hawaii energy and environmental technologies (heet) initiative · 2017-05-01 · 6 task 4.2 final...

Hawaii Energy and

Environmental Technologies

(HEET) Initiative

Office of Naval Research

Grant Award Number N0014-11-1-0391

KAWAIKINI NEW CENTURY PUBLIC

CHARTER SCHOOL

Prepared by:

MKThink

Prepared for:

University of Hawaii at Manoa, Hawaii Natural Energy Institute

March 2015

March 2015

TASK 4.2 FINAL REPORTKAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL

PREPARED BY:

MKThinkMark R. Miller, AIA LEEDAP

CEO, Director of Innovation Services

PREPARED UNDER CONTRACT TO:

Office of Naval ResearchDr. Richard CarlinDepartment Head, Code 33

PREPARED FOR:

Hawaii Natural Energy InstituteUniversity of Hawaii at ManoaDr. Rick RocheleauDirector

TASK 4.2 FINAL REPORT - KAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL2

04/01/15 | CONTRACT NO.N00014-11-1-0391 3TABLE OF CONTENTS

Performance Dashboard 4

1 Introduction 6

2 Study Details 8

3 Performance Category 1: Energy 20

4 Performance Category 2: Interior Environment 48

5 Performance Category 3: Daylighting 62

6 System Analysis 1: 24-Hour Load Profiles by Month 70

7 System Analysis 2: Cooling 100

8 System Analysis 3: Lighting 112

9 Conclusion 120

Acknowledgements 130

Table of Contents

4 TASK 4.2 FINAL REPORT - KAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL

Performance Dashboard

RECOMMENDATION 1 - AIR SUPPLY CHECKRun a system check on E Frog AC distribution system to understand the significant difference in air supply distribution effectiveness. Consider monitoring user patterns as a check against building system use by: providing daily use schedules, system program settings, and/or install additional monitoring.

RECOMMENDATION 2 - AC/FAN/LOUVER CHECKStudy how AC and Fans/Louvers are interacting in order to move towards optimal use goals. Determine a way to distinguish between automatic and manual user selected use of Fans and AC. Determine an alternative method to monitor Louver use, so that it can be related more instantaneously to Fan/AC use.

RECOMMENDATION 3 - ADJUST TC ASSUMPTIONSAdjust evaluation techniques and assumptions for user thermal comfort: conduct surveys and/or potentially field validate assumptions through onsite observation.

RECOMMENDATION 4 - INVESTIGATE OCCUPANT BEHAVIOR RELATED TO ENERGY USEIncorporate class schedules to analysis to better tie energy demand patterns to occupant activities.

1: W-E PLATFORM COMPARISON - IS PERFORMANCE CONSISTENT ACROSS PLATFORMS?4 OF 9 PERFORMANCE MEASURES VARIED BY GREATER THAN 20% ACROSS PLATFORMSInterpretation 1: The majority of performance measures across the two platforms (5 of 9) showed less than 20% difference in annual results. However, selected systems did show moderate (20-50%) to significant (>50%)differences: Air Supply and distribution temperatures varied significantly (>50%) due to increases in distribution temperature in the E Frog during the last two quarters; Fan usage between the two buildings was moderately different (20-50%) without a corresponding difference in AC usage; and Thermal Comfort* conditions varied by 20% across the two platforms with the platform using the least fan energy having the higher thermal comfort - which suggests the PMV model and assumptions are not a good fit for the building type and/or user behavior.

2: MODEL COMPARISON - DO PLATFORMS PERFORM AS PREDICTED BY MODELS?BOTH PLATFORMS USED LESS ENERGY THAN ANTICIPATED (42-48% LESS), BUT ALSO LESS THAN OPTIMAL Interpretation 2: West and East Frogs both out-performed model predictions of total annual energy consumption. However, selected systems did not outperform their individual targets. Lighting systems were used more than the anticipated/optimal amount, but that appears to be affected by outdoor lighting being left on at night that may be related to security. Additionally, the AC systems were used more than optimal and often at air temperatures below the optimal “kick-in” temp of 82°F, especially in the E Platform.

3: STANDARD COMPARISON - DO PLATFORMS PERFORM BETTER THAN ESTABLISHED STANDARDS?3 OF 4 PERFORMANCE MEASURES WERE WITHIN STANDARD REC’S FOR AT LEAST ONE PLATFORMInterpretation 3: While 3 of 4 performance measures showed >90% adherence to standards in at least one platform, Thermal Comfort performance showed less than 50% adherence to standard recommendations. However, it appears that the relatively low performance may be a result of model assumptions for ASHRAE using clothing insulation and metabolic rates that, upon further investigation, tend to cause large variations in model fit. Adjusting those assumptions to fit user behavior as inferred through use of building systems may lead to a better calibration of the PMV calculation and to a higher percentage of PMV scores within the comfort range (see Appendix for details). Lighting use and illuminance varied between the two platforms with E Frog meeting illuminance criteria using daylighting more than W Frog, leading to higher than anticipated indoor lighting loads at W Frog. Air Supply temperature stability across the distribution plenums in the E Frog also showed less than 50% adherence to expectations and should be inspected for issues.

