation. The building is very efficient and has an energy use
intensity (EUI) of 18.5 ± 0.5 kBTU/ ft2/year17. This is significantly lower than the New
Building Institute (NBI) “Getting to Zero” Report18’s definition of “ZNE Capable” buildings19.
lots the monthly energy balance of the facility. This graph summarizes building ZNE
performance. Electricity used by the building is plotted on blue and electricity generated by
the PV system in red (both read on the left axis). Note that for February 2013 through
16 Net energy = PV generation – building use; positive means net electricity generation,
negative means net electricity use. 17 Average annual EUI based on the entire M&V period data from 12/12 – 9/14. Note that the
2013 EUI is 19.0 kBTU/ft2. The uncertainty is the standard deviation between 2013 data and
the extrapolated 2014 EUI data based on 1/2014 through 9/14 data. 18 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs
and Features of Zero Energy Commercial Buildings.” March 2012.
http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf 19 NBI defines ZNE Capable buildings as buildings with EUIs ≤ 35 kBTU/ft2/year. They do not
differentiate by use type.
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PG&E’s Emerging Technologies Program ET13PGE1021
December 2013 the PV generation is estimated due to a failure in the inverter
communication card that reports PV generation data to the BMS for trending. Data is
estimated with a high degree of confidence using a correlation discussed under the Data
Validation section. The year-to-date (YTD) cumulative net energy is plotted in green and
blue (filled) on the right axis. During the winter, the facility uses more electricity than it
generates. The YTD net energy starts the year in deficit. However, heating energy drops
and PV production increases for the rest of the year, resulting in an overall positive energy
balance. The building is healthily zero net energy.
FIGURE 17: MONTHLY ENERGY BALANCE AND YEAR-TO-DATE CUMULATIVE NET ENERGY
Figure 18 compares 2013 and 2014 monthly electricity consumption. As discussed in more
detail in the “Detailed System Performance Analysis” section, the differences are explained
by the following factors: 2013 has more heating degree days that 2014, which increases
2013 winter heating energy. In 2014, summer lighting power increases (no summer
reduction in lighting electricity is observed as per 2013) and plug loads trend upward.
-5,000
0
5,000
10,000
15,000
20,000
25,000
30,000
-2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
YTD
Cu
mu
lati
ve N
et
Ene
rgy
(kW
h)
Mo
nth
ly E
ne
rgy
(kW
h)
2013 YTD Cumulative Net Energy
2014 YTD Cumulative Net Energy
Building Electricity Consumption
PV Generation
Estimated PV Generation
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 18: 2013 VS 2014 MONTHLY LIBRARY ELECTRICITY USE
The net energy balance for 2013, 2014 (partial year) and entire M&V period is summarized
in Figure 19. The building is a net electricity generator (i.e., “net positive”) for 2013,
January through September 2014 (and should be net zero for all of 2014), and for the
entire M&V period (12/12 through 9/14). Note that PV electricity generation for the first
nine months of 2014 is almost the same as all of 2013. Refer to the “Detailed System
Performance Analysis/PV System” section below for additional discussion. The building is
FIGURE 19: NET ENERGY BALANCE
-
1,000
2,000
3,000
4,000
5,000
6,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
-
20,000
40,000
60,000
80,000
100,000
120,000
2013 2014 (partialyear, 1/14 -
9/14)
All Data (12/12 -9/14)
An
nu
al E
lect
rici
ty (
kWh
)
Building Electricity Consumption
PV Generation
Net Energy
35
PG&E’s Emerging Technologies Program ET13PGE1021
Table 4 provides actual monthly energy performance data. Refer to the preceding
paragraphs for relevant discussion.
TABLE 4: MONTHLY BUILDING ELECTRICITY CONSUMPTION AND GENERATION DATA
MONTH
BUILDING
ELECTRICITY
CONSUMPTION
(KWH)
PV GENERATION
(KWH) (ESTIMATED
DATA 2/13 -
12/13)
MONTHLY NET
ENERGY (KWH;
NOTE: + VALUES =
MORE GENERATED
THAN USED)
12/12 2,934 1,796 (1,138)
1/13 5,419 2,576 (2,843)
2/13 5,274 2,966 (2,309)
3/13 3,115 4,246 1,131
4/13 1,996 5,337 3,341
5/13 2,244 6,645 4,401
6/13 1,675 6,198 4,523
7/13 1,648 6,703 5,055
8/13 1,800 5,956 4,156
9/13 2,318 4,756 2,438
10/13 2,401 3,678 1,277
11/13 2,774 2,589 (185)
12/13 4,458 2,289 (2,168)
1/14 4,713 2,651 (2,062)
2/14 3,703 2,732 (972)
3/14 2,590 5,042 2,451
4/14 1,948 6,662 4,714
5/14 1,891 7,953 6,061
6/14 1,850 8,025 6,175
7/14 2,530 7,450 4,920
8/14 2,647 6,536 3,889
9/14 2,713 5,002 2,289
Table 6 summarizes 2013 annual, 2014 year to date, and total M&V period building energy
consumption and PV generation. The Library is a net energy generator on an annual basis.
Note that PV production for 2014 through September is almost equal to total 2013 PV
generation.
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PG&E’s Emerging Technologies Program ET13PGE1021
TABLE 5: ANNUAL AND M&V PERIOD SUMMARIES OF BUILDING ELECTRICITY CONSUMPTION AND GENERATION DATA
MONTH
BUILDING
ELECTRICITY
CONSUMPTION
(KWH)
PV GENERATION
(KWH) (ESTIMATED
DATA 2/13 -
12/13)
MONTHLY NET
ENERGY (KWH;
NOTE: + VALUES =
MORE GENERATED
THAN USED)
2013 Total 35,123 53,939 18,816
2014(partialyear,1/14-9/14)
24,586 52,052 27,466
All Data(12/12-9/14) 62,644 107,787 45,144
ENERGY END USES
Figure 21 summarizes the overall energy end use distribution for 2013 (for which there is
complete data for an entire calendar year). Space conditioning consumes just over 60% of
the building’s energy and is the biggest load. Of the space conditioning load, fans account
for 32%; heating for 48%; cooling for 15%; and pumps, controls, and other ancillary loads
for the remaining 4%. Lighting and non-regulated loads primarily under occupant control
(e.g., plug loads and ceiling fans) consume equal amounts at ~15% of the total building
load each. The rainwater and graywater collection system consumes 8% of the building
energy. The rainwater and graywater loads include a variety of pumps, UV sterilizers and
related equipment—refer to the Emerging Technology section for equipment details.
FIGURE 20: 2013 ANNUAL ENERGY END USE DISTRIBUTION
Space Conditioning, 21,367 , 61%
Lighting, 5,211 , 15%
Water Heating, 80 ,
0.2%
Rainwater/ Graywater
System, 2,750 , 8%
Ceiling Fans and Plug
Loads (Panel B-L2), 5,715 ,
16%
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Monthly end use breakouts are shown in Figure 21
FIGURE 21: MONTHLY ENERGY END USE DISTRIBUTION
ENERGY USE INTENSITY
A building’s Energy Use Intensity (EUI) is the amount of annual energy a building uses on a
square foot basis, with units of kBTU/ft2/year. During the M&V period, Stevens Library had
an EUI of 18.5 ± 0.5 kBTU/ft2/year20. Note that the 2013 EUI is 19.0 kBTU/ft2. The
uncertainty is the standard deviation between 2013 data and the extrapolated 2014 EUI
data based on 1/2014 through 9/14 data.
BENCHMARKED PERFORMANCE Figure 22 shows the adapted EUI scale from the New Building Institute (NBI)’s “Getting to
Zero” Report21 with the Library’s EUI indicated. NBI defines “ZNE Capable” buildings as
those with EUIs ≤ 35 kBTU/ft2/year. This is the upper limit of actual ZNE building energy
use (excluding renewables generation) that is in NBI’s ZNE building database. Clearly, the
Library’s EUI of 18.5 ± 0.5 kBTU/ft2/year is a very high performing building and one the
School and design team should be proud of.
20 the average annual EUI based on 12/12 – 9/14 data. 21 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs
and Features of Zero Energy Commercial Buildings.” March 2012.
http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf
-
1,000
2,000
3,000
4,000
5,000
6,000
12
/12
1/1
3
2/1
3
3/1
3
4/1
3
5/1
3
6/1
3
7/1
3
8/1
3
9/1
3
10
/13
11
/13
12
/13
1/1
4
2/1
4
3/1
4
4/1
4
5/1
4
6/1
4
7/1
4
8/1
4
9/1
4
Mo
nth
ly E
lect
rici
ty (
kWh
)
Rainwater/ GraywaterSystem
Space Conditioning
Water Heating
Lighting
Ceiling Fans and Plug Loads(Panel B-L2)
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 22: STEVENS LIBRARY BENCHMARKED AGAINST ZNE BEST PRACTICE GUIDELINES
The Library’s annual EUI (based on the entire M&V period data of 12/2012 – 9/2014) is also
benchmarked using the Lawrence Berkeley National Laboratory’s EnergyIQ benchmarking
tool (http://energyiq.lbl.gov/EnergyIQ/SupportPages/EIQ-about.jsp). Note that while The
library does not fit neatly into the predefined building use types, it is still of some use to
compare it to the nearest building types. Figure 23 through Figure 25 benchmark the
Library against three different peer groups (all schools, elementary and middle/secondary
schools only, and all office and schools) in the central coast region for all vintages of
buildings. Unfortunately, there is insufficient data to further narrow down the peer group by
vintage and other factors. All data is based on California’s Commercial End Use Study
(CEUS). Note that in the first two cases, the peer group is quite small (18 and 9 buildings
respectively), and that there is a wide variation in peer group EUI.
Figure 23 shows the distribution of energy use for school buildings in California’s central
coast (including the Bay Area) for all school buildings of all vintages and sizes. The median
EUI for these buildings is 24.3 kBTU/ft2/year, with a range of 13.5 to 52.1 kBTU/ft2/year
(5th to 95th percentiles). There are only 18 buildings in this category. The data is from
California’s Commercial Energy End Use Survey (CEUS), for all school types (pre-school
through secondary school), for all vintages. Insufficient data is available to refine the results
SHS LMS
Library (18.5
± 0.5 kBTU/SF)
39
PG&E’s Emerging Technologies Program ET13PGE1021
by vintage. It is interesting to note that there is a significant population of buildings with a
lower EUI of 15 kBTU/ft2/year. These are likely non air-conditioned buildings.
FIGURE 23: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST SCHOOLS
Figure 24 shows similar data, but refined to only show elementary and secondary school
buildings. There are only 9 buildings in this category. The median EUI for these buildings is
38.8 kBTU/ft2/year, with a range of 25.4 to 53.2 kBTU/ft2/year (5th to 95th percentiles).
Unfortunately, there is no descriptive data for the EnergyIQ benchmarked data (i.e.,
occupancy and schedule data such as whether the schools are in use year-round or off
during the summer). It is likely that most of the buildings are for entire schools, not just
school libraries. The use of this data is therefore limited, but it is nevertheless helpful to see
how the Library compares to the closest set of peer buildings for which there is data.
SHS LMS
Library (18.5
± 0.5 kBTU/SF)
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 24: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST ELEMENTARY AND MIDDLE SCHOOLS
For a broader perspective, Figure 25 shows benchmark data for all central coast office and
schools. This provides a more robust sample size of 131 buildings. The typical (median) EUI
for these buildings is 34.0 kBTU/ft2/year, with a range of 14.4 to 87.3 kBTU/ft2/year (5th to
95th percentiles). Note that most offices likely have year-round occupancy, whereas while
Steven’s Library is operational during the summer, it has minimal occupancy. Offices are
therefore expected to have higher EUI.
While these benchmarks have limitations, it does show that Steven’s Library is clearly a high
performing building regardless of the peer group it is compared to. Its use of direct and
indirect evaporative cooling, natural ventilation, low plug loads, efficient envelope, efficient
lighting and controls, and comparatively low operating hours (minimal summer occupancy;
academic calendar breaks and holidays) help it achieve this performance.
SHS LMS Library
(18.5 ± 0.5 kBTU/SF)
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 25: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST OFFICE AND SCHOOL BUILDINGS
OCCUPANT SURVEY
The librarian was surveyed to document use patterns, system performance, and related
data. The purpose of this survey is to obtain space usage data to correlate with M&V data,
and to identify any related issues that affect performance or comfort (e.g., temperature
control problems). Key results are summarized below. The full survey and responses is
provided in the appendix.
OPERATING HOURS AND OCCUPANCY
The library is typically occupied between 7am-4:30pm on weekdays. The librarian typically
arrives at 7 am and departs at 4:30 pm, and the library is open to students per the
following schedule:
Monday: 7:30-3:30pm
Tuesday-Thursday: 7:30 – 4pm
Friday: 7:30-3:30pm
There is some weekend use of the space for meetings and other school events. However,
this is not intensive, occurring approximately 1 day per month.
The typical occupancy and use patterns for the library are as follows:
The best estimate of total patrons (students) visiting the library throughout the day
is 75-100 people per day.
SHS LMS Library
(18.5 ± 0.5 kBTU/SF)
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PG&E’s Emerging Technologies Program ET13PGE1021
The Librarian has ~ 14 classes/week using the library. Typical classes have 18
students and each class spends 15-30 minutes in the library.
Some students use the facility during lunch (12pm-1pm). During standardized
testing, there were approximately 50 students occupying the main library. However
this is infrequent. Aside from this, the maximum number of library visitors at any
one time is estimated at around 30 people.
The typical occupancy and use of the classrooms, tutoring rooms and other rooms are as
follows:
Tutoring Room (Rm 203): 2 people
Conference Room: When occupied, typically 2-4 people.
Tech Office: 2 employees, students visit throughout the day for computer servicing.
Students come to the tech office for computer support. This occurs throughout the
day, heavy traffic occurs during lunch
Next to Tech Office: 2 employees
The custodial staff cleans the library once daily. The specific timing of the cleaning is
variable.
TEMPERATURES
Occupants report that the library temperatures are generally satisfactory, except during the
winter when the mornings are cold. Space temperatures are typically comfortable by around
8:20 am. The space is reported to generally warm up throughout the day. The main RTU
serving the library was warrantied due to the heat pump locking out. It is believed that this
has helped address the problems. The space temperature was reported by one librarian as
never being too hot. However, another reported that in the summer when the sun is out,
that the space can be a bit warm.
When there are a number of people occupying the library at a given time (i.e. standardized
testing), interviewees feel that there is insufficient outside air and the HVAC system has
trouble maintaining space temperature. However, it is reported that the space is generally
able to maintain temperature setpoints during large occupancy changes.
The temperatures in the smaller classrooms and tutoring rooms are generally comfortable,
although there are times when the space gets “stuffy”. Occupants typically open windows
when this occurs. Note that there are no window interlock switches that automatically turn
the air-conditioning off when windows are opened, so the air-conditioning will continue to
run unless occupants manually turn it off. The technology coordinator office gets a bit warm
due to the number of electronic equipment in the space. Staff does not complain and
instead opens windows so the room feels uncomfortable.
VENTILATION AND FRESH AIR
Occupants have made consistent remarks regarding perceived air quality. Occupants feel
that private offices and main reading room can get stuffy on occasion. It is possible that this
is related to humidity, specifically increased humidity due to the direct evaporative cooling
mode. It should be noted that the main library reading room has CO2 sensors and demand
control ventilation, which appears to be working correctly, without excessive CO2 levels
observed. Also note that there was a warranty repair on EC-1 (the main reading room HVAC
unit) to correct a problem related to compressor heating lockout, which was leading to
problems meeting space temperatures on colder days. It is possible that this issue led to
some occupant feelings of stuffiness in the main reading room. Occupants of private spaces
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PG&E’s Emerging Technologies Program ET13PGE1021
typically leave windows open when they feel stuffy. This can have energy implications if
consistently done with the heating/cooling system on. BMS data does not indicate that this
a major issue at present.
CEILING FANS
The library is equipped with ceiling fans in the main reading room. The ceiling fans are
manually operated. The Librarian only uses these fans when the library is fully occupied.
She feels the fans create a lot of noise.
LIGHTING
The occupants are unaware of dimming controls on the lighting system. During the summer,
occupants report regular glare problems and close the window shades during periods of
glare. Occupants believe the occupancy controls are working as intended. There was some
concern at the start of the project that the ceiling fans may keep the occupancy sensors
from working correctly. However, no problems have been reported.
EQUIPMENT AND PLUG LOADS
Cadmus inventoried the interior equipment and plug loads as part of the occupancy survey.
Equipment and plug loads are summarized in Table 6
TABLE 6: LIBRARY EQUIPMENT AND PLUG LOADS COUNT
Equipment Quantity Notes/Comments desktop computers and monitors 10 laptops - TV’s and additional
screens/monitors 1 in reading room
printers 3 large photocopiers - small/medium photocopiers - refrigerators/freezers - Computer cart/charging station 1 in tech room for laptops
DETAILED SYSTEM PERFORMANCE ANALYSIS Individual system performance was reviewed to ensure that all systems were properly
operating and to identify issues that could negatively impact energy use. All systems were
analyzed in detail at the start of the M&V period. System performance was then reviewed
throughout the project on an as-need basis only as issues arose.