RecommendationsFindings + Interpretation

04/01/15 | CONTRACT NO.N00014-11-1-0391 5PERFORMANCE DASHBOARD

PERFORMANCE CATEGORY 2. MODEL COMPARISON 1. W-E PLATFORM COMPARISON

PERFORMANCE CATEGORY 1:ENERGY

West EastDifference b/tw W & E compared to Optimal:

Anticipated* Optimal* Anticipated* Optimal*

Total (kWh/yr per building)% above or below model value ( +/- )

- 42% - 9% - 48% - 19% 10%

AC - 81% + 31% - 80% + 37% 6%

Fan + 225% - 29% + 115% - 53% 24%

Interior Lighting** + 34% + 19% 15%

Plug** - 45% - 46% 1%

Exterior Lighting*** 1087 kWh 1067 kWh 2%

Solar Radiation (Cumulative Insolation) -10%* N/A

3. STANDARD COMPARISON 1. W-E PLATFORM COMPARISON

PERFORMANCE CATEGORY 2:INTERIOR ENVIRONMENT

West East Difference b/tw W & E compared to standard:

Thermal Comfort (PMV):% time w/in ASHRAE Comfort Zone

25%** 45%** 20%

CO2:% time below ASHRAE threshold

99.94% 99.95% 0.01%

Air Supply: % time dist. temp within 10°F of supply temp

92% 36% 56%

PERFORMANCE CATEGORY 3:DAYLIGHTING

West East Difference b/tw W & E compared to standard:

Illuminance Level% time Lights off w/ >5 ft-cd & lll. ratio <5

70.1% 91.7% 21.6%

*Anticipated model predictions are based on inefficient use and interaction of building systems. Optimal model predictions assume efficient building system use and interaction, specifically for AC/Fans. **Interior Lighting and Plug load model predictions are the same for anticipated/optimal, and lower use is considered preferable. ***Exterior Lighting use was assumed to be 0 kWh in the Phase II ONR Report Energy Demand Models (as seen on page 13). Therefore energy use totals are presented for Exterior Lighting, instead of percentage comparisons to the model.


In support of the Hawaii Natural Energy Institute’s Project: Hawaii Energy and Environmental Technologies (HEET) Initiative 2010, and under Contract No. N00014-11-1-0391, MKThink instrumented two identical Project Frog high-performance modular buildings at the Kawaikini New Century Public Charter School in Līhu’e, Kauai for testing and analysis.

The two identical portable classrooms were built using passive design elements to decrease energy demand. They are named Hale Akamai I & II, but will be referred to as the West and East buildings throughout the report.

Introduction1

04/01/15 | CONTRACT NO.N00014-11-1-0391 7INTRODUCTION

Under this project, the buildings have been monitored for energy and building performance from March 2013 to March 2014. This study will evaluate and report on one year of energy and environmental (interior & exterior) data from the two buildings. Results will be compared between the two buildings, as well as to models which were previously developed specificly for the Hale Akamai buildings and Līhu’e climate. Based upon the findings, recommendations for improved performance as well as areas for future study will be presented.


Study Details2The analysis of this study is split into two parts: Performance Categories & System Analyses.

In the Performance Category sections, data collected from each building is compared to previously established performance criteria. The performance criteria are specific to the Hale Akamai buildings and Līhu’e climate, and were designed using both models and established guidelines for building performance. The intent of the Performance Category part of the study is to present building performance results using a structured, standardized analysis process which can be implemented in future studies of other buildings. The three Performance Categories are: Energy, Interior Environment and Daylighting.

The System Analysis sections look at building systems as a whole and allow for more open-ended analyses across different metrics. While the Performance Category sections focus on performance criteria specific to individual metrics, the System Analysis sections focus on how the different metrics influence each other. Consequently, findings from the Performance Category sections will guide the direction of the System Analysis sections and give clues as to which aspects of the system are underperforming. The System Analysis sections will also focus on how system patterns differ from month to month. The three System Analyses are: 24-Hour Load Profiles by Month, Cooling and Lighting.

Throughout the report, identical charts from each building will be presented side-by-side across pages. Left-hand pages (even page #) will show West building charts, while right-hand pages (odd page #) will show East building charts. The following icons will be seen in the top corners of the pages:

W E

04/01/15 | CONTRACT NO.N00014-11-1-0391 9STUDY DETAILS

Background: Building Characteristics 10

Background: Site Climate 11

Methodology: Performance Categories 12

Methodology: Energy Demand Models 13

Methodology: Date Range 14

Equipment: Sensor List 15

Equipment: Sensor Layouts 16

Equipment: Status Summary 18

Notes 19


2 Background: Building Characteristics

W

E

LocationKawaikini New Century Public Charter School3-1821-J Kaumuali’i HwyLīhu’e, HI 96766(21.969138, -159.399956)

Construction• Steel Frame• 1,280 NSF• Built in 2013

Operating Hours7:45 AM - 2:00 PM (M-F)

East Building: Hale Akamai I‘Ōlelo Hawai’i (Grades 7-12)Social Studies (Grades 7-12)

West Building: Hale Akamai IILanguage Arts (Grades 7-12)Math (Grades 7-12)

04/01/15 | CONTRACT NO.N00014-11-1-0391 11

2 Background: Site Climate

STUDY DETAILS

Līhu’e, Kauai Average Climate StatisticsJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Avg Max Temperature [°F] 77.9 78.1 78.4 79.5 81.1 83.1 84.0 84.7 84.7 83.1 80.8 78.6 81.2

Avg Temperature [°F] 71.6 71.6 72.7 73.9 75.7 77.7 79.0 79.5 79.2 77.5 75.6 72.9 75.6

Avg Min Temperature [°F] 65.9 66.1 68.0 69.5 70.3 72.5 73.8 74.1 73.6 72.0 70.2 67.1 70.3

Relative Humidity [%] 69 67 66 66 66 65 65 67 65 67 69 68 67

Number of Wet Days 16 15 19 17 17 17 20 20 16 19 17 19 212

Avg Sunlight Hours/Day 06:15 07:04 06:17 05:58 07:13 08:26 08:46 08:36 08:20 06:58 06:14 05:54 07:10Source: climatemps.com

2.5%

5%

7.5%

10%

12.5%

WEST EAST

SOUTH

NORTH

0 − 1

1 − 2

2 − 3

3 − 4

4 − 5

5 − 6

6 − 7

7 − 8

m/s

% of time (frequency)

Wind Rose(East Building Rooftop Weather Station)


Methodology: Performance Categories21 EnergyCompared measured energy use to energy demand models taken from the Phase II ONR Report. The energy demand models are specific for the Hale Akamai buildings and Līhu’e climate. There are models for the following energy usage groups: mechanical cooling (AC), fans, interior lighting and plugs.