WEATHER
Weather drives a significant portion of the Library’s energy use. Historical weather data for
the M&V period is presented below and referred to throughout the discussion to help
interpret and analyze the results.
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PG&E’s Emerging Technologies Program ET13PGE1021
Historical weather data for the site was obtained for the M&V period from a local weather
station in Atherton (station ID = KCAATHER422). The design phase building energy model
(used to document LEED energy points, California Title 24 compliance, and estimate annual
energy use for ZNE sizing calculations) and the subsequent energy model updates and
calibrations use California Climate Zone 4 climatic data (CACZ04)23. The use of standard
typical meteorological year (TMY) type climatic data such as the California Climate Zone
climatic data is common practice for design teams and energy modelers.
Figure 26 and Table 7 compare CACZ04 climatic data to historical heating degree day (HDD)
data during the M&V period. 2013 has more HDD’s than 2014 and 2014 has more HDDs
then CACZ04 climatic data, particularly for December and January. This leads one to expect
more heating energy in 2013 than 2014, and more heating energy in both 2013 and 2014
than the modeled data which uses the CACZ04 data. Refer to the “Building Energy Model
Evaluation” section for more discussion.
FIGURE 26: HEATING DEGREE DATA DURING THE M&V PERIOD
22 Historical data is available from
http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KCAATHER4&day=1
4&year=2012&month=10&graphspan=month 23 The actual filename used is CZ04RV2
-
100
200
300
400
500
600
HD
D B
ase
18
C/6
5F
CA CZ04
2012
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
TABLE 7: HEATING DEGREE DATA DURING THE M&V PERIOD
Figure 27 and Table 8 compare CACZ04 climatic data to historical heating degree data
(HDD) during the M&V period. 2013 and 2014 are hotter than the CACZ04 climatic data.
This leads one to expect increased air conditioning energy compared to modeled cooling
energy. Also, July and August were significantly hotter than 2014 than in 2013. Thus,
cooling energy should be higher during these months in 2014.
FIGURE 27: COOLING DEGREE DATA DURING THE M&V PERIOD
CA CZ04 2012 2013 2014 2012 2013 2014
January 270 511 361 241 91
February 195 380 298 185 103
March 170 264 190 94 20
April 121 141 169 20 48
May 57 105 68 48 11
June 5 26 29 21 24
July 1 9 - 8 (1)
August - 9 - 9 -
September - 11 5 11 5
October 54 140 86
November 185 233 254 48 69
December 255 513 258
Through September 819 1,120 301
Annual Total 1,313 2,363 1,050
HDD base 18C/5F Difference from CA CZ04
0
20
40
60
80
100
120
140
160
180
CD
D B
ase
18
C/6
5F
CA CZ04
2012
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
TABLE 8: COOLING DEGREE DATA DURING THE M&V PERIOD
Figure 28 summarizes the average daily maximum temperature for each month during the
M&V period and for CACZ04. Average monthly maximum temperatures are very consistent
for all three periods. Interestingly, the peak temperatures are higher for the climatic data
during the summer, although 2013 and 2014 have more CDDs.
FIGURE 28: AVERAGE MAXIMUM MONTHLY TEMPERATURE DURING THE M&V PERIOD
CA CZ04 2012 2013 2014 2012 2013 2014
January 0 0 0 - -
February 0 0 0 - -
March 0 0 0 - -
April 1 36 25 35 24
May 3 58 52 55 49
June 26 111 77 85 51
July 49 93 168 44 119
August 67 124 155 57 88
September 41 120 107 79 66
October 9 29 20
November 0 5 0 5 -
December 0 0 -
Through September 187 584 (235)
Annual Total 196 571 375
CDD base 18C/65F Difference from CA CZ04
-
10
20
30
40
50
60
70
80
90
Tem
pe
ratu
re (
F)
CA CZ04
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 29: AVERAGE MEAN MONTHLY TEMPERATURE DURING THE M&V PERIOD
FIGURE 30: AVERAGE MINIMUM MONTHLY TEMPERATURE DURING THE M&V PERIOD
-
10
20
30
40
50
60
70
80
90
Tem
pe
ratu
re (
F)
CA CZ04
2013
2014
-
10
20
30
40
50
60
70
80
90
Tem
pe
ratu
re (
F)
CA CZ04
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 31: AVERAGE RELATIVE HUMIDITY DURING THE M&V PERIOD
FIGURE 32: AVERAGE WIND SPEED DURING THE M&V PERIOD
0
10
20
30
40
50
60
70
80
90
Re
lati
ve H
um
idit
y
CA CZ04
2013
2014
0
1
2
3
4
5
6
7
8
9
Win
d S
pe
ed
(M
PH
)
CA CZ04
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 33: AVERAGE CLOUD COVER DURING THE M&V PERIOD
PV SYSTEM
The library has a 40 kWDC rated Photovoltaic (PV) electricity generation array.
As discussed in the PV system description in the “Emerging Technology” section, the original
design phased PV performance calculations were based on an array tilt of 30o to the South,
whereas the final design had the PV array mounted flat on the roof.
Figure 34 summarizes the original design phase PV performance analysis inputs and results.
PV Watts was used for the analysis. The analysis also assumed a 0.770 de-rate factor
(accounting for inverter losses, collector soiling, and other losses), for a total AC power
output of 30.8 kWAC and an annual power generation of 58,032 kWh. Figure 35 shows the
revised PV calculations for the final design’s flat installation. Power capacity remains at 30.8
kWAC , but annual output drops to 50,263 kWh, a 7,769 kWh or 13% decrease. This drop in
expected power led the design team to replace the gas heated HVAC system for the main
reading room with an electric heat pump system. This change reduces site energy due to
the efficiency/COP difference between gas heating and heating with a heat pump.
0%
10%
20%
30%
40%
50%
60%
70%
Clo
ud
Co
ver
CA CZ04
2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 34: ORIGINAL PV PERFORMANCE PROJECTIONS FOR 30O
TILT
FIGURE 35: REVISED PV PERFORMANCE PROJECTION FOR PANELS FLAT ON ROOF
Figure 36 shows actual power output measured during the initial system performance
analysis (November-December 2012). The maximum output observed during the initial M&V
was 30.8 kW, occurring on 11/17/12. This matches the projected power output from the PV
Watts design calculations.
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 36: PV OUTPUT (KW) DURING THE INITIAL SYSTEM PERFORMANCE ANALYSIS (NOVEMBER – DECEMBER 2012)
During the initial system performance analysis period (November – December 2012), the
daily incident solar radiation striking the PV panels24 versus the PV output was plotted to
confirm that the PV system was performing as expected. This plot was very similar to Figure
38 (Figure 38 includes additional data through January 2013, which did not result in any
significant change in chart shape or best fit slope. See discussion in next paragraph). The
trend is consistent with expectations and indicates the system is performing well.
Upon commencement of the second M&V period starting in September 2013, significant
periods of missing PV data were identified starting in February 2013. Figure 37 shows an
example of the missing data.
24 The daily incident solar radiation comes from the California Irrigation Management
Information System (CIMIS)’s spatial data (http://wwwcimis.water.ca.gov/SpatialData.aspx),
which reports data at 2km spatial resolution using remotely sensed data from the
Geostationary Operational Environmental Satellites (GOES) coupled with the Heliosat-II
model. Measured insolation data for the site is not available.
0
5
10
15
20
25
30
35
PV
Ou
tpu
t (k
W)
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 37: OCTOBER 2013 PV DATA SHOWING GAPS IN LOGGED DATA (REPRESENTATIVE OF 2/13 – 12/13 PV DATA)
The problem turned out to be a failing communication card in the inverter that sends PV
generation data to the BMS system. Interface Engineering Inc. (the MEP consultant on the
project) worked to get the problem resolved. The card was replaced in mid-December 2013,
and the system has worked since. To fill in missing data, a correlation between daily PV
output versus daily solar radiation25 was developed. This correlation is shown in Figure 38,
and includes data from November 2012 through January 2013. The coefficient of
determination (r2) is 0.92, which along with review of the residuals indicates a very good fit,
and a monthly uncertainty of ±11%. Missing PV data from February 2013 through
December 2013 was estimated using this curve.
25 The daily incident solar radiation comes from the California Irrigation Management
Information System (CIMIS)’s spatial data (http://wwwcimis.water.ca.gov/SpatialData.aspx),
which reports data at 2km spatial resolution using remotely sensed data from the
Geostationary Operational Environmental Satellites (GOES) coupled with the Heliosat-II
model. Measured insolation data for the site is not available.
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 38: INCIDENT SOLAR RADIATION VERSUS PV OUTPUT FOR NOVEMBER 2012 THROUGH JANUARY 2013
Figure 39 compares the PV output design projections for a 30o tilt and 0o tilt to 2013 data
and 2014 partial year data (January – September). The PV system is outperforming the
revised PV Watts design estimate.
FIGURE 39: COMPARISON OF PV OUTPUT DESIGN PROJECTIONS TO 2013 AND 2014 (PARTIAL YEAR) GENERATION DATA
Figure 40 shows 2013 estimated and 2014 measured monthly PV generation data.
It is interesting to note that PV output for the first 9 months of 2014 is almost equal to
2013’s total annual output. PV generation for January, February and September is almost
58,032
50,263 53,939 52,052
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
kWh
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PG&E’s Emerging Technologies Program ET13PGE1021
identical in 2013 and 2014, but summer 2014 PV generation is significantly higher than
2013 generation. The reason for this was explored. All PV output data from the BMS and
missing data estimates were double checked and confirmed. The primary explaining variable
appears to be the fact that cloud cover is higher for 2013 during this period for all months
except April. (refer to Figure 41 below). It is likely that the differences are further explained
by average cloud cover vs. time of day (i.e., cloudier during the middle of the day/afternoon
during peak PV generating hours). Unfortunately, the historical cloud cover data is only
available on an average daily basis. To further assess difference the data, hourly cloud
cover data and ideally solar radiation would be needed. There are also variations in other
weather parameters, such as temperatures, cooling degree days, which could impact PV
generation (i.e., PV output drops with temperature). Further assessment of the differences
between 2013 and 2014 YTD PV output is beyond the scope of this report. However, it
should be noted that the likely issue is caused by the increased cloudiness in 2013, and that
no problems with the PV system are noted; in fact, the PV system is performing better than
expected during both 2013 and 2014.
FIGURE 40: 2013 ESTIMATED AND 2014 MEASURED PV GENERATION
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 41: AVERAGE MONTHLY CLOUD COVER FOR MARCH-AUGUST
TOTAL HVAC ENERGY
Figure 42 compares total measured HVAC electric use between 2013 and 2014. Note that
HVAC energy use is higher for 2013. This corresponds well to the higher heating degree
days in 2013 (refer to Figure 26). Note that measured monthly energy consumption is only
available for total HVAC use, and is not available by specific HVAC unit.
FIGURE 42: 2013 AND 2014 MEASURED HVAC ELECTRICITY CONSUMPTION
INDIRECT EVAPORATIVE COOLER/ HEAT PUMP RTU (EC1)
The main library reading area is served by a single Speakman direct/indirect evaporative
cooler/heat pump RTU (unit EC1). Refer to the “Emerging Technology” section for a detailed
system description.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Mar Apr May Jun Jul Aug
Ave
rage
Mo
nth
ly C
lou
d C
ove
r
2013
2014
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
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PG&E’s Emerging Technologies Program ET13PGE1021
During the initial assessment period (September 2012 December 2012), a detailed system
performance assessment was conducted to confirm system performance is as expected.
BMS trend data and temporary data loggers were used to assess sub-system performance.
Based on the data, all systems appeared to be functioning properly: space temperatures
were being maintained, supply air temperatures were modulating as expected, system
scheduling/night setback was working, demand controlled ventilation system was
modulating as expected, space CO2 setpoints were maintained, the fan was operating as
expected, the compressor showed minimal use as expected for the weather, and both the
direct and indirect evaporative cooling systems were engaging as appropriate. Subsystem
performance is discussed in more detail below.
Figure 43 summarizes EC1’s operating the characteristics (i.e., percent of time each system
was in operation) for the initial M&V period of November through December 2012, and all of
2013. During the initial M&V period, the fan operated 31% of the time, the compressor only
operated 6% of the time, the direct evaporator section was active 11% of the time, and the
indirect evaporator section was active 1% of the time. Performance was very similar for all
of 2013, except that there was increased compressor use. This is expected as the initial
M&V period did not capture hotter summer weather.
FIGURE 43: EC1 OPERATING CHARACTERISTICS
Figure 44 shows the supply air temperatures, outside air temperature, incoming (outside)
air temperature, and room temperatures from the BMS system during the initial detailed
system performance analysis in November 2012. The time period includes the Thanksgiving
holiday (Wednesday 11/21/2012 – Sunday 11/25/2012). Room temperatures are
maintained between 65oF and 72oF. The high temperature spikes shown are for the supply
air temperature and occur when AC1 is in heating mode providing hot air to the zones. No
abnormal operations were observed. Note that the anomalous data between 11/14 and
11/15 represents a period when the BMS was not recording data.
31%
6%
11%
1%
34%
15%13%
2%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Fan Compressor Direct Evap Indirect Evap
Pe
rce
nt
of
Tim
e in
Op
era
tio
n
Nov -Dec 2012
2013
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 44: EC1 TEMPERATURE DETAILS FOR NOVEMBER 2012
Figure 45 shows the measured zonal CO2 levels. There are a few cases where CO2 levels
spike around 1000 PPM, but the system appears to be modulating the outside air dampers
to bring levels back down.
FIGURE 45: EC1 SPACE CO2 LEVELS FOR NOVEMBER 2012
50
60
70
80
90
100
110Te
mp
(F)
Supply Air Temp 1 Supply Air Temp 2 West Room Temp Incoming Air East Room Temp
0
200
400
600
800
1000
1200
1400
CO2
Leve
l (PP
M)
East CO2 Sensor
West CO2 Sensor
BMS Off Heating On
BMS Off
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PG&E’s Emerging Technologies Program ET13PGE1021
Figure 46 through Figure 48 plot the logged average hourly electric demand for the HVAC
equipment for the initial M&V period, a typical week, and a typical day, respectively. Note
that units are off (with the exception of CU/FC 4, 5 which serves the electric closet) on
weekends and the Thanksgiving holiday, and during the unoccupied period.
FIGURE 46: AVERAGE HOURLY HVAC KW FOR THE INITIAL M&V PERIOD (OCTOBER THROUGH DECEMBER 2012)
FIGURE 47: AVERAGE HOURLY HVAC KW FOR A TYPICAL HEATING SEASON WEEK (11/26 – 12/2 2012)
0
1
2
3
4
5
6
7
kW
Speakman CU/FC 1/3 CU/FC 2 CU/FC 4,5
0
1
2
3
4
5
6
7
kW
Speakman CU/FC 1/3 CU/FC 2 CU/FC 4,5
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 48: AVERAGE HOURLY HVAC KW FOR A TYPICAL DAY (FRIDAY 11/30/12)
Figure 49 summarizes the daily HVAC kWh for the initial M&V period. Also shown on this
graph is the daily heating degree days (HDD, base 60). Figure 50 shows the same data, but
with heating degree days plotted against kWh. There is a clear relationship between HDD
and HVAC energy use. The flat line at the bottom shows a constant 10 kWh/day irrespective
of HDD is the energy use during weekends and holidays when the main HVAC systems are
off but the electrical closet heat pump is on.
0
1
2
3
4
5
6
7kW
Speakman CU/FC 1/3 CU/FC 2 CU/FC 4,5
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 49: DAILY HVAC KWH FOR THE INITIAL M&V PERIOD AND HEATING DEGREE DAYS (HDD) FOR (OCTOBER THROUGH
DECEMBER 2012)
FIGURE 50: DAILY HVAC KWH VS. HEATING DEGREE DAYS
During the second phase of the M&V (January 2013 – September 2014), a number of issues
arose with the reading room RTU. The system had problems meeting morning heating
needs. The unit was taken out of night setback mode in an effort to get the space to
maintain comfortable conditions. It was eventually discovered that the heat pump’s
compressor was being disabled from use (locking out) at relatively moderate outside air
0
10
20
30
40
50
60
70
HVAC kWh HDD
0
10
20
30
40
50
60
70
0 5 10 15 20 25
Dai
ly H
VA
C k
W
Heating Degree Days (HDD)
kWh
(b
lue)
an
d H
DD
(re
d)
Dai
ly H
VA
C k
Wh
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PG&E’s Emerging Technologies Program ET13PGE1021
temperatures. Thus, the heat pump was not providing heating. The M&V team reviewed
trend data and provided this to Sacred Heart Schools and the engineer. Note that during the
time running up to the EC1 replacement, no apparent problems were seen in the BMS trend
data that Cadmus had access to. It appeared that supply air temperatures were modulating
correctly (but this may have been due to the electric duct heaters). Cadmus did not have
access to the airflow data (CFM) that would have been useful to help diagnose this issue.