For each energy usage group, there are models representing high, anticipated and optimal estimates of energy use. The following page, ‘Methodology: Energy Demand Models’, describes each model.

Finally, solar radiation measurements from the weather station atop the East Building roof are compared to solar radiation model data from the National Solar Radiation Data Base (hosted by National Renewable Energy Laboratory).

2 Interior EnvironmentThermal comfort is modeled using Predicted Mean Vote (PMV), which takes the following inputs: air temperature, relative humidity, mean radiant temperature, air speed, clothing insulation and metabolic rate. Building performance in relation to thermal comfort is then judged by the percentage of time each building’s PMV value is within the ASHRAE Comfort Zone (-0.5 ≤ PMV value ≤ 0.5).

Building performance in relation to air quality is judged by whether or not indoor carbon dioxide concentrations exceed benchmarks for inadequate ventilation (ASHRAE) or minor cognitive impairment (Satish et al., 2012).

Supply air distribution performance is measured by comparing the plenum inlet temperature to the temperatures by floor diffusers in the northwest, center and southeast areas of the room. Building performance in relation to supply air distribution is then judged by the percentage of time ΔT ≤ 10°F between the plenum inlet and floor diffusers.

3 DaylightingIlluminance levels are measured at the teaching wall and on the ceiling. Ceiling illuminance is used as a proxy for working surface illuminance. The problem of glare is measured using Illuminance ratio, which is the ratio between wall and working surface illuminance. Building performance in relation to daylighting is judged by the percentage of time lights were off while the wall illuminance exceeded 5 ft-cd and illuminance ratio stayed below 5.

04/01/15 | CONTRACT NO.N00014-11-1-0391 13

2 Methodology: Energy Demand Models

STUDY DETAILS

High Anticipated OptimalThe high estimate assumes that natural ventilation is not used at all - that instead, air conditioning is used, with a thermostat setpoint of 77°F. In addition to air conditioning, the high estimate also assumes that the fans are used, even though they should not be on while the buildings are in active cooling mode. This is simply done to be conservative. The high estimate also assumes that daylighting is not used, so that electric lights are used during 100% of occupied hours. This, in turn, is assumed to increase cooling loads by 11%.

The anticipated estimate is a middle value between the two extremes of high and optimal estimates. Anticipated estimates assume that daylighting is used but that natural ventilation is not.

The optimal estimate for each site assumes that the buildings are cooled by natural ventilation up until a thermostat setpoint of 82°F, at which point they switch from passive to active mode, closing the ventilation louvers or windows and turning on air conditioning. This should be comfortable despite the higher temperature setpoint because of the building’s features such as ceiling fans, proper solar control, and reduced interior mean radiant temperatures. The optimal estimate also assumes that daylighting is used to reduce lighting energy.

Mechanical Cooling 3,838 kWh/year 3,458 kWh/year 500 kWh/year

Fans 1,000 kWh/year 220 kWh/year 1,000 kWh/year

Interior Lighting 2,008 kWh/year 1,028 kWh/year 1,028 kWh/year

Exterior Lighting 0 kWh/year 0 kWh/year 0 kWh/year

Plug Loads 1,278 kWh/year 1,278 kWh/year 1,278 kWh/year

Total 8,124 kWh/year 5,984 kWh/year 3,806 kWh/yearSource: Phase II ONR Report


Methodology: Date Range2

Active/Non-Active DefinitionsActive Days Non-holiday weekdays (includes summer break)

Non-Active Days Weekends and holidays (excludes summer break)

Monthly Day TotalsMar ‘13 Apr ‘13 May ‘13 Jun ‘13 Jul ‘13 Aug ‘13 Sep ‘13 Oct ‘13 Nov ‘13 Dec ‘13 Jan ‘14 Feb ‘14 Mar ‘14 Total

Active Days 5 22 22 19 22 21 20 23 18 15 14 19 10 230

Non Act. Days 6 8 9 11 9 10 10 8 12 16 17 9 5 130

School Days 5 22 22 5 0 19 20 23 18 15 14 19 10 192

Holidays3/26/13 Prince Jonah Kuhio Kalanianaole Day (HI)

3/29/13 Good Friday (HI)

5/27/13 Memorial Day

6/8/13 - 8/4/13 SUMMER BREAK

8/16/13 Statehood Day (HI)

9/2/13 Labor Day

11/11/13 Veterans Day

11/28/13 - 11/29/13 Thanksgiving Day

12/21/13 - 1/12/14 WINTER BREAK

1/20/14 Dr. Martin Luther King Jr. Day

2/17/14 President’s Day

Study PeriodTotal 3/21/2013 - 3/15/2014

Quarter 1 3/21/2013 - 6/20/2013

Quarter 2 6/21/2013 - 9/20/2013

Quarter 3 9/21/2013 - 12/20/2013

Quarter 4 12/21/2013 - 3/15/2014

04/01/15 | CONTRACT NO.N00014-11-1-0391 15

Equipment: Sensor List2

STUDY DETAILS

Energy

Mechanical Cooling (AC)Condensing Unit 1 (W/E)

Exhaust Fan 1 (W/E)

FansCeiling Fans 1 (W/E)

Fan Coil Unit 1 (W/E)

LightingMain Space 1 (W/E)

Wall and Exterior 1 (W/E)

Louvers Louver Actuator 1 (W/E)

Panel Feed (System Total) Panel Feed 1 (W/E)

Interior Environment

Temperature

Floor - Plenum 3 (W/E)

Floor - Surface 3 (W/E)

Wall - Air 2 (W/E)

Wall - Surface 2 (W/E)

HVAC - End 1 (W/E)

Relative HumidityRoom 1 (W/E)

HVAC 1 (W/E)

Air Speed Room 1 (W/E)

Carbon Dioxide Room 1 (W/E)

Weather StationTemperature

Roof (only East building)

1

Relative Humidity 1

Solar Radiation 3

Wind Speed, Gust Speed 1

Wind Direction 1

Rain 1

Dew Point 1

Air Pressure 1

Daylighting

IlluminanceWall 1 (W/E)

Ceiling 1 (W/E)


W E

2 Equipment: Sensor Layouts

Cat 5 Cable

18-4 Cable

04/01/15 | CONTRACT NO.N00014-11-1-0391 17

W E

STUDY DETAILS

Cat 5 Cable

18-4 Cable


2 Equipment: Status Summary

Equipment Item LocationEquipment Operational

(functioning)

Communication Operational

Sensor Operational

(data accurate)

AMX Ni-2100 Central Controller E/W

Serial Communications Adapter E/W

Dent Industries Powerscout 1 E/W

Split Core Current Transformers E/W

1-Wire Network Hub E/W

1-Wire Network 2 Port Box E/W

1-Wire Network 4 Port Box E/W

Floor Surface Digital Temp Sensors E/W

Room Air Digital Temp Sensors E/W

Wall Surface Digital Temp Sensor E/W

Analog Sensor Hub (standard) E/W

Analog Sensor Hub (photometric) E/W

LI-COR LI-210SZ Photometric Sensor E/W

Air Velocity Sensor E/W

Non-Dispersive Infrared CO2 Analyzer E/W

Wall Mounted Humidity Transmitter E/W

YesNo

04/01/15 | CONTRACT NO.N00014-11-1-0391 19

2 Notes

STUDY DETAILS

Energy NotesEnergy and power calculations are performed by summing power values in kilowatts (KW) and dividing by a factor of 6 to account for 10 minute sensor collection frequency, resulting in kilowatt hour (kWh) values. System total energy and plug load energy calculation changed when platform PV panels were installed, making Total Panel Feed data inaccurate.

Before 9/25/13: Total System Power came from the Panel Feed sensor. Plug load was calculated by subtracting the sum of all system loads (AC, Fans, Lighting, Louvers) from the Panel Feed values.

After 9/25/13: Panel Feed values were not usable, therefore Plug Load average power value to date was used as constant value and Total System Power was a sum of all systems (AC, Fans, Lighting, Louvers) with the Plug Load constant also added.

Possible issue with November 2013 West Building mechanical cooling power data, as suggested by comparing it to East building data from the same month.

The pyranometer has a maximum around 1277 W/m2, which was confirmed by the manufacturer. Therefore, measured cumulative solar insolation was potentially less than modeled.

PMV AssumptionsClothing Insulation 0.35 clo (t-shirt & shorts)

Metabolic Rate 1.1 Met (sitting at desk)

Pressure 0.99 atm


Measured energy use is compared to energy demand models taken from the Phase II ONR Report. The energy demand models are specific for the Hale Akamai buildings and Līhu’e climate. There are models for the following energy usage groups: mechanical cooling (AC), fans, interior lighting and plugs. For each energy usage group, there are models representing high, anticipated and optimal estimates of energy use. The models are described in ‘Methodology: Energy Demand Models’, on page 13.

For each energy usage group, there are average daily use totals and 24-hour load profiles. The charts display averages for active, non-active and top 5% usage days.

Here is the color scheme for energy usage groups used in this section:

Mechanical Cooling Fans Interior Lighting Exterior Lighting Plugs

Finally, solar radiation measurements from the weather station atop the East Building roof are compared to solar radiation model data from the National Solar Radiation Data Base (hosted by National Renewable Energy Laboratory). However, the validity of the solar radiation data is questionable due to sensor issues, as mentioned in ‘Notes’, on page 19. This solar radiation analysis is in Section A of the Appendix.

Key findings from this performance category are highlighted on the following pages (22-23). The complete ‘Findings’ are on pages 122-123.

Performance Category 1: Energy3

04/01/15 | CONTRACT NO.N00014-11-1-0391 21PERFORMANCE CATEGORY 1: ENERGY

Findings 22

3.01 Total System Energy Use [W/E] 24

3.02 Total Energy Use by Category [W/E] 26

3.03 Average Daily Energy Use: Total [W/E] 28

3.04 Average 24-Hour Load Profiles: Total [W/E] 30

3.05 Average Daily Energy Use: Mechanical Cooling [W/E] 32

3.06 Average 24-Hour Load Profiles: Mechanical Cooling [W/E] 34

3.07 Average Daily Energy Use: Fans [W/E] 36

3.08 Average 24-Hour Load Profiles: Fans [W/E] 38

3.09 Average Daily Energy Use: Total Lighting [W/E] 40

3.10 Average 24-Hour Load Profiles: Total Lighting [W/E] 42

3.11 Average Daily Energy Use: Plugs [W/E] 44

3.12 Average 24-Hour Load Profiles: Plugs [W/E] 46


Findings3

19% 21% 8% 31% 20%

22% 15%5% 35% 22%

656 kWh

686 kWh

714 kWh

472 kWh

289 kWh

159 kWh

1087 kWh

1067 kWh

706 kWh

691 kWh

A HO

A H,O

A,O H

H,A,O

AO3452 kWh

3074 kWh

Performance Category 1: Energy

The total energy use for both buildings was below both the anticipated and optimal estimates. The West building used 13% more total energy than the East. In terms of the percentage breakdown by energy usage group, the biggest discrepency between the buildings was in fan usage, which accounted for 21% of the West total and 15% of the East total.

None of the individual energy usage groups in either building had an energy use total exceeding the high estimate. The East building used more energy on mechanical cooling, despite using less energy overall. The West building used 51% more energy on fans and 82% more energy on interior lighting. The difference in plug load was 2%.