From the data reviewed, it appeared that there may not have been sufficient airflow. It is
possible that the compressor locking out prevented the fan from ramping up. The unit was
eventually warrantied by the manufacturer. The system appears to be working well now,
and the problem appears to be corrected. The library staff will need to monitor system
performance as it moves into the 2014 heating season.
Aside from the above issues, the primary HVAC unit serving the reading room (EC1)
performance worked well. Key performance parameters for October 2013 are summarized in
the following graphs. Figure 51 shows equipment (fan, compressor, direct evaporative
cooling pump and indirect evaporative cooling pump) status (on/off). Systems are
performing as expected, with no anomalous behavior. Minimal compressor (mechanical
cooling) use is observed, with direct evaporative cooling in operation. Indirect evaporative
cooling is not used, which would not necessarily be expected given the minimal cooling load
during the period and the higher winter relative humidity levels.
FIGURE 51: EC1 HVAC UNIT FUNCTION STATUS (OCTOBER 2013)
Figure 52 shows EC1 CO2 levels and air temperatures for a representative period (October
2013. The y-axis units for CO2 are PPM, and the y-axis units for temperatures are in oF. The
system appears to be under proper control. CO2 spike up during occupancy events, but are
brought back under control as the demand controlled ventilation system increases outside
air ventilation. The highest CO2 levels observed is 1000 PPM (a single event), with the
system responding quickly to bring this down as anticipated.
Unit o
pera
ting s
tatu
s (
hig
h =
on,
low
= o
ff)
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PG&E’s Emerging Technologies Program ET13PGE1021
Only 5 days of heating are observed during the period (heating is indicated by the spike in
the supply air temperatures). This is expected given the daily temperatures during this
period (see Figure 53), as average daily outside air temperatures are dropping during the
last part of the month when heating is observed.
FIGURE 52: EC1 HVAC UNIT CO2 AND TEMPERATURES (OCTOBER 2013)
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 53: DAILY TEMPERATURE RANGES (OCTOBER 2013
PACKAGED HEAT PUMP UNITS
The study rooms and classrooms are served by high efficiency variable speed heat pump
units, summarized under the “Emerging Technology” section.
Figure 54 through Figure 58 summarize the room temperature trend logs for the rooms
served by the packaged heat pumps for November 2012. These temperature trends were
reviewed to identify operational issues, temperature control, and other issues. Room
temperatures indicate that the HVAC systems are generally turning off at night, allowing the
room temperatures to drift during the off periods. It appears that the thermostats all have a
relatively large dead-band (i.e., throttling zone) that allows the space temperature to drift
significantly (anywhere from 2 – 5oF) before the system turns on to heat or cool the room
back to the temperature setpoint. Compare the large daily temperature variations for most
rooms to the much tighter temperature control in the electrical room (Figure 56), where the
unit maintains a ~±2oF setpoint with no night setback. This wide deadband is generally a
good thing from the energy perspective, but it may not be ideal for humidity/moisture
control. It is also possible that the wide swings are indicative of the heat pumps being
undersized to meet zone loads, and/or routine opening of the windows.
The one potential energy conservation measure that Stevens Library may want to consider
is increasing the cooling setpoint for the electrical room, which contains a variety of servers.
The temperature is kept below 69.5oF. Typically, servers have higher acceptable operating
temperature ranges. The server’s temperature specifications should be reviewed and the
cooling temperature setpoint increased accordingly.
0
10
20
30
40
50
60
70
80
90
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Tem
pera
ture
(F)
October 2013
Max
Mean
Min
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 54: ROOM 201 TUTORING ROOM TEMPERATURE (NOVEMBER 2012)
FIGURE 55: ROOM 204 TECH ROOM TEMPERATURE (NOVEMBER 2012)
FIGURE 56: ROOM 206 ELECTRICAL ROOM TEMPERATURE (NOVEMBER 2012)
50
55
60
65
70
75
80
Zon
e T
em
p (
F)
Sample Time
Room 201 Tutoring Room Thanksgiving 11/21-11/23
50
55
60
65
70
75
80
Zon
e T
em
p (
F)
Room 204 Tech RoomThanksgiving 11/21-11/23
50
55
60
65
70
75
80
Zon
e T
em
p (
F)
Room 206 Electrical Room
Thanksgiving 11/21-11/23
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 57: ROOM 207 TEMPERATURE (NOVEMBER 2012)
FIGURE 58: ROOM 211 OFFICE TEMPERATURE (NOVEMBER 2012)
Figure 59 through Figure 63 plot the same temperature data except for the most recent
year-long period from 10/13 through 9/14. Note that these plots contain room temperatures
when the rooms are occupied, as well as unoccupied and in night setback mode. These plots
reveal long term average room temperature fluctuations and indicate HVAC system
performance. Note that maximum and minimum room temperatures fluctuate seasonally
(i.e., when the units turn off in the summer after work, room temperatures drift up towards
warmer ambient temperatures, and vice-versa during winter). This is indicative of proper
night setback control. Temperature setpoints generally appear to be maintained at
reasonable levels.
Figure 59 shows the tutoring room temperatures. Summer average temperatures are
around 70-75oF with night time temperatures in night setback mode drifting up to 80oF.
Winter average occupied temperatures are generally maintained at 68-70oF and allowed to
50
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Thanksgiving 11/21-11/23
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drift down to 60oF during unoccupied periods. Note that the tutoring room is a corner room
with more exterior exposure than some of the other rooms (see Figure 4). This exposure
would account for the quicker temperature drops/rises during night setback mode.
FIGURE 59: ROOM 201 TUTORING ROOM TEMPERATURES (10/13 – 11/14)
The Tech Room temperatures (Figure 60) show a similar pattern, but with less extremes.
This is expected given that this room has less exterior exposure than the tutoring room.
FIGURE 60: ROOM 204 TECH ROOM TEMPERATURES (10/13 – 11/14)
Note that the electrical room (Figure 61) temperature is more tightly controlled on the
upper end and is not allowed to go into night setback. This room is equipped with A/C only
to prevent equipment from overheating due to internal equipment heat gains. Due to
internal heat gains, this unit is almost always in heating mode. Note that the thermostat
setting has been adjusted upward from its original low temperature, per M&V feedback. This
will help save energy.
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FIGURE 61: ROOM 206 ELECTRICAL ROOM TEMPERATURES (10/13 – 11/14)
FIGURE 62: ROOM 207 MEETING ROOM TEMPERATURES (10/13 – 11/14)
The office (Figure 63) is interior and temperatures do not drift as far during night setback.
FIGURE 63: ROOM 211 OFFICE TEMPERATURES (10/13 – 11/14)
EXHAUST FANS
The exhaust fan consumes minimal energy; energy consumption for the exhaust fan was
not directly measured due to space constraints in the electrical panel, but was included in
total panel logged data.
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LIGHTING
Lighting is provided by a combination of T5 fluorescent tubes and CFL down lights, with
daylight and occupancy control in all main spaces. Lighting power is monitored by the BMS.
Note that the current transducer (CT) installed on the lighting submeter is the wrong size
and reads incorrectly. An updated calibration factor has been developed for this submeter to
correct the BMS reported data. Refer to the “Technology Evaluation” and “Data Validation
and Quality Control” sections for a detailed discussion. Data presented in this section
contains “correct” lighting data. Specifically, data for 10/2012 through 12/2012 comes from
Cadmus’ temporary loggers installed on the lighting circuit, and data for 2013 through 2014
is corrected BMS data. The estimated uncertainty in this corrected lighting data is ±10% for
hourly peak demand (kW) and consumption (kWh) results, and 1% difference between the
corrected BMS data and the Dent data.
For 2013 (for which a complete year of M&V data is available), lighting consumes 5,211
kWh, or 15% of the buildings’ electricity, and the building has a 0.827 kWh/ft2/year annual
lighting power density. For the first nine months of 2014, lighting consumed 5,317 kWh, or
22% of the building total year-to-date. There was a significant increase in lighting electricity
consumption.
Figure 64 plots the monthly lighting electricity use. Note that for 2013, lighting power
dropped significantly during the spring and summer. Summer lighting power reductions are
expected due to summer break. Spring variability is also expected due to daylighting control
and occupancy sensors. There is a noticeable change in 2014 where lighting power stays
comparatively high, does not show the summer break decrease. Extrapolating 2014 data
(assuming a typical monthly electricity use of ~600 kWh), the projected 2014 lighting
electricity use will be ~7,100 kWh for an annual lighting power density of ~ 1.125
kWh/ft2/year. The library should carefully track lighting energy and ensure that lighting
controls (occupancy sensors, daylighting controls) continue to operate correctly, and that
any issues precluding their full utilization (i.e. glare) be addressed.
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FIGURE 64: MONTHLY LIGHTING ENERGY USE FOR THE ENTIRE M&V PERIOD
Lighting energy consumption was reviewed in detail during the initial M&V period (October –
December 2012) to ensure that the lighting systems and controls were operating as
intended. Figure 65 plots the measured lighting power density (LPD) during November
2012, and Figure 66 shows the detailed load shape for a typical day in November. The LPD
load shape is indicative of occupancy and photo sensor operation, showing that the lights
are dimming and/or shutting off throughout the day as daylighting permits and/or rooms
are left unoccupied. From Figure 65, it can be seen that the peak lighting power density is
just over 2 W/SF on Thursday 11/29/12, with typical daily peak lighting power between 1.5
and 2 W/SF. The average winter weekday LPD is ~0.95 W/SF during occupied hours (7 AM
– 4 PM), and 0.2 W/SF after hours (5 PM – 6 AM weekdays, and most weekends. There is
occasional Saturday or weekend activity (i.e., Saturday 11/17).
0
100
200
300
400
500
600
700
800
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
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FIGURE 65: MEASURED LIGHTING POWER DENSITY (LPD) FOR NOVEMBER 2012
The representative winter weekday LPD profile shown in Figure 65 shows an increase in
lighting power from 10 – 11 PM from approximately 0.2 to 0.5 W/SF (or ~1900 Watts). The
daily load profiles typically show some type of night time lighting increase, but it is usually
smaller and around 0.1 W/SF (630 W). There is also similar night time and early morning
spikes during the weekends occurring one to three times per night/mornings. It is believed
this is site security patrols triggering motion sensor lights.
-
0.5
1.0
1.5
2.0
2.5LP
D (
W/S
F)
Weekend
Weekend
Weekend
Weekend
Thanksgiv
ing H
oliday
Security?
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FIGURE 66: LIGHTING POWER DENSITY FOR A TYPICAL DAY (MONDAY, 11/12)
DHW
The building is equipped with three tankless electric water heaters, EWH-1 serves the
bathrooms, and EWH-2A and EWH-2B serve the janitor closet. For 2013, DHW consumed 80
kWh, accounting for 0.2% of the building’s total electricity use. Electricity use for 2014 YTD
(through September) is similar.
Figure 67 plots monthly DHW electricity consumption. Two patterns are observed. First,
there is a large spike in hot water electricity use in July or August. This is slightly more than
double typical consumption. This is most likely due to custodial activities preparing for the
school year. The second trend noted is decreased DHW use during June due to summer
vacation.
-
0.5
1.0
1.5
2.0
2.5LP
D (
W/S
F)
Security or
custodial
Lights turn
on full in
morning
when there
is minimal daylighting
Lighting
power
gradually
declines as
daylighting increases Likely
occupancy
sensors in
classrooms and offices
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FIGURE 67: MONTHLY LIGHTING ENERGY USE FOR THE ENTIRE M&V PERIOD
Figure 68 plots the detailed logged electricity consumption during the initial M&V period. No
significant changes were observed during the second M&V period. There is minimal DHW
use, with many days showing no consumption. Almost all of the consumption is for the
bathrooms, there is nearly no DHW use for the janitor closets.
FIGURE 68: DHW MEASURED ELECTRICITY USE FOR OCTOBER AND NOVEMBER 2012
-
2
4
6
8
10
12
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10
kW
October November
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DHW consumption was significantly over-estimated during design phase ZNE projections
and for the LEED energy model. The original LEED energy model specified the DHW system
as a tank-type water heater which uses significantly more energy than the tankless heaters
that were specified. Another reason for the over-estimate of water use is the assumption of
the number of hand washes and other hot water draws. LEED water use calculator assumes
daily fixture uses. Based on the consumption data shown in Figure 68, it is apparent that
daily DHW use assumptions are incorrect for this facility.
RAINWATER AND GRAYWATER SYSTEMS
On average, the rainwater and graywater systems use 5.77 kWh/day, or 7.8% of the
Library’s total electricity consumption in 2013. This is a significant portion of the Library’s
total electricity use. Figure 69 shows the monthly rainwater and graywater system
electricity use, both in total kWh/month as well as percent of the Library’s total monthly
electricity use. Figure 70 shows the same consumption data comparing 2013 and 2014.
There is significant seasonal variation, and during summer months the rainwater and
graywater system can represent up to 25% of the total building electricity use. It should be
noted that total building electricity use drops dramatically in summer, so this 25% of the
total needs to be taken in context. The large summer consumption is driven by the
graywater irrigation system. Summer irrigation needs are higher, so the irrigation system is
in operation more frequently. Electricity consumption is very similar between 2013 and
2014.
FIGURE 69: MONTHLY RAINWATER AND GRAYWATER SYSTEM ELECTRICITY USE
0%
5%
10%
15%
20%
25%
30%
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% o
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FIGURE 70: 2013 AND 2014 COMPARATIVE MONTHLY RAINWATER AND GRAYWATER SYSTEM ELECTRICITY USE
It should be noted that the graywater system treats water from adjoining buildings and uses
this for wider site irrigation. It was considered a “process load” during the initial design
development and not included it in the ZNE calculations. This M&V effort has included the
rainwater and graywater system electricity use in its overall ZNE calculations, since the
equipment is physically located in the library and included on the Library’s total electricity
use submeter. For this project, this does not affect the buildings ZNE status and is not an
issue. However, this could be an issue for other buildings. These types of rainwater and
wastewater recovery systems are increasingly incorporated into green buildings, and
represent a “new” source of building electricity use. Electricity use for these systems is of
interest to the sustainable building community and utility stakeholders. Temporary power
meters were installed on individual pieces of equipment during the initial M&V period of
October-December 2012. A breakdown of electricity use by component is shown in Figure
71. Note that no electricity consumption was measured on the UV filter. Sacred Heart
Schools confirmed that the UV filter is operating. The reason for the zero measured
electricity consumption for the UV filter is unclear. It is possible that the electrical feed
labeled UV filter in the electrical room was mislabeled, or that there was an instrumentation
error (although the logger appeared to be operational). The ozone generator is the largest
single use (39% of the total, although pumping is the largest overall consumer. While the
end use data varies some seasonally, the following rainwater/graywater system end use
distribution is representative of typical end use distributions and indicates the primary
electricity consuming pieces of equipment in the system.
-
50
100
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200
250
300
350
400
450
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
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FIGURE 71: RAINWATER/GRAYWATER SYSTEM AVERAGE DAILY ELECTRICITY CONSUMPTION (KWH, AND % OF
RAINWATER/GRAYWATER TOTAL)
PLUG LOADS
Plug loads and ceiling fans (both are on the same subpanel) accounted for 5,715 kWh in
2013, or 16% of the total building electricity use. Figure 72 plots monthly plug load energy
use. Consumption is relatively constant except for July through September 2014, where a
noticeable increasing trend is evident. The reason for this is unclear, and the Library has
been notified. While this will not affect the building’s ZNE performance due to the large PV
array, this is a significant increase and should be investigated and corrected if necessary.
FIGURE 72: MONTHLY PLUG LOAD AND CEILING FAN ELECTRICITY USE
Irrigation Graywater Pump, 1.20 , 21%
Rainwater Irrigation Pump, 0.07 , 1%
Graywater Pump for Domestic Uses,
0.05 , 1%
Ozone Generator, 2.24 , 39%
Pump P2, 2.21 , 38%
UV Filter, - , 0%
-
200
400
600
800
1,000
1,200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh 2013
2014
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BUILDING ENERGY MODEL EVALUATION AND CALIBRATION
Building energy modeling is a topic of significant interest in the ZNE community. Energy
modeling is a very powerful tool that has a critical role in optimizing ZNE building efficiency.
Energy modeling is also the primary tool for establishing annual energy budgets for
renewable energy system sizing. In many cases, failure of a building to achieve ZNE can be
traced to back to some type of “failure” in the building energy modeling—i.e., an inaccurate
estimate of actual building energy use which led to insufficient sizing of the onsite energy
system(s), or insufficient margin was provided to account for the normal weather-related
variations, uncertainty in occupant behavior and plug load use, and related factors.
Overestimating building energy use is not as consequential but can lead to oversizing the
onsite energy system and increase project costs. Finally, building energy modeling can be a
useful tool for helping maintain a building’s ZNE status throughout its operations—an
important but often overlooked use of building energy modeling. This section explores a
range of building energy modeling issues with the intent of providing useful information to
help design teams, building owners/operators, and other stakeholders improve the accuracy
and utility of building energy modeling as a tool for achieving and maintaining ZNE
buildings.