Plu

gs W

E

Energy Demand Models

O: Optimal A: AnticipatedH: High

Ext

erio

r Li

ght

ing W

E

Inte

rio

r Li

ght

ing W

E

Fans

W

E

Mec

h.

Co

olin

g W

E

TOTA

L W

E

04/01/15 | CONTRACT NO.N00014-11-1-0391 23PERFORMANCE CATEGORY 1: ENERGY

EW

W E

EW

EW

The West building had PMV values outside the Comfort Zone 75% of the time

The East building had PMV values outside the Comfort Zone 55% of the time.

In both buildings, carbon dioxide concentrations never exceeded benchmarks for inadequate ventilation (ASHRAE) or minor cognitive impairment (Satish et al., 2012).

In the West building, ΔT ≥ 10°F between the plenum inlet and floor diffusers 8% of the time.

In the East building, ΔT ≥ 10°F between the plenum inlet and floor diffusers 64% of the time.

In the West building, during active days between 6:00 AM and 6:30 PM, the lights were on for 21% of the time. For 9% of the time, the lights were off but the lighting criteria wasn’t met (wall illuminance > 5 ft-cd and illuminance ratio < 5).

In the East building, during active days between 6:00 AM and 6:30 PM, the lights were on for 6% of the time. For 2% of the time, the lights were off but the lighting criteria wasn’t met (wall illuminance > 5 ft-cd and illuminance ratio < 5).

Performance Category 2: Interior Environment

Performance Category 3: Daylighting


W E

3.01 Total System Energy Use

Findings• The total energy use for the West building was below both the anticipated and optimal estimates.• Fan usage accounted for 21% of the total energy use in the West building.

3452 kWh

Mechanical Cooling

Fans

Interior Lighting

Exterior Lighting

Plugs

Anticipated

Optimal

Ene

rgy

[kW

h]

0

1000

2000

3000

4000

5000

6000

0

04/01/15 | CONTRACT NO.N00014-11-1-0391 25

W E

PERFORMANCE CATEGORY 1: ENERGY

Findings• The total energy use for the East building was below both the anticipated and optimal estimates.• The West building used 13% more total energy than the East.• Fan usage accounted for 15% of the total energy use in the East building.

3074 kWh

Mechanical Cooling

FansInterior Lighting

Exterior Lighting

Plugs

Anticipated

Optimal

Ene

rgy

[kW

h]

0

1000

2000

3000

4000

5000

6000

0


W E

3.02 Total Energy Use by Category

Findings• None of the individual energy usage groups in the West building had an energy use total exceeding the high estimate.

656 714

289

1087

706

Anticipated

High

Optimal

Anticipated

High, Optimal Anticipated, Optimal

High

High, Anticipated, Optimal

Ene

rgy

[kW

h]

0

500

1000

1500

2000

2500

3000

3500

4000


04/01/15 | CONTRACT NO.N00014-11-1-0391 27

W E


Findings• None of the individual energy usage groups in the East building had an energy use total exceeding the high estimate.• The East building used more energy on mechanical cooling, despite using less energy overall.• The West building used 51% more energy on fans• The difference in total plug load between the West and East buildings was 2%.

686

472

159

1067

691

Anticipated

High

Optimal

Anticipated

High, Optimal Anticipated, Optimal

High

High, Anticipated, Optimal

Ene

rgy

[kW

h]

0

500

1000

1500

2000

2500

3000

3500

4000



W E

3.03 Average Daily Energy Use: Total

27.73

11.45

5.83

11.45

5.83

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

5

10

15

20

25

30

Top 5% Usage Day Active Day Non Active Day

04/01/15 | CONTRACT NO.N00014-11-1-0391 29

W E


28.16

10.53

5.17

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

5

10

15

20

25

30



W E

3.04 Average 24-Hour Load Profiles: Total

Top 5% Usage DayActive DayNon Active Day

Po

wer

[kW

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 31

W E



Po

wer

[kW

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

3.05 Average Daily Energy Use: Mechanical Cooling

13.58

2.66

0.14

2.66

0.14

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

2

4

6

8

10

12

14

16

18

20


04/01/15 | CONTRACT NO.N00014-11-1-0391 33

W E


18.26

2.89

0.08

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

2

4

6

8

10

12

14

16

18

20



W E

3.06 Average 24-Hour Load Profiles: Mechanical Cooling


Po

wer

[kW

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 35

W E



Po

wer

[kW

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

3.07 Average Daily Energy Use: Fans

10.25

2.56

0.82

2.56

0.82

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11


04/01/15 | CONTRACT NO.N00014-11-1-0391 37

W E


8.24

1.79

0.44

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11



W E

3.08 Average 24-Hour Load Profiles: Fans


Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 39

W E



Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

3.09 Average Daily Energy Use: Total Lighting

9.83

4.18

3.10

4.18

3.10

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11


04/01/15 | CONTRACT NO.N00014-11-1-0391 41

W E


8.27

3.70

3.01

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11



W E

3.10 Average 24-Hour Load Profiles: Total Lighting


Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 43

W E



Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

3.11 Average Daily Energy Use: Plugs

3.85

2.071.77

2.071.77

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11


04/01/15 | CONTRACT NO.N00014-11-1-0391 45

W E


6.86

2.121.61

Ave

rag

e D

aily

Ene

rgy

Use

[kW

h]

0

1

2

3

4

5

6

7

8

9

10

11



W E

3.12 Average 24-Hour Load Profiles: Plugs


Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 47

W E



Po

wer

[kW

]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Thermal comfort is modeled using Predicted Mean Vote (PMV), which takes the following inputs: air temperature, relative humidity, mean radiant temperature, air speed, clothing insulation and metabolic rate. Building performance in relation to thermal comfort is then judged by the percentage of time each building’s PMV value is within the ASHRAE Comfort Zone (-0.5 ≤ PMV value ≤ 0.5). Assumptions used while calculating PMV are stated in ‘Notes’, on page 19.