CONTEXT
It is useful to note that “building energy modeling” is used and applied in different ways
depending on what one is trying to accomplish. There are a five primary uses for building
energy modeling: (1) optimizing building energy efficiency (ideally used during initial
building design in an integrated design environment), (2) documenting compliance with
energy codes and standards (typically performed near the end of the building design to
“document” as-designed performance per code/standard methodologies), (3) accurately
projecting actual building energy use for sizing ZNE renewable energy systems, for energy
saving performance contracts and related guaranteed savings projects, (4) verifying energy
savings, and (5) supporting a variety of building operational activities such as load
forecasting and automated fault detection and diagnostics. Each use has a unique set of
practitioners, goals, and established approaches to building energy modeling. Traditionally
there has been limited cross-over between each of these different building energy modeling
domains, their practitioners, their targeted building lifecycle phase. Each domain requires a
niche expertise, and involves different stakeholders, customers, team-members and
building phases. The rise of ZNE buildings creates very interesting cross-over opportunities
between the different energy modeling domains. The building energy model now becomes a
critical tool for (1) optimizing building energy performance in the early design phase, (2)
documenting compliance, (3) accurately projecting actual building performance during
operations to size the onsite renewable system and meet ZNE performance requirements
(i.e., for achieving the Living Building Challenge), (4) potentially verifying ZNE performance
and “correcting” for atypical weather, occupancy, and other operational issues as is done for
guaranteed energy savings projects, and (5) facilitating building operations personnel to
maintain ZNE operations.
The five primary building energy modeling applications are discussed in more detail below:
1. Building energy modeling tools were originally developed to help design teams and
researchers improve building energy efficiency. Building energy modeling is a
powerful tool that can help optimize building energy efficiency, particularly if used to
inform and guide the design from the very early stages of design. Effective use of
building energy modeling throughout design is critical to ZNE buildings.
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Unfortunately, building energy modeling is often reserved for the latter stages of
design to document compliance.
2. The most common use of building energy modeling is to document compliance with
energy codes (i.e., California Title 24 Building Energy Efficiency Standards) and
voluntary sustainability programs such as LEED. There is a well-established industry
of energy modelers and engineers who specialize in compliance energy modeling,
and there are well-established protocols for how to perform this modeling (i.e.,
ASHRAE Standard 90.1 Appendix G). The intent of compliance modeling is not to
predict actual building energy use during operations but to compare the as-designed
building energy use to a hypothetical base-case building that meets code or standard
requirements. Standardized sets of assumptions are used for operating schedules,
occupancy schedules, plug loads, and related variables that are primarily outside of
the design team’s influence (yet can have significant impact on actual building
energy use). Compliance focused building energy modeling is typically used to
inform and guide most ZNE projects. This presents a number of challenges to
achieving ZNE. The root problem is that compliance based energy modeling is not
intended to predict actual building performance. This leads to a variety of issues.
There is typical no feedback to modelers and design teams as to how well the design
phase compliance model predictions match actual building energy use. As long as
code compliance is shown and LEED points are achieved, there are typically no
consequences for energy models that do not accurately predict performance. There is
little incentive for modelers to spend significant time understanding and refining
input assumptions for non-regulated plug loads, occupancy schedules and related
parameters which can significantly impact actual building energy performance. Many
design teams do not utilize building energy modeling to inform and optimize building
design early in the design process, but perform energy modeling on the back-end
after substantial design is completed done to check compliance and determine the
number of LEED points.
3. At the other end of the spectrum, Energy Service Companies (ESCOs) use building
energy modeling to develop energy saving performance contracts, guaranteed
savings projects, and demand side management projects. They have almost none of
the protocol constraints that compliance modelers have and are solely focused on
accurately projecting building26 performance. There are direct financial consequences
for models that do not accurately project performance. This type of modeling, and
the understanding of actual building operations, typical and expected load profiles,
and related “intuition” is important to guide ZNE building sizing. These are skills that
not all compliance energy modelers have, and is an issue that many design teams
may not even be fully aware of.
4. Energy modeling is sometimes used by ESCOs and others to document and verify
energy savings for energy saving performance contracts. This involves detailed
weather corrections, occupancy corrections, and operational adjustments and is
typically guided by the International Performance Monitoring and Verification
Protocol. It should be noted that this is typically a very different modeling process
and effort than that used by ESCOs to develop the energy saving projects on the
front end. This type of modeling, and the skills and expertise to apply a very
different set of protocols would be useful to determine why a building is not meeting
its ZNE targets. Specifically, to identify whether this is weather-related, due to
26 typically existing buildings
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changes in occupancy or occupant use that could have impacted energy use
intensities from design assumptions, or incorrect design assumptions. Ability to
perform this type of energy modeling in a contentious (potentially litigious)
environment will likely be increasingly important as more ZNE buildings are
constructed and (presumably) problems are encountered attaining actual ZNE
operation.
5. Building energy models are being increasingly used to support ongoing building
energy management efforts, and for load forecasting. The initial calibrated model
described below was used to inform this project’s M&V efforts. This manual process
is labor intensive, and needs to be automated and incorporated into BMS and/or
building dashboard systems to provide real-time and actionable data to inform
facility management personnel how to maintain ZNE operations over the long term.
A very interesting range of automated fault detection, and related monitoring based
(or continuous) commissioning programs are nascent but gaining attention. These
types of systems will be very valuable for ZNE building operators and can use
building energy modeling to inform systems on expected energy use given actual
weather and other parameters.
PURPOSE AND GOALS
The original purpose of this energy modeling evaluation was to calibrate the energy model
at the end of the initial M&V phase (which obtained only a few months of data) and use the
updated model to project whether the Library would attain ZNE over a year period.
Secondary goals of this energy model calibration effort are to understand how well the
original design phase compliance building energy model predicts actual energy use, explore
how simple design phase model tuning efforts (i.e., updating the model to reflect as-built
design conditions) can improve model predictions, explore the effectiveness of using the
energy model to support ongoing M&V efforts to keep the building on track to achieving ZNE
over the long term, and generally explore how to improve the effectiveness of building
energy modeling efforts to support ZNE efforts across the building life cycle.
The specific goals of this building energy model calibration include:
1. Determine how well the original design phase building energy model matches as-
built conditions;
2. Determine how well the original design phase building energy model energy
projections match actual building performance;
3. Document how well calibrating the building energy model to reflect as-built
conditions matches actual performance data when using standard climatic data;
4. Document how well calibrating the building energy model to reflect as-built
conditions and actual schedules, occupancy/use data, HVAC performance and related
information obtained from M&V efforts improves model performance using standard
climatic data;
5. Compare the calibrated building energy model energy consumption projections on an
on-going basis with actual observed system performance to help inform both overall
energy performance analysis and inform investigation of systems performance
anomalies that may be driving deviations from ZNE goals;
6. Document the effectiveness of using the calibrated energy model as a tool for
maintaining ZNE during operations; and
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7. Document any lessons learned and recommendations to improve building energy
modeling to support ZNE
APPROACH
The following approach is used for the building energy model calibration efforts.
1. First, the design phase building energy model was obtained, reviewed, and its
projections compared to actual data.
2. The design phase building energy model was updated to reflect as-built conditions.
The model was re-run and the energy use projections were compared to the original
design model projections and actual energy consumption.
3. The building energy model was then further calibrated using initial M&V data, the
occupant survey and detailed BMS review to update schedules and other operational
related items. The model was rerun using standard climatic data. Energy use
projections were then compared to initial M&V period energy use. The model
projections were then used to aid ongoing M&V efforts to help identify whether
performance is on track and to identify issues that need further investigation (i.e.,
are there operational problems, equipment problems, or weather variances form
climatic averages).
4. At the end of the M&V period, the model was recalibrated again using the additional
insights from the entire M&V period data.
WEATHER
All energy models used for this project and described below use the California Climate Zone
4 data used in the initial design model. The primary reason for doing so is that we are trying
to compare how the original design phase energy model projections compare to “as-built”
model energy projections to calibrated model projections.
During the initial M&V plan development, the team also considered whether to calibrate the
model using historical weather data for the M&V Period. Calibrating energy models to actual
climatic data can be very valuable. However, obtaining and validating actual historical
weather data for a specific site can be a significant challenge and expense for many
projects. Cadmus investigated different historical weather sources. For this project, the
Atherton weather station does not have all of the sensors required to develop a customized
weather file by the building energy modeling software (eQuest). Furthermore, significant
QA/QC efforts would be required to clean and review the data, fill in missing data, and
format the data into the appropriate file format. Another source of weather data is the US
Department of Energy (DOE). The DOE collects real time weather data and makes this
available on its EnergyPlus.gov website27 for download. This information typically includes
dry bulb temperature, dew point temperature, wind speed/direction, atmospheric pressure,
visibility, cloud conditions, and precipitation type. Unfortunately, the data is provided “as is”
from its various sources and has not undergone quality control review, validation, filling in
missing data and related quality control review. This data typically has gaps that users fill
by extrapolation and time stamp data needs to be reviewed and converted from GMT to
local standard time if needed. This is a significant effort and beyond the scope of most
design team and building operator expertise and budget to do. The most promising source
27 http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_download.cfm
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for weather data reviewed was from Weather Analytics28, which, for a fee, can provide
historical weather files in standard formats for use in energy models. Their data processing
algorithms include a range of quality control checks and the weather files have been found
to be clean, useable, and accurate on past projects. They provide “synthetic” weather files
for any location using weather forecasting models and all relevant nearby historic weather
sources (e.g., weather stations, weather buoys, etc.) as boundary conditions for the
weather forecast models. Cadmus has reviewed their data files for other projects and found
them to be accurate and suitable for most building energy modeling needs. There are other
companies that provide similar weather data as well29.
Given the challenges and costs most design teams or building managers would incur
obtaining and processing historical weather data, Cadmus decided not to calibrate the
building energy model to historical data, but to rather use a calibration/evaluation process
that has greater potential to be used by design teams and building operators to extend the
usefulness of existing compliance energy models to support ZNE efforts. The calibration
process included correcting the building energy model to reflect as-built conditions; updated
schedules and occupancy data based on an occupant survey; and a detailed review of
system performance with respect to actual performance data, BMS trend logs, and weather
data.
DESIGN BUILDING ENERGY MODEL
The design team used eQuest building energy simulation software to estimate Library
electricity use for LEED documentation and establish annual energy budgets for the PV array
sizing. As noted earlier, the design team initially specified two Coolerado HVAC units with
gas duct furnaces and evaporative cooling to serve the main library reading room. Table 9
compares 2013 actual energy use to the design building energy model. The design building
energy model has the gas heated HVAC units in the main reading room and projects an
annual energy use of 56,439 kwh/year and an annual energy use index (EUI) of 30.6
kBTU/ft2/year. This 30.6 kBTU/ft2 EUI is on the upper end of what the PG&E ZNE Roadmap
Report30 considers “ZNE Capable31”. The 40 kWDC rated PV system was sized to meet this
load, assuming the collectors were installed at a 30o tilt facing south, with a predicted
generation of 58,032 kWh/year. This is just sufficient to make the building ZNE with a 1,593
kWh/year (2.8%) margin. This is a very thin margin given the typical uncertainties in
building energy modeling and factors outside the design team’s control including actual
scheduling and occupancy, plug loads, weather and other factors that could easily drive
energy use higher by more than 2.8% or PV production down by more than 2.8%. In other
words, the original PV size has almost no margin and puts the project at high risk of not
meeting ZNE targets should anything deviate from projections. Note that the design phase
model used the California climate zone 4 weather file.
28 www.weatheranalytics.com 29 i.e., Meteo Group, www.meteogroup.com 30 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in
California.” December 2012.
http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20
Report.pdf 31 i.e., the efficiency level at which current ZNE buildings are performing.
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FIGURE 73: DESIGN ENERGY MODEL END USE BREAKOUT
TABLE 9: COMPARISON OF 2013 ACTUAL ENERGY USE AND DESIGN ENERGY MODEL
AS-BUILT BUILDING ENERGY MODEL
During design, the PV array was changed from a tilted racked installation to being installed
flat on the roof (primarily for aesthetic considerations). This reduced projected output to
50,263 kWh/year—a 13% reduction and insufficient to meet the original building’s
Cooling, 1,730 , 3%
Heating, 20,929 ,
37%
Ventilation Fans,
2,540 , 5%
Lighting, 10,800 ,
19%
DHW, 9,800 ,
17%
Plug Loads,
10,640 , 19%
2013 Actual Design Model
Cooling 3,290 1,730
Heating 10,351 20,929
Ventilation Fans 7,729 2,540
Lighting 5,211 10,800
DHW 80 9,800
Plug Loads 5,715 10,640
Rainwater/ Graywater 2,750 in plug loads
Total 35,126 56,439
119,850 192,571
19.0 30.6
kBTU/yr
kBTU/SF
kWh
/ Y
ear
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PG&E’s Emerging Technologies Program ET13PGE1021
estimated load using natural gas heating. In order to meet the ZNE design goals and
increase the margin of safety, the Coolerado units with gas heating were replaced with a
similar unit with electric heat pump heating and ancillary electric duct heaters for backup.
The LEED energy model was not updated to reflect this change at the time of M&V startup.
Note that the estimated PV generation of 50,263 kWh/year is lower than actual 2013 PV
generation of 53,939 kWh by3,676 kWh or 7.3%. The updated PV generation estimate is
quite close to actual 2013 generation.
Cadmus updated the original LEED energy model to reflect as-built conditions. Specifically,
the main library reading room HVAC unit was updated from natural gas heating to a heat
pump. The model was reviewed to identify any other significant changes required to align
with the as-built design, but no major changes were identified. Note that at this point the
model was not reviewed in detail to fine-tune specific system details or operational data.
The as-built model update used the California climate zone 4 weather file. The purpose of
this effort was to determine how the design team’s model would predict building total
building energy use once the primary as-built changes were made. The HVAC change
resulted in a 19% reduction in energy use between the original energy model and the
revised energy model. The revised energy model estimates annual electricity consumption
at 45,720 kWh, and an estimated 24.8 kBTU/ft2 EUI. The as-built PV system would
theoretically provide enough electricity to make the library ZNE in this scenario. This is
10,597 kWh, or 30% higher than 2013 actual building energy use of 35,123 kWh.
FIGURE 74: AS-BUILT ENERGY MODEL END USE BREAKOUT
Cooling, 1,730 , 4%
Heating, 10,210 ,
22%
Ventilation Fans,
2,540 , 6%
Lighting, 10,800 ,
24%
DHW, 9,800 , 21%
Plug Loads, 10,640 ,
23%
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TABLE 10: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN AND AS-BUILT ENERGY MODELS
INITIAL CALIBRATED BUILDING ENERGY MODEL
The as-built energy model was reviewed and calibrated to match operational data obtained
during the initial M&V period. The primary reason for calibrating the model was to estimate
total annual Library energy use to determine if the building would likely meet its annual ZNE
goals, because at the end of the first M&V period, it was not determined whether the M&V
period would be extended for a full year. The initial calibrated energy model predicted that
the Library would meet its ZNE targets, as discussed below. Additionally, once the second
phase M&V contract was approved, the initial calibrated energy model results were used to
compare against ongoing monthly energy use. This proved to be a very useful tool to
determine if ongoing monthly energy use was in line with modeled expectations, with
deviations flagging the need for more detailed investigation to determine what was going on
(e.g., weather differences from climatic averages, equipment or operational issues that
required further review, model calibration issues).
Data for November 2012 was used to calibrate the model because this was the only full
month of typical data obtained in this period32. The following calibration steps were taken:
(1) The model was reviewed in detail to ensure that systems and subsystems were being
correctly modeled and parameters appropriately selected by Cadmus’s energy modeling
staff. (2) Building operation and occupancy schedules were reviewed against data obtained
from the “Occupancy Survey”, and the school schedule (see Appendix C). (3) Individual
system operation and power consumption data were reviewed against BMS trend logs and
data logger data. Note that correct lighting data was used for this model calibration (actual
logged data from the temporary data loggers installed on the lighting subpanel was used).
(4) November 2012 weather was reviewed and found to be very similar to the November
California Climate Zone 4 data, with no significant weather-related changes expected for
November 2012 data. Refer to the discussion below and Table 11 for more information. The
32 October data was available but was atypical due to start up and BMS programming issues,
and the initial M&V period ended in the middle of December.
2013 Actual Design Model
As-Built
Model
Cooling 3,290 1,730 1,730
Heating 10,351 20,929 10,210
Ventilation Fans 7,729 2,540 2,540
Lighting 5,211 10,800 10,800
DHW 80 9,800 9,800
Plug Loads 5,715 10,640 10,640
Rainwater/ Graywater 2,750 in plug loads in plug loads
Total 35,126 56,439 45,720
119,850 192,571 155,997
19.0 30.6 24.8
kBTU/yr
kBTU/SF
kWh
/ Y
ear
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initial design phase model calibration used the California Climate Zone 4 weather file, per
the rationale discussed above under “Weather”.
The initial design phase calibration revisions made to the “as-built” model include:
The night cycle controls for the main reading room HVAC unit (EC1) were modified to
reflect observed system performance. The “as-built” model showed significant after-
hour energy consumption for EC1. However, these units are currently disabled after
hours through the BMS system and the metered data showed these units did not
cycle on during the evenings/weekends. Modeled controls were changed to “stay off”
from allowed to cycle on/off after hours.
The fan operating schedules for HVAC equipment were fine tuned to reflect actual
weekday operational hours.
The domestic hot water specifications were updated from electric hot water storage
tanks to electric instantaneous (point-of-use) tankless water heaters. This reduces
storage tank-related standby losses.
Lighting, plug load and domestic hot water Day Schedules were updated to reflect
actual schedules observed from BMS trend logs and metered data. The original
model’s schedules for these end uses did not correlate well to metered data.
November 2012 weather data was reviewed against the California Climate Zone 4
November climatic data used in the model. Summary data is shown in Table 11.
There is a close match between November 2012 and climatic averages, with key
temperature parameters within 5 oF of each other. 2013 heating degree days are
53% higher than the climate file heating degree days. However, the total number of
heating degree days is very small compared to total annual heating degree days.
Due to the generally close match between actual and climate file key variables, it is
expected that November 2012 energy consumption data should match the modeled
November relatively data well, although we do anticipate that the model would show
slightly higher heating energy than actual data based on the HDD data.
TABLE 11: COMPARISON OF ACTUAL ATHERTON TO CACZ04 TEMPERATURES FOR NOVEMBER 2012
CACZ04 Actual Difference % Dif.
Monthly High Temp 78 75 (3) -5%
Average Daily High Temp 66 62 (4) -7%
Average Daily Temp 53 53 0 0%
Average Daily Low Temp 42 47 5 10%
Monthly Low Temp 32 36 4 11%
Average Dew Point Temperature (F) 41 44.8 3.8 8%
Average Relative Humidity 68 77.3 9.3 12%
HDD 185 283 98 53%
CDD 0 5 5 n/a
The initial calibration changes discussed above resulted in the energy model predicting
November building energy to within 8% of November 2012 performance data. Table 12
compares actual November 2012 library energy end use energy consumption to the initial
calibrated energy model results. HVAC energy use is very close, with modeled energy use
slightly lower than actual by 2%. This is expected because November 2012 has more
heating degree days than the climatic data used in the model. Since the HVAC equipment is
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off during the night when it is coldest outside, we do not anticipate significant increases in
heating energy, and this is reflected in the data. Modeled lighting energy is within 15% of
measured energy. We deemed this to be sufficiently close given expected variations in
occupant schedules, daylighting controls, and occupancy sensor controls that are hard to
capture perfectly in the model (i.e., a 15% variation in actual vs. estimated lighting use is in
line with expected variance between modeled and actual data). DHW energy consumption is
very small, with only 0.5 kWh/month difference (note that the small consumption values
result in a relatively large (33%) variation between modeled and actual energy use.
Modeled plug loads are within 8% of actual data. Note that the design and as-built models
lumped the rainwater/graywater energy use with plug-loads. To be consistent with past
models, the rainwater/graywater energy use is aggregated in the plug loads as well.
TABLE 12: INITIAL CALIBRATED ENERGY MODEL PROJECTIONS COMPARED TO NOVEMBER 2012 M&V DATA
November 2012
Measured Energy (kWh)
November Initial Calibrated Energy
Model Projections (kWh)
Difference Between Measured and Modeled Data
Total Building Consumption 2,082 1870 8%
HVAC 864 850 2%
Lighting 720 610 15%
DHW 2 2.5 33%
Plug Loads 447 410 8%
Overall, Cadmus deemed the initial calibrated model to sufficiently represent actual building
energy use (based on the limited M&V data available) to predict annual energy consumption
for the initial purpose of determining whether the building would meet its design ZNE goals.
Figure 75 shows the initial calibrated model total energy projections for 2013. Table 13
shows the same data in tabular form. The initial calibrated model predicts an annual energy
use of 23,010 kWh and an EUI of 12.5 kBTU/ft2/year. This is lower than the projected
annual PV system generation of 50,263 kWh. Thus, the initial calibrated model predicts that
the Library would meet its ZNE targets based on climatic data, per the original design
intent.
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FIGURE 75: INITIAL CALIBRATED ENERGY MODEL END USE BREAKOUT FOR 2013
TABLE 13: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN, AS-BUILT AND INITIAL CALIBRATED ENERGY MODELS
Table 13 also compares the initial calibrated model projections to actual 2013 energy use,
the design phase energy model projections, and the as-built energy model projections. Note
that the design phase model’s 620 Therms of natural gas heating energy has been
converted to kWh-equivalent in the table to facilitate easy comparison between model
updates.
The initial calibrated energy model predicts nearly half the energy use of the as-built energy
model. The largest change between the as-built model and the initial calibrated model
comes from DHW. Calibrating the model reduced DHW energy use by 9,770 kWh, nearly a
Cooling, 800 , 4%
Heating, 7,050 , 31%
Ventilation Fans, 2,120
, 9%
Lighting, 7,840 , 34%
DHW, 30 , 0%
Plug Loads, 5,170 , 22%
2013 Actual Design Model
As-Built
Model
Initial
Calibrated
Model
Cooling 3,290 1,730 1,730 800
Heating 10,351 20,929 10,210 7,050
Ventilation Fans 7,729 2,540 2,540 2,120
Lighting 5,211 10,800 10,800 7,840
DHW 80 9,800 9,800 30
Plug Loads 5,715 10,640 10,640 5,170
Rainwater/ Graywater 2,750 in plug loads in plug loads in plug loads
Total 35,126 56,439 45,720 23,010
119,850 192,571 155,997 78,510
19.0 30.6 24.8 12.5
kBTU/yr
kBTU/SF
kWh
/ Y
ear
Total Energy Use,
23,010 kWh
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100% reduction in DHW energy use. The DHW correction reduces total energy use by
~21%. The DHW over-estimate in the as-built model has a significant impact on total library
building consumption. The DHW savings are primarily due to significantly less DHW
consumption observed during the initial M&V period compared to the initial design phase
projections. Note that the as-built model also specified tank-type water heaters whereas the
initial calibrated energy model corrected this to tankless water heaters (eliminating standby
losses) per actual design. The next largest calibration savings is from plug loads. In the
design and as-built models, estimated plug loads are 50% higher than plug loads measured
in the initial M&V period. The plug load over-estimate of 5,470 kWh/year represents 12% of
the 2013 measured total building energy consumption. The next most significant change
between the as-built and initial calibrated model is changes to heating and lighting energy,
both of which are over-estimated by ~30% and result in an excess energy projection of
approximately 3,000 kWh, the equivalent of 7% of the total building energy.
The initial calibrated model predicts 35% lower total energy (12,113 kWh) than 2013
measured building energy use of 35,126 kWh. This is expected because 2013 has more
heating degree days and cooling degree days than the California Climate Zone 4 weather
file used by the model. Refer to Figure 26 and Figure 27 for a comparison of 2013 actual
and climate file heating and cooling degree days.
Refer to the following “Final Calibrated Building Energy Model” section for discussion on how
the initial and final calibrated models compare to one another.
During the course of the final M&V period, the monthly library energy use was compared to
the initial calibrated energy model’s monthly projections, as shown in Figure 76. This was a
very useful diagnostic tool to track whether actual energy use was in line with expected
(modeled) data. Consumption that significantly differed from modeled data triggered the
M&V team to review performance and identify causes so they can be rectified in a timely
manner to help keep the library on track to ZNE. One of the immediate observations was
that the initial model was significantly under-estimating winter heating energy consumption.
The higher than projected energy use triggered a deeper review of heating system
performance, to identify potential operational or equipment problems. No apparent
problems were identified and the systems appeared to be functioning as intended. The team
then reviewed the model for potential modeling errors. It was initially believed that the
model was under-estimating heating energy due to the very limited heating data (1 month’s
worth of data in November 2012 which had limited heating system use) used to calibrate
the model with. A deeper review of the climatic data showed that this was exacerbated by
the fact that the actual 2013 and 2014 weather had significantly more heating degree days
than the California Climate Zone 4 data used by the model. Summer and cooling season
energy use matched modeled energy use well. Another issue observed during the final M&V
period was that July, August and September 2014 energy use was significantly higher than
projected energy use, and that July and August 2014 energy use was significantly higher
than in 2013. Comparison of 2013 to 2014 weather showed that July and August 2014 had
significantly more cooling degree days (i.e., hotter) than 2013, which helps account for the
July and August 2013 vs. 2014 discrepancy. Cooling degree days were similar in 2013 and
2014, and August monthly energy use is similar between years. All three months in both
2013 and 2014 had more cooling degree days than the California Climate Zone 4 climatic
data used by the model, which helps explain the difference. A final factor that was found
when exploring the discrepancies is that the plug load energy for July, August and
September 2014 showed a significant rise over past months (see Figure 72). This issue is
flagged to school personnel to investigate possible causes of this increased plug load and
address as necessary. It should also be noted that the rainwater/graywater system energy
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PG&E’s Emerging Technologies Program ET13PGE1021
use turned out to be highly seasonal (refer to Figure 69) and was significantly higher on
average than the November 2012 data used for the initial model calibration.
FIGURE 76: COMPARISON OF MEASURED ENERGY VS. INITIAL CALIBRATED ENERGY MODEL PROJECTIONS
FINAL CALIBRATED BUILDING ENERGY MODEL
The initial calibrated energy model was recalibrated again at the end of the final M&V
period. The same process used to calibrate the initial calibrated model was used, with the
exception that performance data from the entire M&V performance period was used to
inform the calibration. The following calibration steps were taken: (1) The model was
reviewed again in detail to ensure that subsystems were being correctly modeled and
parameters appropriately selected by Cadmus’s energy modeling staff. (2) Building
operation and occupancy schedules were reviewed against data obtained from the
“Occupancy Survey”, the school schedule (see Appendix C), BMS data, and data logger data
and updated as needed. (3) Individual system operation and power consumption data was
reviewed against BMS trend logs, data logger data, and weather data. System level
adjustments were made, as appropriate, to better reflect actual system performance. Note
that the corrected lighting data was used in the final calibration. (4) Actual weather was
compared to California Climate Zone 4 climatic data used by the model to help inform model
calibration and understand where and why actual energy use data would be expected to
vary from modeled data using the climatic average weather file.
The following calibration changes were made for the final calibration:
EC1 RTU (the main system supplying the library reading room):
o The system type for the main reading room AHU was changed from a variable
volume variable temperature system to a single zone system with
supplemental electric heat. This change was needed to more accurately
capture the supplemental duct heater energy use. Note that one of the
constraints and limitations of the building energy modeling software used
-
1,000
2,000
3,000
4,000
5,000
6,000
kWh
Measured Energy Initial Calibrated Model
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PG&E’s Emerging Technologies Program ET13PGE1021
(eQuest) is the ability to precisely model the exact HVAC system performance
characteristics. This is likely to be an ongoing issue as designers continue to
increasingly incorporate advanced and innovative HVAC system that do not
quite fit into standard HVAC system models. Note that this has always been
an issue with energy models, and that energy modelers have often had to use
creative work-arounds to model actual system performance within modeling
constraints. Note that different energy modeling tools have different
capabilities to fine tune HVAC system components, controls, and building
characteristics. A detailed discussion of energy modeling tools is beyond the
scope of this paper, but it is worth noting that generally speaking, energy
models capable of modeling more complex systems and controls (i.e.,
energy+) typically have a higher level of modeling expertise required and
typically have higher modeling costs. There are many tradeoffs in selecting
the most appropriate modeling tool.
o The outside air supply rate was adjusted to match the mechanical schedule.
o The heating COP was updated from 2.7 to 3.9 to match the schedule.
o The cooling efficiency was updated from SEER 15 to SEER 16.5 to match the
schedule.
o The fan power was adjusted from 0.00583 to 0.003 kW/CFM to match the
schedule.
o The nighttime setback was change from “stay off” to “cycle on as needed” to
reflect operational practice. Note that this reverses the initial model
calibration. Upon review of additional data with larger heating and cooling
loads the units were observed to cycle on as needed.
o The heating setpoint and fan schedules were adjusted to better reflect
February and December school breaks.
o The occupied heating setpoint was adjusted from 70oF to 68oF to better align
with the average zonal setpoints obtained from the M&V data.
Rainwater/graywater system
o Rainwater/graywater system energy was included in plug loads in earlier
models. The rainwater/graywater equipment is in a separate mechanical room
outside the occupied zone thermal boundary. These loads were broken out as
process loads outside of the building envelope.
o Note that the initial calibrated model used November 2012
rainwater/graywater electricity use and assumed this was relatively constant
throughout the year. As shown in Figure 69, rainwater/graywater electricity
use turned out to vary significantly throughout the year and the initial
calibrated model significantly under-estimated this load.
Figure 77 shows a breakout of the final calibrated model annual energy end use. The final
calibrated model projects a total annual energy use of 33,265 kWh, and an EUI of 18.0
kBTU/ft2/year. This is very close to 2013’s actual measured EUI of 19 kBTU/ft2/year.
Heating is the largest energy end use, followed by lighting, plug loads, ventilation, the
rainwater/ graywater system, and cooling.
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FIGURE 77:PLUT CALIBRATED ENERGY MODEL END USE BREAKOUT
Figure 78 compares 2013 and 2014 measured total building energy use against the final
calibrated model projections. The modeled data compares well to actual data, and the
observed variation are as anticipated from reviewing the climatic data. With respect to
heating, there are more heating degree days in 2013 than in and 2014, and the California
Climate Zone 4 file used by the model has the least (refer to Table 7 for HDD data). The
California Climate Zone 4 file has fewer cooling degree days than 2013 and 2014 (refer to
Table 8). We would expect that actual energy would be higher in the summer than modeled
energy. It is generally the opposite. This is explained by the fact that the energy model is
not able to model the evaporative cooling systems well and is not accounting for this energy
savings. There is a significant spike in June and July 2014 cooling degree days. This is
reflected in the 2014 energy use.
Cooling, 1,364 , 4%
Heating, 10,774 ,
33%
Ventilation Fans, 5,158
, 16%
Lighting, 7,403 , 22%
DHW, 82 , 0%
Plug Loads, 5,735 , 17%
Rainwater/ Graywater, 2,750 , 8%
Total Energy Use, 33,265 kWh
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 78: COMPARISON OF 2013 AND 2014 MEASURED BUILDING ENERGY VS. FINAL CALIBRATED ENERGY MODEL
Table 13 compares 2013 actual energy use to the design, as-built, initial and final calibrated
models. The final calibrated model generally tracks all end uses very well. Lighting power in
the final calibrated model is higher than actual data. We believe this is primarily due to the
fact that the model is not able to capture to effectiveness of the occupancy and daylighting
control throughout the library.
0
1,000
2,000
3,000
4,000
5,000
6,000
1 2 3 4 5 6 7 8 9 10 11 12
kWh
Month
Calibrated Model
2013 Actual
2014 Actual
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TABLE 14: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN, AS-BUILT AND INITIAL CALIBRATED ENERGY MODELS
TABLE 15: COMPARISON OF MEASURED VS. UPDATED ENERGY MODEL TOTAL BUILDING ENERGY CONSUMPTION (KWH)
MONTH FINAL CALIBRATED MODEL 2013 ACTUAL 2014 ACTUAL
Jan 4,395 5,419 4,713
Feb 3,169 5,274 3,703
Mar 3,088 3,115 2,590
Apr 2,389 1,996 1,948
May 2,397 2,244 1,891
Jun 2,099 1,675 1,850
Jul 2,271 1,648 2,530
Aug 2,190 1,800 2,647
Sep 2,180 2,318 2,713
Oct 2,381 2,401 n/a
Nov 2,692 2,774 n/a
Dec 4,014 4,458 n/a
Total 33,265 35,123 n/a
Variance between
measured and modeled annual total
-5% n/a
2013 Actual Design Model
As-Built
Model
Initial
Calibrated
Model
Final
Calibrated
Model
Cooling 3,290 1,730 1,730 800 1,364
Heating 10,351 20,929 10,210 7,050 10,774
Ventilation Fans 7,729 2,540 2,540 2,120 5,158
Lighting 5,211 10,800 10,800 7,840 7,403
DHW 80 9,800 9,800 30 82
Plug Loads 5,715 10,640 10,640 5,170 5,735
Rainwater/ Graywater 2,750 in plug loads in plug loads in plug loads 2,750
Total 35,126 56,439 45,720 23,010 33,265
119,850 192,571 155,997 78,510 113,500
19.0 30.6 24.8 12.5 18.0
- 61% 30% -34% -5%
6% 70% 37% -31% 0%
Difference Between Final
Calibrated Model
kBTU/yr
kBTU/SF
kWh
/ Y
ear
Difference Between Actual
& Modeled
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PG&E’s Emerging Technologies Program ET13PGE1021
DATA VALIDATION AND QUALITY CONTROL Data validation and quality control (QC) is a critical element of M&V. Cadmus’s experience
on similar projects indicates that building M&V equipment, including non-revenue grade
submeters are typically not subject to the same rigorous commissioning review that other
systems are, and are often incorrectly installed, calibrated, and/or providing inaccurate
information to the owner. The M&V plan outlined a robust set of data validation and quality
control processes which were followed throughout the course of the M&V. A number of
significant issues were discovered and are reported below.