The ‘5% most/least comfortable day’ averages from charts 4.01 and 4.02 are calculated by finding the 5% of days with the highest and lowest percentages of PMV values within the ASHRAE Comfort Zone. Building performance in relation to air quality is judged by whether or not indoor carbon dioxide concentrations exceed benchmarks for inadequate ventilation (ASHRAE) or minor cognitive impairment (Satish et al., 2012).

Supply air distribution performance is measured by comparing the plenum inlet temperature to the temperatures by floor diffusers in the northwest, center and southeast areas of the room. Building performance in relation to supply air distribution is then judged by the percentage of time ΔT ≤ 10°F between the plenum inlet and floor diffusers. The supply air charts (4.04 - 4.05) only use data from days with AC usage.


Performance Category 2: Interior Environment4

04/01/15 | CONTRACT NO.N00014-11-1-0391 49PERFORMANCE CATEGORY 2: INTERIOR ENVIRONMENT

Findings 50

4.01 Average 24-Hour Predicted Mean Vote (PMV) Profiles [W/E] 52

4.02 Percentage of Time PMV within Comfort Zone [W/E] 54

4.03 Average 24-Hour Carbon Dioxide Concentration Profiles [W/E] 56

4.04 Average 24-Hour Supply Air Temperature Profiles [W/E] 58

4.05 Supply Air Distribution Criteria Building Comparison 60


Findings4

19% 21% 8% 31% 20%

22% 15%5% 35% 22%

656 kWh

686 kWh

714 kWh

472 kWh

289 kWh

159 kWh

1087 kWh

1067 kWh

706 kWh

691 kWh

A HO

A H,O

A,O H

H,A,O

AO3452 kWh

3074 kWh



None of the individual energy usage groups in either building had an energy use total exceeding the high estimate. The East building used more energy on mechanical cooling, despite using less energy overall. The West building used 51% more energy on fans and 82% more energy on interior lighting. The difference in plug load was 2%.Energy

Demand Models


Plu

gs W

E

Ext

erio

r Li

ght

ing W

E

Inte

rio

r Li

ght

ing W

E

Fans

W

E

Mec

h.

Co

olin

g W

E

TOTA

L W

E


EW

W E

EW

EW

The West building had PMV values outside the Comfort Zone 75% of the time.










W E

4.01 Average 24-Hour Predicted Mean Vote (PMV) Profiles

04/01/15 | CONTRACT NO.N00014-11-1-0391 53

W E

PERFORMANCE CATEGORY 2: INTERIOR ENVIRONMENT


W E

Percentage of Time PMV within Comfort Zone4.02

Findings• The West building had PMV values outside the Comfort Zone 75% of the time.

24.69% 24.84% 24.65%

89.00%

0%

% o

f ti

me

wit

hin

AS

HR

AE

Co

mfo

rt Z

one

0

10

20

30

40

50

60

70

80

90

100

Overall Active Day Non Active Day 5% Most Comf. Day 5% Least Comf. Day

04/01/15 | CONTRACT NO.N00014-11-1-0391 55

W E


Findings• The East building had PMV values outside the Comfort Zone 55% of the time.

44.95% 44.00% 45.23%

87.87%

0%

% o

f ti

me

wit

hin

AS

HR

AE

Co

mfo

rt Z

one

0

10

20

30

40

50

60

70

80

90

100

Overall Active Day Non Active Day 5% Most Comf. Day 5% Least Comf. Day


W E

Average 24-Hour Carbon Dioxide Concentration Profiles4.03

Findings• In the West building, carbon dioxide concentrations never exceeded benchmarks for inadequate ventilation (ASHRAE) or minor

cognitive impairment (Satish et al., 2012).

Top 5% CO2 DayActive DayNon Active Day

Minor Cognitive Impairment (Satish et al., 2012)

Proxy for Inadequate Ventilation (ASHRAE)

CO

2 C

onc

entr

atio

n [p

pm

]

300

400

500

600

700

800

900

1000

1100

1200

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 57

W E


Findings• In the East building, carbon dioxide concentrations never exceeded benchmarks for inadequate ventilation (ASHRAE) or minor

cognitive impairment (Satish et al., 2012).

Top 5% CO2 DayActive DayNon Active Day

Minor Cognitive Impairment (Satish et al., 2012)

Proxy for Inadequate Ventilation (ASHRAE)

CO

2 C

onc

entr

atio

n [p

pm

]

300

400

500

600

700

800

900

1000

1100

1200

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Supply Air Temperature Profiles4.04

SE PlenumCenter PlenumNW PlenumPlenum Inlet

Tem

per

atur

e [°

F]

64

66

68

70

72

74

76

78

80

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 59

W E


SE PlenumCenter PlenumNW PlenumPlenum Inlet

Tem

per

atur

e [°

F]

64

66

68

70

72

74

76

78

80

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Supply Air Distribution Criteria Building Comparison4.05

Findings• In the West building, ΔT ≥ 10°F between the plenum inlet and floor diffusers 8% of the time.• In the East building, ΔT ≥ 10°F between the plenum inlet and floor diffusers 64% of the time.

92%

8%

36%

64%

ΔT<10°F between plenum inlet and floor diffusersΔT≥10°F between plenum inlet and floor diffusers

W E


Illuminance levels are measured at the teaching wall and on the ceiling. Ceiling illuminance is used as a proxy for working surface illuminance. The problem of glare is measured using Illuminance ratio, which is the ratio between wall and working surface illuminance. Building performance in relation to daylighting is judged by the percentage of time lights were off while the wall illuminance exceeded 5 ft-cd and illuminance ratio stayed below 5.

The ‘5% lowest illuminance day’ average profile from charts 5.01 was calculated by finding the 5% of days with the lowest average illuminance values.


Performance Category 3: Daylighting5

04/01/15 | CONTRACT NO.N00014-11-1-0391 63PERFORMANCE CATEGORY 3: DAYLIGHTING

Findings 64

5.01 Average 24-Hour Illuminance Profiles [W/E] 66

5.02 Daylighting Criteria Building Comparison 68


Findings5

19% 21% 8% 31% 20%

22% 15%5% 35% 22%

656 kWh

686 kWh

714 kWh

472 kWh

289 kWh

159 kWh

1087 kWh

1067 kWh

706 kWh

691 kWh

A HO

A H,O

A,O H

H,A,O

AO3452 kWh

3074 kWh




Demand Models


Plu

gs W

E

Ext

erio

r Li

ght

ing W

E

Inte

rio

r Li

ght

ing W

E

Fans

W

E

Mec

h.

Co

olin

g W

E

TOTA

L W

E


EW

W E

EW

EW











W E

5.01 Average 24-Hour Illuminance Profiles

Active Day5% Lowest Illuminance Day

Illum

inan

ce [

ft-c

]

0

5

10

15

20

25

30

35

40

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 67

W E

PERFORMANCE CATEGORY 3: DAYLIGHTING

Active Day5% Lowest Illuminance Day

Illum

inan

ce [

ft-c

]

0

5

10

15

20

25

30

35

40

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Daylighting Criteria Building Comparison5.02

70%

21%

9%

92%

6%

2%

Lights Off & Illuminance Criteria Met (>5 ft-cd & Illum. ratio <5)*Lights On*Lights Off & Illuminance Criteria NOT Met*

W E

*(from 6:00 AM - 6:30 PM on active days)

Findings• In the West building, during active days between 6:00 AM and 6:30 PM, the lights were on for 21% of the time. For 9% of the time,

the lights were off but the lighting criteria wasn’t met (wall illuminance > 5 ft-cd and illuminance ratio < 5).• In the East building, during active days between 6:00 AM and 6:30 PM, the lights were on for 6% of the time. For 2% of the time,

the lights were off but the lighting criteria wasn’t met (wall illuminance > 5 ft-cd and illuminance ratio < 5).


System Analysis 1: 24-Hour Load Profiles by Month6Daily load profiles for each building are averaged for each month in charts 6.02 - 6.13. Charts 6.01 shows the average daily totals for all months.

Energy usage groups used in this section are more detailed than those used in the ‘Performance Category 1: Energy’ section. The lighting group is broken into interior and exterior lighting. Also, an energy usage group for louvers is used in this section. However, louver energy use makes up a very small percentage (< 1%) oftotal energy use, and its inclusion does little to change the overall load profiles.

Here is the color scheme (updated from ‘Performance Cateogry 1: Energy’, page 20) for energy usage groups used in this section:

Mechanical Cooling Fans Interior Lighting Exterior Lighting Plugs Louvers

Totals from June and July are lower than the rest of the year due to summer break. Totals from March 2013 are also lower than expected since the study period began in the middle of this month.

Finally, as mentioned in ‘Notes’ on page 19, there is a possible issue with November 2013 West building mechanical cooling power data, as suggested by comparing it to East building data from the same month (charts 6.10). One possible explaination is that the mechanical cooling energy sensor malfunctioned during this month, and the energy was then instead counted as plug energy.

Key interpretations from this section are summarized on pages 72-73. Each interpretation is explained with findings used as evidence.

04/01/15 | CONTRACT NO.N00014-11-1-0391 71SYSTEM ANALYSIS 1: 24-HOUR LOAD PROFILES BY MONTH

Interpretations 72

6.01 Average Daily Energy Use Totals, by Month [W/E] 74

6.02 Average 24-Hour Load Profiles: March 2013 [W/E] 76

6.03 Average 24-Hour Load Profiles: April 2013 [W/E] 78

6.04 Average 24-Hour Load Profiles: May 2013 [W/E] 80

6.05 Average 24-Hour Load Profiles: June 2013 [W/E] 82

6.06 Average 24-Hour Load Profiles: July 2013 [W/E] 84

6.07 Average 24-Hour Load Profiles: August 2013 [W/E] 86

6.08 Average 24-Hour Load Profiles: September 2013 [W/E] 88

6.09 Average 24-Hour Load Profiles: October 2013 [W/E] 90

6.10 Average 24-Hour Load Profiles: November 2013 [W/E] 92

6.11 Average 24-Hour Load Profiles: December 2014 [W/E] 94

6.12 Average 24-Hour Load Profiles: January 2014 [W/E] 96

6.13 Average 24-Hour Load Profiles: February 2014 [W/E] 98


Interpretations6

Before the 2013 summer break, the East building used more energy than the West; after the break, the West used more energy

As seen in charts 6.01, in Apr ‘13 and May ‘13 the East building used more energy than the West.

The 2013 summer break occured in June and July, as listed in ‘Methodology: Date Range’ on page 14.

For every month beginning in Aug ‘13 until the end of the study period (Feb ‘14), the West building used more energy than the East.

February 2014 had the biggest discrepancy in energy use patterns between buildings*

*This is only looking at “complete” months without interruptions from vacations or the start/end of the study period (Apr ‘13, May ‘13, Aug ‘13, Sep ‘13, Oct ‘13, Nov ‘13, Feb ‘14)

As seen in charts 6.01, the West building used 39% more energy than the East in Feb ‘14.

For the East building, Feb ‘14 had the lowest energy use among “complete” months, while for the West building more energy was used in Feb ‘14 than in Apr ‘13 or May ‘13.

The load profiles in charts 6.13 show that the discrepancy in energy use between buildings occured in the afternoon. In the West building, AC and fan use increased in the afternoon while interior lighting use continued from the morning. Meanwhile in the East building, AC, fan and interior lighting use ceased between 3:00 PM and 5:00 PM.

AC usage dipped during lunchtime in East building from August to October, 2013

As seen in charts 6.07 - 6.09, there are differences in the shapes of the mechnical cooling load profiles of the two buildings. The East load profiles show dips occuring at around 12:00 PM for the three months. Meanwhile, the West load profiles have no such dips which clearly separate the morning load from afternoon load.