DATA VALIDATION FOR THE TEMPORARY DATALOGGERS
Cadmus’s data validation and quality controls began with the temporary loggers installed for
the M&V process. During the initial site visit, Cadmus metering personnel inspected the
electrical panel locations, access, and confirmed circuit configuration (i.e., voltage, number
of phases) for each circuit to be measured. Cadmus then programmed the data loggers at
the office, selected and cross-checked the appropriate CT size, and tested the CT/meter
configuration for proper operation and data logging at the office. Meters and CTs came pre-
calibrated from the factory, and Cadmus metering personnel performed spot checks to
confirm proper readings. Specially trained Cadmus metering specialists deployed the meters
to the site. Meter operation/logging status was confirmed at deployment. The data loggers
were downloaded approximately two weeks later. Data was reviewed for consistency,
compared against spot check measurements taken at the site and expected readings based
on equipment schedules, and cross-checked against BMS electricity submeter data and
other BMS data to ensure reasonableness of all systems and to spot any issues.
At the onset of the second phase of M&V (September 2013), Cadmus replaced the Dent
data loggers with HOBO dataloggers and cellular communications cards that would allow
ongoing data collection without the need to access the interior of the facility’s electrical
panels on an ongoing basis and providing improved ability to monitor building end uses on a
real time basis. Note that due to physical space constraints in the electrical cabinet, the
temporary logger could not be installed for the lighting subpanel (LCP B-H1)33. Cadmus’
metering team reviewed meter calibrations, proper CT/meter configurations, programming,
logging and communication both prior to installation and at the time of installation. After the
first month’s data collection, the Hobo data was cross-checked against the DENT data logger
data to identify any issues and inconsistencies. No issues were found with the Hobo loggers
and data aligned with data obtained by the DENT loggers.
33 Note that the original DENT loggers were left in place at the end of the first M&V period
(12/2012) with the intent that they would continue to log data in case the contract was
extended. By the time the contract for the 2nd phase was executed and Cadmus downloaded
the loggers (9/2013) some of the data logger batteries had died and data was lost for 2013,
including panel B-H1’s logger. This was not realized until after the new loggers were
installed. Cadmus was unable to install a replacement Hobo logger for B-H1 due to physical
space constraints and the expectation that this panel was already being logged by the BMS,
unaware that the CT had not been corrected. See discussion in following section for details.
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PG&E’s Emerging Technologies Program ET13PGE1021
DATA VALIDATION FOR THE BMS ELECTRIC SUBMETERS (E-MON D-MON
SYSTEM)
During the installation of the temporary data loggers, Cadmus inspected and verified the
installation of the E-Mon D-Mon electric submetering system that provides electric power
and consumption data to the BMS. The E-Mon D-Mon system consists of individual current
transducers (CTs) connected to six submetering units. These in turn communicate data to
the BMS. The CT placement was inspected and is summarized in the following table. The
panel labeling is shown in the following picture.
TABLE 16: E-MON D-MON SYSTEM CT PLACEMENT
E-Mon D-Mon
Meter ID Loads Measured
Panel/ Circuit(s) Measured
Notes
B-L2 Entire Panel B-L2, primarily plug load
circuits and ceiling
fans
Panel BL-2 feed.
B-L1
(1)
Not logging
meaningful data
CT originally
installed on EF-1 but currently disconnected
The E-Mon D-Mon CT for the meter marked "B-L1 (1)"
was installed on the exhaust fan ( EF-1, circuit 9). A 200 amp CT was installed, but the E-Mon D-Mon was configured for a 400 amps CT. This mismatch between expected and actual CT amperage results in meter reading failure. Furthermore, spot measurements of the exhaust fan circuit showed a power of 1 Amp (not expected to vary). This small current is too small for
the 200 Amp CT. Per recommendation from Mark Roark, controls contractor, the CT for this sensor was disconnected to prevent inadvertent mis-use of this data, and the bathroom exhaust fan was added to the
circuits being measured by E-Mon D-Mon meter labeled "B-L1(2)".
B-L1 (2)
Speakman unit, EF-1, split system heat-pumps, east duct heater, panel
B-L2 feed
Panel B-L1 circuits: 1,3,5,7,11,13,15,17,19,21,2
3, 25, 37,39,41
Multiple circuit wires are bundled onto individual CT's by phase.
B-L1
(3)
Instantaneous
Water Heaters
2, 4, 6, 8, 10,
12
Multiple circuit wires are bundled onto individual CT's
by phase.
B-L1 (4)
Rainwater & graywater
equipment
14,16,18,20,24,26,28,29,31
Multiple circuit wires are bundled onto individual CT's by phase.
Panel B-H1
Entire panel B-H1, lighting
panel B-H1 feed
A 200 amp CT is installed but the E-Mon D-Mon meter is configured for a 100 Amp CT. This will cause erroneous readings. The CT needs to be changed to
100 Amps. Temporary data logging equipment is installed on this circuit to cross-check data.
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PG&E’s Emerging Technologies Program ET13PGE1021
FIGURE 79: E-MON D-MON PANEL LABELING
Three issues were identified with the E-Mon D-Mon system during the initial M&V period’s
detailed system performance analysis and reported to the facility and PG&E in the
November 2012 Monthly Report:
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PG&E’s Emerging Technologies Program ET13PGE1021
1. E-Mon D-Mon “B-H1” meter (lighting power loads)
a. A 200 Amp CT is installed but the E-Mon D-Mon meter is configured for a 100
Amp CT. E-Mon was contacted and they confirmed that this will cause
erroneous readings, as the submeter is pre-programmed with the expected
CT’s standard calibration factor. In addition to the CT/meter mismatch, the
200 amp CT is too large for the expected load.
b. Temporary data logging equipment was installed on this circuit to crosscheck
data and confirms that this submeter is off by a factor of 4.8162 (i.e., it reads
nearly 4.8162 times lower than the actual lighting load).
c. Note that this erroneous reading also causes the total building energy to be
off by approximately 13%, since it is calculated by summing the submeter
loads.
d. The CT needs to be changed to a 100 Amp CT. This issue was flagged to the
facility and design team during the initial M&V period in 2012. It was
understood that the CT issue was being corrected. However, the incorrect CT
is still in use and needs to be replaced.
e. A least-squares regression analysis was performed to develop a revised
calibration factor to correct the lighting power loads. Refer to the following
section for details.
2. Panel B-L1 meters (total of 4 meters)
a. Inspection found that the CT for the E-Mon D-Mon meter labeled “B-L1 (1)”
was installed on only the exhaust fan (EF-1, circuit 9). Spot measurements of
the exhaust fan circuit showed a power of 1 Amp (not expected to vary). This
small current is too small for the 200 Amp CT being used. Furthermore, the E-
Mon D-Mon meter is configured for a 400 Amp CT. This mismatch between
expected and actual CT amperage causes an error. The CT must be replaced
with the correct meter.
b. Per recommendation from Mark Roark, the controls contractor, the CT for this
sensor was disconnected and the bathroom exhaust fan was added to the
circuits being measured by E-Mon D-Mon channel 2 to provide meaningful
data.
c. Temporary data loggers have been installed to verify measurements of the E-
Mon D-Mon data.
d. It is recommended that the unused E-Mon D-Mon Panel B-L1 Channel 1 meter
be redeployed once the correct CTs are installed. The most logical
redeployment would be to install this on the Speakman Unit (circuits 1, 3, 5).
3. The BMS calculates total power by adding up the power from the five operating
submeters. However, this reading is inaccurate, because the lighting submeter
(Panel BH-1) has the wrong CT and reads incorrect lighting power. The building BMS
data must be corrected.
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It was the M&V team’s understanding that the lighting CT had been replaced with the
appropriate CT. However, upon cross-checking the data for the final report, it was found
that CT for the lighting power submeter (panel BH-1) has not been corrected. All lighting
power and total building power data has now been corrected with the updated calibration
factor. Refer to the next section for details. Sacred Heart Schools was notified and they
have replaced the CT with the correct CT.
BMS LIGHTING PANEL (PANEL BH-1) SUBMETER CALIBRATION CORRECTION
As discussed in the previous subsection, there was a CT mismatch on the BMS submeter the
lighting loads (panel BH-1). To make the historical BMS lighting data usable, an updated
calibration factor was developed using the temporary data loggers. To do this, the actual
lighting panel kW obtained from the temporary Dent loggers was plotted against
corresponding data from the BMS lighting submeter, shown in the Figure 80, for the month
of November 2012. The updated calibration factor is 4.8162 (i.e., multiply the BMS’s
lighting power reading by 4.8162 to get the corrected lighting power.
Note that each meter (Dent vs. E-Mon D-Mon system) has a different data recording and
aggregation interval. Prior to plotting, the data from each meter was aggregated on an
hourly basis into average hourly lighting kW. This was the smallest time increment possible
from the data, and aligns with hourly energy modeling and climatic data needs. This
aggregation does lose some of the sub-hourly detail and results in some inaccuracy in peak
demand, but provides accurate consumption (kWh) data. Also note that the CT is oversized
for the load, which introduces additional inaccuracy, particularly for lower power readings.
Estimated uncertainty in this calibration is ±10% for hourly peak demand (kW) and
consumption (kWh) results. Individual data points are relatively noisy, but over a month
period there is 1% difference between the corrected BMS data and the Dent data.
FIGURE 80: UPDATED CALIBRATION FACTOR FOR THE BMS’ LIGHTING PANEL (PANEL BH-1) SUBMETER
Figure 81 shows November 2012 lighting power for the miscalibrated BMS sensor, the
correct reading obtained from the Dent submeter, and the corrected BMS data using the
calibration factor listed above. Figure 82 shows the same data zoomed to 11/26/12 through
y = 4.8162xR² = 0.9371
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11/30/12 to better illustrate the data. The peak lighting kW data shows excellent
correlation. Note that the slight phase mismatch between peaks is due to small differences
between BMS clock and data logger clock, and the hourly data aggregation. Also note that
the peak data is more accurate than the low after hours lighting power, due to reduced
sensitivity of the CT at this very low end of the CT range.
FIGURE 81: COMPARISON OF ACTUAL (DENT), MIS-CALIBRATED (BMS) AND CORRECTED BMS LIGHTING POWER
MEASUREMENTS (11/1/12 THROUGH 12/4/12)
FIGURE 82: COMPARISON OF ACTUAL (DENT), MIS-CALIBRATED (BMS) AND CORRECTED BMS LIGHTING POWER
MEASUREMENTS (11/26/12 THROUGH 11/30/12)
BMS TOTAL KWH (STEVENS TOTAL KWH USAGE) CORRECTION
As discussed above, the BMS calculates total power by adding up the power from the
five operating submeters. However, this reading is inaccurate, because the lighting
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submeter (Panel BH-1) has the wrong CT and reads incorrect lighting power. The
building BMS data must be corrected.
Figure 83 shows the BMS’s reported total power (blue line; sum of the individual
submeters) and the total power measured by the temporary data logger installed by
the M&V team. Note the significant deviations. Review and analysis indicates that
these are due to the aforementioned lighting CT mismatch.
FIGURE 83: COMPARISON OF MEASURED TOTAL BUILDING POWER VS. BMS REPORTED BUILDING POWER
Figure 84 plots measured building power (from the temporary DENT loggers installed
by the M&V team) and the corrected BMS reported total building power (the incorrect
lighting submeter data from the BMS is replaced with lighting power measured by
temporary Dent loggers installed on lighting panel BH-1, and added to the other BMS
submeter data). The total building energy now correlates very well. Note that the
result is nearly identical to that obtained when using the BMS lighting power
correction factor discussed in the previous subsection.
There are a number of reasons why the data does not align perfectly. One factor is
that the BMS and data logger averaging and recording intervals are different, which
leads to slight differences in reported peak demand. The BMS is reporting hourly
averages, while the Dent submeters recorded 15 minute interval data, which was
post-processed into hourly averages for comparison to the BMS data). Another issue
is that the temporary loggers and the BMS submeters do not all begin recording their
data at exactly the same time. Finally, meter uncertainty results in some error. In
summary, once the lighting correction is made, there is excellent correlation between
total building power obtained from the BMS’s submeters and the temporary Dent
loggers.
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FIGURE 84: COMPARISON OF MEASURED TOTAL BUILDING POWER VS. CORRECTED BMS POWER (CORRECTED LIGHTING)
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EVALUATIONS The Sacred Heart Schools Stevens Library is a very successful ZNE project. From the
performance perspective, the building is achieving ZNE by a significant margin and is “net
positive” energy in terms of energy production. The building delivers an extremely high level
of efficiency and performance. Aside from concerns about stuffiness, occupants had minimal
concerns about the building. Aside from a few minor issues, the building has operated very
well in its initial two years. The issues that were encountered include failure of the PV
inverter communications card, and a warranty replacement/repair on the main HVAC unit
EC1.
Some very useful data, recommendations and lessons learned have been gained from the
M&V process. These are summarized in the next section and it is hoped these will be
valuable to building design teams, building operators, and others.
It should be noted that this is an easy building type in a favorable climate to achieve ZNE.
The building has a very low space use intensity and low average occupant density compared
to other commercial buildings such as offices. It is operated on an academic schedule with
more breaks (including a summer break with minimal student library use) than most
commercial facilities. It also has very low plug loads, and is in a very mild climate with
minimal heating and cooling needs.
An interesting note on the design side, with potential utility implications, is the fact that the
building likely would not have achieved ZNE (base on a ZNE site energy definition) with gas
heating, based on the available roof area that could be used for PV. The project had to
switch to an electric heat pump system to reduce its site energy use. This brings up an
interesting discussion relating to site zero net energy verses source zero net energy verses
TDV zero net energy. When looking at source zero net energy, the upstream impacts of the
electric grid generation efficiency and transmission and distribution losses must be factored
in. This would require a larger PV system. Also, fuel switching becomes less advantageous.
The benefits of switching from natural gas heating (with an efficiency of 80-95%) to an
electric heat pump (COP ~3) are reduced because the overall grid efficiency must be
accounted for. A detailed evaluation of the broader implications of ZNE definitions is beyond
the scope of this project and treated in other PG&E ZNE activities34, but we do flag this as
an issue that has relevance to PG&E.
RECOMMENDATIONS AND LESSONS LEARNED This M&V project has resulted in a number of recommendations and lessons learned that
may be useful to a variety of stakeholders. The following discussion is organized by topic
(i.e., sub-metering) followed by specific recommendations to key stakeholder groups.
34 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in
California.” December 2012.
http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20
Report.pdf
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SUB-METERING
Metering and submetering are critical to ZNE buildings. Without proper, accurate and
sufficient metering it is impossible to track building performance and manage the building to
achieve and maintain ZNE status. This is particularly critical at this facility as the building is
part of a larger master-metered campus and does not have its own utility meter.
Stevens Library is equipped with six electric submeters to monitor and manage electricity
use. There were several metering related issues that this M&V project identified which would
likely have otherwise gone unnoticed. First, two of the current transducers (CTs) in
submetering panel did not match the submeters’ requirements and resulted in incorrect
readings. The CT measuring the lighting loads was a 200 Amp CT, but the submeter was
configured to read a 100 Amp CT. This mismatch resulted in inaccurate readings. The
lighting loads are being recorded by the BMS at 4.86 times lower than actual loads. This
error is compounded because the BMS adds all of the submetered loads to get total building
energy use. The incorrect lighting panel reading resulted in total building energy being
reported as ~20% lower than actual energy use. It is possible that the error could have
gone the other way and reported higher than actual energy use. This has the potential to
jeopardize a building’s ability to demonstrate that is has achieved ZNE. This is problematic
to all parties involved, particularly if an important sustainability program performance rating
is at stake (i.e., loss of LEED energy and atmosphere credit 1 points for energy efficiency;
inability to meet the living building challenge). The M&V team flagged the CT/submeter
mismatch issue on the lighting panel during the initial M&V period and had thought that the
issue had been resolved. During the data review and QC for the final report, unusually low
lighting power densities were observed and investigated, and it was determined that the
lighting panel CT had not been changed (this has since been corrected).
Metering problems have been a common issue for several recent ZNE buildings. Metering is
a specialty field that, depending on the metering equipment involved, can require special
expertise to install and calibrate. It is recommended that all metering and submetering
equipment in ZNE and high performing buildings receive appropriate commissioning and
verification to ensure it is providing accurate data.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Historical BMS trend log data for lighting and total building energy prior to the
12/16/2014 lighting CT replacement are inaccurate. To make the historical
data useful, apply the lighting calibration multiplier. Note: this calibration
multiplier has been applied to the BMS data presented in this report.
One submeter is currently unused. It would be valuable to operations to
connect this and pick up power or current data for EC1 and the duct heaters.
The submeter readings for the other new buildings should be spot checked
with a hand-help multimeter to confirm their accuracy.
RECOMMENDATIONS TO DESIGN TEAMS
Metering and submetering is often left out of commissioning scopes and
metering problems often go unnoticed. The design team should ensure that
the commissioning scope includes commissioning for submeters. This is
particularly critical for ZNE buildings and other high performing buildings
where having accurate data is vital to achieving and maintaining performance
goals. It is not inconceivable to envision a situation where a building would
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“fail” to achieve ZNE or properly document performance due to a simple
metering issue and miss out on LEED points or miss out on achieving a rating
(i.e., the Living Building Challenge).
The design team on this project did a great job designing the electrical
circuits to facilitate easy submetering. However, many building electrical
layouts present significant challenges and preclude easy and inexpensive
submetering. Design teams should make sure to include submetering
requirements in the project and ensure that appropriate design team
personnel are aware of these requirements.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Metering and submetering issues are a common theme observed on multiple
projects. There are opportunities to encourage projects to improve the
submetering and metering process and make this data more useable and
useful for building owners to achieve and maintain ZNE or similar energy
performance goals.
BMS TREND DATA
This project had excellent BMS trend data to work with. This was invaluable to the M&V
efforts and will be extremely useful for the school to help manage and maintain its ZNE
status. Part of the success was due to the fact that the M&V consultant was able to
coordinate with the controls contractor before the BMS programming was finished, and it
was easy to set up the desired trend logs. Unfortunately, this is not always the case on
projects. More often than not the rich BMS data vital to managing ZNE and deep energy
efficiency is very difficult for building operators, M&V personnel, and others to access, and
therefore it is not used to the extent it could be.
Another issue encountered in the BMS trend data is that the PV power and energy points
were configured to log data on a change in value, rather than a fixed time increment. This
resulted in a massive amount of data (tens of thousands of records per year) that is very
difficult to utilize. Each time increment is different, so it is difficult to overlay this data with
other data (i.e., building performance data). It is possible that the very heavy
communication load placed on the inverter’s communication module to report data on such
a frequent basis may have contributed to the problems it experienced.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Reviewing the BMS trend logs on a monthly basis through the M&V program
has been very useful in identifying issues and spotting problems early and is
critical to maintaining long term ZNE status.
Explore opportunities to automate the routine monthly BMS trend log
downloads and include key performance indicators on the building dashboard.
Examples include plotting monthly energy use against calibrated modeled
energy use. Any significant deviation from monthly expectations could help to
identify and respond to significant issues early.
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RECOMMENDATIONS TO DESIGN TEAMS
It would be valuable to develop a coordinated M&V approach that outlines key
BMS data to trend, time increment for trending the data, defining which
circuits need to be submetered, and give thought to how various stakeholders
charged with meeting ZNE performance goals will be able to access the data
in a quick and easy way. Leaving these as ad-hoc decisions that the controls
contractor has to make on the fly is not optimal for leveraging the usefulness
of the BMS data for meeting ZNE goals.
Downloading and processing BMS trend data remains a complex and time
consuming job for building O&M personnel. The design team and controls
contractor should jointly consider opportunities in specifications and control
system selection that would help facilitate ready use of appropriate BMS data
vital to managing and attaining ZNE and related performance goals.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Design teams are becoming increasingly accountable for building performance
and will have an increased stake in how well buildings perform. There are
numerous opportunities for the design team to enhance the effectiveness of
monitoring, controls, and related systems through thoughtful design. There
are significant opportunities to further explore and promote these
opportunities from the utility and policy side.
AUTOMATED FAULT DETECTION AND DIAGNOSTICS
The BMS system produces a large amount of very useful data that are being stored in trend
logs. Unfortunately, this data is not always easily accessible to typical building owners and
operators. The data must be manually downloaded to a spreadsheet and processed, which is
a time consuming process. Typically, this data is rarely reviewed and therefore provides
little actionable information to inform building owners/operators on an ongoing basis.
Making better use of this data will be crucial for ensuring ongoing ZNE performance by the
Library and other ZNE buildings.
An emerging set of complementary automated “Fault Detection and Diagnostic” (FDD)
software tools and related building dashboard tools are coming onto the market which will
facilitate use of this detailed BMS data and automate much of the labor-intensive review and
processing. While the building automation system is capable of controlling equipment, data
display, alarming and trending, it is not capable of detailed fault detection and
troubleshooting. Fault detection and diagnostics software is capable of conducting custom
detailed analysis on the data handled by the building automation system and serving it in a
graphical method that is intuitive to the user. The appropriate fault detection system, much
like the automation system, is flexible enough to be modified and updated to accommodate
future changes to systems and sequences of operation. This software package gives the
user the capability to run analytics across the entire range of control points within the
automation system, generate and distribute alarms, display data graphically and make
corrections to setpoints and schedules accordingly.
Ongoing commissioning of the building systems is the primary intent of the automated fault
detection system. While commissioning and re-commissioning of systems is effective for
instantaneous verification of correct system operation, fault detection systems continue to
watch building systems long after start up and initial testing is complete. The combination of
ongoing monitoring and custom analytics provides a platform for continued system
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optimization and a real-time view of the buildings energy consumption. One example of a
FDD system is SkySpark, illustrated below.
FIGURE 85: EXAMPLE OF SKYSPARK FDD SOFTWARE INTERFACE
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Explore opportunities to expand the Lucid Designs dashboard to include some
of the automated diagnostics and fault detection reporting that will help the
Steven’s library maintain ZNE status and minimize facilities impact for
downloading and processing BMS data. This could include things such as
comparing monthly building EUI to predicted EUI (from calibrated model) and
reporting significant deviations.
RECOMMENDATIONS TO DESIGN TEAMS
Design teams will want to watch this nascent field carefully. There are some
very exciting developments that may be useful to incorporate into high
performing building projects to help ensure challenging performance targets
are met.
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RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
AFDD is an exciting development in the building industry and has significant
potential to help improve long term building performance. Emerging
technology studies and similar efforts to document performance impacts and
best practices with AFDD systems would be very valuable.
ENERGY MODELING
Building energy modeling is used and applied in different ways depending on what one is
trying to accomplish. Each use has a unique set of practitioners, goals, and established
approaches to building energy modeling. Traditionally there has been limited cross-over
between each of these different building energy modeling domains, their practitioners, their
targeted building lifecycle phase. Each domain requires a niche expertise, and involves
different stakeholders, customers, team-members and building phases. The rise of ZNE
buildings creates very interesting cross-over opportunities between the different energy
modeling domains. The building energy model now becomes a critical tool for (1) optimizing
building energy performance in the early design phase, (2) documenting compliance, (3)
accurately projecting actual building performance during operations to size the onsite
renewable system and meet ZNE performance requirements, (4) verifying ZNE performance
and “correcting” for atypical weather, occupancy, and other operational issues as is done for
guaranteed energy savings projects, and (5) facilitating building operations personnel to
maintain ZNE operations. There is need for increased education about the different ways
energy modeling can and needs to be applied to ZNE buildings.
RECOMMENDATIONS TO DESIGN TEAMS
ZNE building projects will require a higher level of modeling accuracy and
applying energy modeling for different purposes. Increased design team use
of building energy modeling is required and team energy modeling expertise
must generally increase as well..
Design teams need to understand that there are different uses for energy
modeling throughout the project life cycle, and effectively use energy
modeling at each phased.
Design teams should be very careful to understand the difference between a
“compliance energy model” and an energy model used to estimate actual
building operational energy for ZNE renewable energy system sizing.
A final “as built” energy model should be developed and used to confirm ZNE
estimates.
Appropriate safety margins should be built into ZNE renewable energy system
sizing to account for weather, occupancy, schedule, space use intensity, and
plug load variance that are likely to occur.
Standard assumptions for plug loads, DHW, and other loads which do not
typically matter as much in compliance modeling (since they are assumed
equal in both the design and base-case and do not typically appreciably
impact compliance energy savings projections) should be very carefully
evaluated. These loads are often significantly different from actual building
loads and poor estimates can jeopardize a building’s ability to achieve ZNE.
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RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
There is a significant need to develop modeling guidelines and best practices
for practitioners to transition from “compliance” modeling to “performance”
modeling. The National Renewable Energy Laboratory’s Building America
Program, for example, developed a set of energy modeling guidelines and
data for residential energy modeling that were very useful to practitioners.
Similar approaches could be taken for commercial building modeling. Existing
databases (i.e., CUESS) could be leverage to help develop guidelines for
water heating energy use and other relevant loads. Water heating energy use
was significantly over-estimated for this project.
PLUG LOADS
Plug loads comprise an increasingly large percentage of the total building energy use as
HVAC and other regulated loads are reduced. It is not uncommon for plug loads to represent
25% - 50% of a ZNE building’s total load. The Library’s plug loads are relatively small
compared to typical buildings, accounting for 16% of the total building energy use in 2013.
The original energy model over-estimated plug load energy by ~50%.
Note that a significant upward trend in plug load energy began in July 2014 and continued
through the end of the M&V period in September 2014. Refer to Figure 72. The reasons for
this increased consumption is unclear, but could include significant new equipment
additions, equipment not being turned off, some type of equipment malfunction, the use of
a portable electric heater(s), or similar issues.. The reasons for this should be investigated
and corrected if needed by Library staff.
The key lessons learned are that plug loads represent a large portion of building energy use
and focusing on opportunities to reduce these loads will be important for future ZNE
buildings. Furthermore, it is important to refine energy modeling efforts to estimate these
as accurately as possible. There may be opportunities for PG&E and other organizations to
support projects to improve the modeling of plug loads. As a starting point, it would be
useful to document how well plug loads are currently being modeled (e.g., a study
comparing LEED building energy modeled data vs. actual plug loads).
ELECTRICAL ROOM TEMPERATURE SETPOINT The electrical room, which contains a number of servers, was initially maintained between
66 oF and 69.5oF. Typically, servers have higher permissible operating temperature ranges
(server temperature specifications should be reviewed to determine thresholds), and typical
temperature setpoint relaxed accordingly to reduce air-conditioning energy use. Sacred
Heart increased its temperature setpoints in the electrical room to 74-76oF.
RECOMMENDATIONS TO DESIGN TEAMS
Explore opportunities to specify equipment with robust temperature operating
ranges, and make sure this information is communicated to building owners,
control contractors who set initial temperature setpoints, commissioning
agents, and other building operations stakeholders.
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LIGHTING
The Library uses linear fluorescent lighting with daylighting and occupancy controls to
reduce peak lighting power density (LPD). LED lighting is becoming increasingly cost
effective and can be merged with advanced control strategies, individually controlled
luminaires, and advanced control strategies to minimize lighting energy.
RECOMMENDATIONS TO DESIGN TEAMS
Design teams should specify LED lighting and advanced control strategies that
are well matched to LED lighting technology.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Programs such as the Emerging Technologies Program provide invaluable
information to the design community on what works and what does not,
costs, and other barriers and opportunities related to the installation and
performance of emerging products and technologies. There is an ongoing
need for this information regarding emerging lighting technologies and
practices (i.e., “occupant specific lighting”).
ONSITE WATER RECYCLING AND RAINWATER CAPTURE
Nearly 10% of the library’s energy is spent on the rainwater and graywater systems.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
No electricity use was measured on the graywater system’s UV system. The
facility should check to ensure the UV system is operating correctly.
RECOMMENDATIONS TO DESIGN TEAMS
Consider the energy impacts of onsite water systems. Make sure to include
these loads in the relevant ZNE and PV array sizing calculations if they are to
be included in the “ZNE” load. Specify efficient and appropriate equipment
and systems and ensure they are performing as expected.
RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS
Water/energy/carbon nexus issues are increasingly becoming a part of
building-level design. This is an area where designers could use guidance on
best practices.
VENTILATION AND AIR QUALITY The operational survey indicated there is a tendency for the building to feel hot or stuffy
during high occupancy periods or hot weather. Occupants use windows (natural ventilation)
for supplemental ventilation. There are a number of potential reasons for this condition,
which are beyond the scope of this M&V effort to fully investigate. The M&V efforts did note
that there is limited compressor use for the main reading room, and it is possible that this
system is not providing adequate humidity control. Room humidity is not one of the trend
logs available on the BMS. It is possible that fine-tuning of the controls could address this
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(e.g., CO2 level setpoints, evaporative cooling staging, supply air humidity control
setpoints), or it could be that the building is operated per its design intent, and that some
occupant education on the building design and use of the natural ventilation and ceiling fan
features to provide additional airflow could address the issues.
RECOMMENDATIONS TO SACRED HEART SCHOOLS
Investigate humidity levels if building occupants continue to note hot and
stuffy conditions. Fine-tuning of minimum ventilation rates and demand
controlled ventilation controls sequences may be required.
CEILING FANS The operational survey indicates that the ceiling fans are noisy and are not used often.
RECOMMENDATIONS TO DESIGN TEAMS
Issues such as noise have a demonstrated impact on occupants use of the
ceiling fans and other equipment. Designers should carefully consider noise
and related issues which may impact user acceptance and use of equipment
and strategies.
In summary, Stevens Library is performing very well and meeting its ZNE goals. The most
important recommendations to the facility is to make sure that the incorrectly sized CT’s on
the BMS electricity submeters are replaced with the correct sized CTs, or have the updated
calibration factor programmed into the BMS. We also strongly recommend that Sacred Heart
Schools continue some type of M&V for not just the library, but all of its buildings to ensure
efficient and cost effective operations. For the design team, the most significant
recommendations for future projects would be to continue refining the energy modeling
process. Plug load, rainwater/graywater system, and DHW heating energy projections were
significantly off. This does not significantly impact this building, but these mis-estimates
could significantly impact ZNE attainment for another building type. Also, the design team
did an excellent job designing the electrical system to be well metered, and included a front
end dashboard. At this point however, it will most likely take strong design team leadership
to ensure that the data logging capabilities are translated into useful and actionable data on
the dashboard that will help building managers maintain long-term ZNE performance. From
the utility perspective, there are significant opportunities more effectively incorporate
submetering into buildings and work with controls contractors, building dashboard
developers, and building operators to make this data useful and actionable. Automated fault
detection and diagnostics will play an important role in managing the massive amounts of
data that submeters and BMS systems generate.
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APPENDIX A: ADDITIONAL ELECTRICAL AND
MECHANICAL SYSTEM DETAILS System details, drawings, schedules, control sequences, BMS points, and related
information are excerpted from the drawings and other design documents below for
reference and future diagnostic efforts.
ELECTRICAL SYSTEM (LAYOUT AND PANEL CONFIGURATION) The following figure shows the 1-line electrical diagram for the library. All of the buildings
are fed from a single main distribution panel. The library’s PV system also feeds directly into
the main switchgear via a separate distribution panel, which is separate from the building
feed. There are no interconnections between the PV system and building supply panels in
the library’s electrical room.
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FIGURE 86: BUILDING B ELECTRICAL 1-LINE DRAWING
(Excerpted from Drawing E601B showing library distribution, with other building feeds
removed for clarity)
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The following schedules show the individual circuits serving the library. All of these panels
are located in the library’s electrical room. The following schedules are excerpted from
Drawing E701B. The library is served only by electricity. The first schedule summarizes the
mechanical equipment electrical connections and is not an actual panel schedule. Panel
schedules follow.
FIGURE 87: MECHANICAL EQUIPMENT ELECTRICAL CONNECTION SUMMARY
FIGURE 88: DISTRIBUTION SWITCHBOARD DP-BH SCHEDULE
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FIGURE 89: PANEL B-H1 SCHEDULE
FIGURE 90: PANEL LCP B-H1 SCHEDULE
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FIGURE 91: PANEL B-L1 SCHEDULE (AS-BUILT)
Note: Figure 91 shows the as-built drawing for electrical panel B-L1. The markups are made
by the contractors and show the corrections as installed.
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FIGURE 92: PANEL B-L2 SCHEDULE (AS-BUILT)
CONTROLS/BUILDING MANAGEMENT SYSTEM (BMS) The following figure summarizes the BMS layout. Each building has a local controller
connected to a central system.
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FIGURE 93: BMS 1-LINE DIAGRAM
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APPENDIX B: OPERATIONAL SURVEY The purpose of the operational survey is to obtain space usage data to correlate with M&V
data, to identify any related issues that affect performance or comfort (e.g., temperature
control problems), and to aid in model calibration.
The following survey questions were answered via dialogue with the librarian.
OPERATING HOURS What are the library’s typical operational/open hours?
Library is typically occupied/open between 7am-4:30pm; weekdays.
o Monday: 7:30-3:30pm
o Tue-Thur: 7:30 – 4pm
o Friday: 7:30-3:30pm
Some weekend occupancy occurs utilizing the space for meetings and other
activities.
What time do the librarians typically arrive and depart?
7am-4:30pm; Weekdays
Can you please describe the typical occupancy/use patterns for the library
throughout the day? Does this vary throughout the week?
Typical (average) number of people in the main library reading room throughout
the day:
Best guess 75-100 people per day.
Librarian has ~ 14 classes/week; 18 students/class. 15-30 minutes per class.
Tutoring occurs in the main library.
Some students use the facility during lunch (12pm-1pm).
Maximum number of people in the main library likely to be encountered
During standardized testing, there were approximately 50 students occupying the main
library (isolated case).
Other than this event, max number is estimated at around 30 people.
Describe the typical occupancy/use for each of the classrooms, tutoring rooms
and other rooms receive?
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Tutoring Room (Rm 203): 2 people
Conference Room: When occupied, typically 2-4 people.
Tech Office: 2 employees, students visit throughout the day for computer servicing.
Office Next to Tech Office: 2 employees
Are there significant variations in occupancy throughout the day or week
(describe)
Yes. Students come to the tech office for computer support. This occurs
throughout the day, heavy traffic occurs during lunch.
Does the library receive significant weekend use?
No. Once/month.
When and how often does the custodial staff clean the library?
One entry/exit per day.
TEMPERATURES What best describes the temperature conditions of the main reading room:
o Typically “just right”
o Generally “just right”, but occasional temperature swings or other issues.
If yes, describe:
During the winter, the mornings are cold, but generally warm up throughout the
day.
Temperature is never too hot. Climate is described as cool to comfortable.
Can you identify the cause, or when the temperature swings typically
occur? (e.g., mornings, when it is hot/cold out, when the sun shines
through the window, when the space is at capacity, etc.)
o Too cold
Previously discussed with the librarian that a few weeks back, the Speakman unit
was not providing sufficient heat. This has been since corrected.
o Too hot:
In the summers when the sun is out, feels the space can be a bit warm.
o Significant temperature fluctuations/variations
If yes, describe:_________________
Can you identify the cause, or when the temperature swings typically
occur? (e.g., mornings, when it is hot/cold out, when the sun shines
through the window, when the space is at capacity, etc.)
When there are a number of people occupying the library at a given time (i.e.
standardized testing), interviewees feel that there is insufficient outside air and
the HVAC system has trouble maintaining space temperature.
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What best describes the temperature conditions of the smaller classrooms and
tutoring rooms:
o Typically “just right”
o Generally “just right”, but occasional temperature swings or other issues.
If yes, describe:
Occupant generally is content with the exception of that they feel it gets “stuffy”.
Typically opens windows.
Tech office gets a bit warm due to the number of electronic equipment in the
space. Staff doesn’t complain, and opens windows the room feels uncomfortable.
Can you identify the cause, or when the temperature swings typically
occur? (e.g., mornings, when it is hot/cold out, when the sun shines
through the window, when the space is at capacity, etc.)
o Too cold
o Too hot
o Significant temperature fluctuations/variations
If yes, describe:_________________
Tech office gets a bit warm due to the number of electronic equipment in the
space. Staff doesn’t complain, and opens windows when the room feels
uncomfortable.
Can you identify the cause, or when the temperature swings typically
occur? (e.g., mornings, when it is hot/cold out, when the sun shines
through the window, when the space is at capacity, etc.)
Are the spaces able to maintain temperature setpoints during large occupancy
changes (e.g., a class entering/leaving a room, or swings between full and
minimal occupancy?)
Interviewee thinks so.
Are the night-time temperature setback times set appropriately?
Unaware of temperature set-back controls.
Does the space warm up in time for the normal occupancy hours?
Chilly in the mornings. Typically warms up around 8:20 am.
VENTILATION AND FRESH AIR Are you satisfied with the ventilation and fresh air?
Consistent remarks regarding air quality. Occupants feel that private offices and
libraries can get stuffy. Occupants of private spaces typically leave windows
open.
Please describe any problems or issues related to ventilation (e.g., the classrooms
get stuffy when a class is in there, excessive noise, etc.)
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When it is warm outside and/or there are a number of people in the building,
occupants feel uncomfortable. Consistent remarks regarding air quality.
CEILING FANS Are the ceiling fans used? If so, how often?
Ceiling fans are manually operated. Librarian only uses these fans when the
library is fully occupied. Feels the fans create a lot of noise.
LIGHTING Have you noticed whether the lights dim in response to daylighting?
Unaware of dimming controls on the lighting system.
Have you noticed excessive glare, or solar heating?
More so in the summer, there is constant glare and it warms up the reading
room. Librarian typically utilizes the window shades during this time.
Are there any other issues related to the daylighting systems?
None. Not aware that the site had daylighting controls.
Are the occupancy sensors working correctly?
Thinks so. But staff noted sometimes people forget to turn lights off when they
leave. I don’t think they are aware they have motion sensors.
Have you noticed whether the ceiling fan operation keeps the occupancy sensor
engaged?
No.
Are there any other problems or issues related to lighting?
None.
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EQUIPMENT AND PLUG LOADS
TABLE 17: LIBRARY EQUIPMENT AND PLUG LOADS COUNT
Equipment Quantity Notes/Comments desktop computers and monitors 10 Laptops TV’s and additional
screens/monitors 1 In reading Room
Printers 3 large photocopiers - small/medium photocopiers - refrigerators/freezers -
Any other significant equipment or
appliances? (Please list below) Computer cart/charging
station in tech room for
laptops.
OTHER ISSUES What is your overall impression of the library?
It’s ok.
What do you like best about the new space?
No comments
Are there any areas that need adjustment or fine-tuning?
Bathroom stinks.
Are there any other operational issues that need to be addressed?
Needs to address air quality.
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APPENDIX C: SCHOOL SCHEDULE The Academic Calendar is available online at http://www.shschools.org/page.cfm?p=1465 .
Holidays and other key events are summarized below
FALL 2012
Sunday - August 26, 2012 Mass and Grand Opening
Celebration 1:00 PM
Monday - August 27, 2012 SHP Student Orientation/First
Day of Classes Prep Campus
Tuesday - August 28, 2012 Grades 1-8 HALF DAY Lower and
Middle School Campus
Wednesday - August 29, 2012 Grades 1-8 Full Day 7:50 AM to
2:25 PM Lower and Middle School Campus
Thursday - August 30, 2012 Grades 1-8 Back to School Night
6:00 PM to 9:00 PM
Monday - September 3, 2012 LABOR DAY - ALL SCHOOL NO
CLASSES
Monday - October 15, 2012 FALL BREAK - ALL SCHOOL NO
CLASSES
Tuesday - October 16, 2012 Prep Parent Conferences - SHP
NO CLASSES 8:00 AM to 7:00 PM
Wednesday - October 31, 2012 1st-8th Half Day 11:40 AM Lower
and Middle Schools Campus Dismissal at 11:40am (1st-8th) 1st-8th Parent Conferences 1:00
PM to 4:00 PM Lower and Middle Schools Campus
Thursday - November 1, 2012 Grades 1-8 & Montessori Parent
Conferences - NO CLASSES 8:00 AM to 6:00 PM Montessori & Lower/Middle School Campuses
Friday - November 2, 2012 - NO CLASSES Lower & Middle
School Campus
Wednesday - November 21, 2012 Thanksgiving Break - ALL SCHOOL
NO CLASSES
Thursday - November 22, 2012 Thanksgiving Break - ALL SCHOOL
NO CLASSES
Friday - November 23, 2012 Thanksgiving Break - ALL SCHOOL
NO CLASSES
Monday - November 26, 2012 ALL SCHOOL - CLASSES RESUME
Friday - December 21, 2012
Grades 1-8 & Montessori Dismissal for Christmas Break - HALF DAY Lower/Middle School
Monday - December 24, 2012 Christmas Break - All School NO
CLASSES
Tuesday - December 25, 2012 Christmas Break - All School NO
CLASSES
Wednesday - December 26, 2012 Christmas Break - All School NO
CLASSES
Thursday - December 27, 2012 Christmas Break - All School NO
CLASSES
Friday - December 28, 2012 Christmas Break - All School NO
CLASSES
Monday - December 31, 2012 Christmas Break - All School NO
CLASSES
Tuesday - January 1, 2013 Christmas Break - All School NO
CLASSES
Wednesday - January 2, 2013 Christmas Break - All School NO
CLASSES
Thursday - January 3, 2013 Christmas Break - All School NO
CLASSES
Friday - January 4, 2013 Christmas Break - All School NO
CLASSES
Monday - January 7, 2013 Christmas Break - All School NO
CLASSES Faculty/Staff Retreat - All School
NO CLASSES
SPRING 2013
Tuesday - January 8, 2013 All School - CLASSES RESUME
Monday - January 21, 2013 Martin Luther King Jr. Holiday -
All School NO CLASSES
Monday - February 18, 2013 Winter Break - All School NO
CLASSES
Tuesday - February 19, 2013 Winter Break - All School NO
CLASSES
Wednesday - February 20, 2013 Winter Break - All School NO
CLASSES
Thursday - February 21, 2013 Winter Break - All School NO
CLASSES
Friday - February 22, 2013 Winter Break - All School NO
CLASSES
Monday - February 25, 2013 All School -CLASSES RESUME
Thursday - March 28, 2013 Easter Break - All School NO
CLASSES
Friday - March 29, 2013 Easter Break - All School NO
CLASSES
Monday - April 1, 2013 Easter Break - All School NO
CLASSES
Tuesday - April 2, 2013 Easter Break - All School NO
CLASSES
Wednesday - April 3, 2013 Easter Break - All School NO
CLASSES
Thursday - April 4, 2013 Easter Break - All School NO
CLASSES
Friday - April 5, 2013 Easter Break - All School NO
CLASSES
Monday - April 8, 2013 All School -CLASSES RESUME
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Friday - May 17, 2013 Grades 1-8 In-Service Day
Friday - May 24, 2013 Prep Graduation 5:00 PM Soccer
Field
Monday - May 27, 2013 Memorial Day Holiday - All School
NO CLASSES
Friday - May 31, 2013 Prep Grades 9-11 End of
Semester Exams
Friday - June 7, 2013 Dismissal for Summer Break
FALL 2013 Sunday - August 25, 2013 Freshman Orientation SHP
Campus
Monday - August 26, 2013 Prep Full Day SHP Campus
Tuesday - August 27, 2013 Lower School Half Day 7:50 AM
to 11:40 AM Bergeron Lower School
Middle School Full Day 7:50 AM to 3:15 PM Xie Middle School7:50am-3:15pm
Wednesday - August 28, 2013 Lower & Middle School Full Day
7:50 AM to 2:25 PM Bergeron Lower School 7:50am-2:25pm
Thursday - August 29, 2013 LMS Back to School Night 6:00
PM to 9:00 PM Johnson Performing Arts Building
Monday - September 2, 2013 LABOR DAY- NO SCHOOL
Monday - October 14, 2013 FALL BREAK- NO SCHOOL
Tuesday - October 15, 2013 NO SCHOOL - LMS & PSK In-
Service Day NO SCHOOL - Prep Parents
Conferences
Wednesday - October 16, 2013 PREP TEST DAY
Thursday - October 31, 2013 Halloween Celebration - LMS &
PSK Half Day 7:50 AM to 11:40 AM
LMS Parent Conferences 1:00 PM to 4:00 PM
Friday - November 1, 2013 LMS & PSK NO SCHOOL - Parent
Conferences 8:00 AM to 6:00 PM
Wednesday - November 27, 2013 THANKSGIVING BREAK - NO
SCHOOL
Thursday - November 28, 2013 THANKSGIVING BREAK - NO
SCHOOL
Friday - November 29, 2013 THANKSGIVING BREAK - NO
SCHOOL
Monday - December 2, 2013 CLASSES RESUME
Wednesday - December 11, 2013 FEAST OF GUADALUPE
Friday - December 20, 2013 Dismissal for Christmas Break -
PSK & LMS HALF DAY 11:40 AM Montessori & LMS Campus
Monday - December 23, 2013 CHRISTMAS BREAK - NO CLASSES
Tuesday - December 24, 2013 CHRISTMAS BREAK - NO CLASSES
Wednesday - December 25, 2013 CHRISTMAS BREAK - NO CLASSES
Thursday - December 26, 2013 CHRISTMAS BREAK - NO CLASSES
Friday - December 27, 2013 CHRISTMAS BREAK - NO CLASSES
Monday - December 30, 2013 CHRISTMAS BREAK - NO CLASSES
Tuesday - December 31, 2013 CHRISTMAS BREAK - NO CLASSES
Wednesday - January 1, 2014 CHRISTMAS BREAK - NO CLASSES
Thursday - January 2, 2014 CHRISTMAS BREAK - NO CLASSES Friday - January 3, 2014 CHRISTMAS BREAK - NO CLASSES
Monday - January 6, 2014 CHRISTMAS BREAK - NO CLASSES
SPRING 2014 Tuesday - January 7, 2014 CLASSES RESUME
Monday - January 20, 2014 Martin Luther King Jr. Holiday -
NO CLASSES
Friday - February 7, 2014 PSK No School - Parent
Conferences
Monday - February 17, 2014 WINTER BREAK - NO CLASSES
Tuesday - February 18, 2014 WINTER BREAK - NO CLASSES
Wednesday - February 19, 2014 WINTER BREAK - NO CLASSES
Thursday - February 20, 2014 WINTER BREAK - NO CLASSES
Friday - February 21, 2014 WINTER BREAK - NO CLASSES
Monday - February 24, 2014 CLASSES RESUME
Friday - March 14, 2014 NO SCHOOL - IN-SERVICE DAY
Friday - April 4, 2014 LMS No School - Parent
Conferences
Monday - April 14, 2014 EASTER BREAK - NO SCHOOL
Tuesday - April 15, 2014 EASTER BREAK - NO SCHOOL
Wednesday - April 16, 2014 EASTER BREAK - NO SCHOOL
Thursday - April 17, 2014 EASTER BREAK - NO SCHOOL
Friday - April 18, 2014 EASTER BREAK - NO SCHOOL
Monday - April 21, 2014 EASTER BREAK - NO SCHOOL
Tuesday - April 22, 2014 CLASSES RESUME
Friday - May 16, 2014 PSK - NO SCHOOL Parent
Conferences
Friday - May 23, 2014 Prep Graduation
Monday - May 26, 2014 MEMORIAL DAY - No School
Friday - June 6, 2014 LMS & PSK Half Day
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FALL 2014 Sunday - August 24, 2014 Freshman Orientation SHP
Campus
Monday - August 25, 2014 Prep Full Day SHP Campus PSK Back to School Night 5:00 PM
to 8:00 PM Montessori and Campbell Center
Tuesday - August 26, 2014 Lower School Half Day 7:50 AM
to 11:40 AM Bergeron Lower School
Middle School Full Day 7:50 AM to 3:15 PM Xie Middle School7:50am-3:15pm
Wednesday - August 27, 2014 Lower & Middle School Full Day
7:50 AM to 2:25 PM Bergeron Lower School7:50am-2:25pm
PSK Orientation 9:00 AM to 1:00 PM Montessori
Thursday - August 28, 2014 PSK Half Day Montessori LMS Back to School Night 6:00
PM to 9:00
Friday - August 29, 2014 PSK Half Day Montessori
Monday - September 1, 2014 LABOR DAY- NO SCHOOL
Monday - October 13, 2014 FALL BREAK- NO SCHOOL
Tuesday - October 14, 2014 NO SCHOOL - Prep Parents
Conferences
Wednesday - October 15, 2014 PREP TEST DAY
Friday - October 31, 2014 Halloween Celebration - LMS &
PSK Half Day
Thursday - November 6, 2014 MIDDLE SCHOOL - NO SCHOOL -
Parent Conferences 8:00 AM to 6:00 PM
Friday - November 7, 2014 LMS & PSK NO SCHOOL - Parent
Conferences 8:00 AM to 6:00 PM
Wednesday - November 26, 2014 THANKSGIVING BREAK - NO
SCHOOL
Thursday - November 27, 2014
THANKSGIVING BREAK - NO SCHOOL
Friday - November 28, 2014 THANKSGIVING BREAK - NO
SCHOOL
Monday - December 1, 2014 CLASSES RESUME
Friday - December 19, 2014 Dismissal for Christmas Break -
PSK & LMS HALF DAY 11:40 AM Montessori & LMS Campus
Monday - December 22, 2014 CHRISTMAS BREAK - NO SCHOOL
Tuesday - December 23, 2014 CHRISTMAS BREAK - NO SCHOOL
Wednesday - December 24, 2014 CHRISTMAS BREAK - NO SCHOOL
Thursday - December 25, 2014 CHRISTMAS BREAK - NO SCHOOL
Friday - December 26, 2014 CHRISTMAS BREAK - NO SCHOOL
Monday - December 29, 2014 CHRISTMAS BREAK - NO SCHOOL
Tuesday - December 30, 2014 CHRISTMAS BREAK - NO SCHOOL
Wednesday - December 31, 2014 CHRISTMAS BREAK - NO SCHOOL
Thursday - January 1, 2015 CHRISTMAS BREAK - NO SCHOOL
Friday - January 2, 2015 CHRISTMAS BREAK - NO SCHOOL
Tuesday - January 6, 2015 CLASSES RESUME
Monday - January 19, 2015