04/01/15 | CONTRACT NO.N00014-11-1-0391 73SYSTEM ANALYSIS 1: 24-HOUR LOAD PROFILES BY MONTH

East building interior lighting use patterns changed in November 2013, when daytime usage became consistent

As seen in charts 6.03 - 6.09, East building interior lighting use occured mostly at night before Nov ‘13. Meanwhile, the West building had consistent daytime usage over the same period.

Starting in Nov ‘13, the East building interior lighting began to be used consistently during school hours, with a load profile similar to that of the West building. This can be seen in charts 6.10 - 6.14.

Plug load consistent throughout the day and night for both buildings during the entire study period

The only exception was in May ‘13 for the East building, when there was a noticible increase in plug energy use during school hours. This can be seen in charts 6.04.

The plug load data for the West building in Nov ‘13 is most likely erroneous labeled and should really be counted as mechanical cooling energy use, as mentioned in ‘Notes’ on page 19.

Highest energy usage months coincided with months when fans were left on overnight

The three months with the highest overnight fan energy use (Aug ‘13, Sep ‘13 and Dec’13, all for West building), were also three out of the top four highest energy usage months.

The exception among the top four highest energy usage months was Aug ‘13 for the East building. Unlike in the West building, the fans in the East building were rarely left on overnight.


W E

6.01 Average Daily Energy Use Totals, by Month

4.1

8.6 8.9

5.0

6.4

20.421.1

13.612.9

16.3

10.811.8

LouversPlugsExterior LightingInterior LightingFansMechanical Cooling

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

2

4

6

8

10

12

14

16

18

20

22

24

Mar '13 Apr '13 May '13 Jun '13 Jul '13 Aug '13 Sep '13 Oct '13 Nov '13 Dec '13 Jan '14 Feb '14

04/01/15 | CONTRACT NO.N00014-11-1-0391 75

W E

SYSTEM ANALYSIS 1: 24-HOUR LOAD PROFILES BY MONTH

3.2

9.3

12.0

4.2

6.8

18.4

16.3

11.311.9

10.7

7.2

8.5

LouversPlugsExterior LightingInterior LightingFansMechanical Cooling

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

2

4

6

8

10

12

14

16

18

20

22

24



W E

Average 24-Hour Load Profiles: March 20136.02

Mechanical CoolingFansInterior LightingExterior LightingPlugsLouvers

Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 77

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: April 20136.03


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 79

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: May 20136.04


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 81

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: June 20136.05


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 83

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: July 20136.06


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 85

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: August 20136.07


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 87

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: September 20136.08


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 89

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: October 20136.09


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 91

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: November 20136.10


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 93

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: December 20136.11


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 95

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: January 20146.12


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 97

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


W E

Average 24-Hour Load Profiles: February 20146.13


Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

04/01/15 | CONTRACT NO.N00014-11-1-0391 99

W E



Ave

rag

e P

ow

er [

kW]

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Chart 7.01 shows how often the AC and fans were used simultaneously.

Charts 7.02 are histograms of the outdoor temperatures during AC or fan usage.

Charts 7.03 show average profiles of PMV and supply air temperature for days of AC usage. With the two profiles overlapped, the average time period of greatest AC usage can be inferred. Also, the effectiveness of the AC system is expressed in the shape of the PMV profile.

Charts 7.04 - 7.06 show daily energy use totals by month for mechnical cooling, fans and louvers.


System Analysis 2: Cooling7

04/01/15 | CONTRACT NO.N00014-11-1-0391 101SYSTEM ANALYSIS 2: COOLING

Interpretations 102

7.01 Outdoor Temperatures during AC/Fan Usage [W/E] 104

7.02 Average 24-Hour PMV & Supply Air Temperature Profiles [W/E] 106

7.03 Monthly Mechanical Cooling Energy Use Building Comparison 108

7.04 Monthly Fans Energy Use Building Comparison 109

7.05 Monthly Louvers Energy Use Building Comparison 110

7.06 Simultaneous AC & Fan Usage Building Comparison 111


Interpretations7

Before the 2013 summer break, the East building used 4x more AC than the West; after the break, both buildings consistently used AC at comparable levels

As seen in chart 7.03 , West building AC was only used sporadically in Apr ‘13 and May ‘13, while it was used four times as much in the East.

From May ‘13 to Aug ‘13, West building AC usage increased twentyfold.

From Aug ‘13 to Dec ‘13, AC usage was comparable in both buildings

The mechanical cooling energy use data for the West building in Nov ‘13 is most likely erroneous. Plug energy use was mislabeled and should really be counted as mechanical cooling energy use, as mentioned in ‘Notes’ on page 19.

West building AC usage stopped at 4:00 PM on average; East stopped at 3:00 PM on average

As seen in charts 7.02, both the average supply air temperature and PMV spiked upwards at around 4:00 PM in the West building.

The East building had upward spikes in average supply air temperature at both around 11:00 AM and 3:00 PM. However, it was only after the 3:00 PM spike that the supply air temperature returned to its nighttime baseline.

Buildings switched from AC cooling to fan cooling at different temperatures

As seen in charts 7.01, the buildings switched from fans to AC at different temperatures.

The West building used AC more often than fans at a threshold temperature of 77°F.

The East building used AC more often than fans at a threshold temperature of 79.5°F.


Louver usage in West building ceased after August 2013

As seen in chart 7.05, louver usage in the West building ceased after Aug ‘13, with the exception of small energy totals in Dec ‘13 and Feb ‘14.

In the East building, louver usage still continued after Aug ‘13, but at a lower rate when compared to before.

For both buildings, Apr ‘13 was the month with highest louver use.

Simultaneous AC and fan usage occured less than 2% of the time in both buildings

As seen in chart 7.06, simultaneous AC and fan usage occured 1.8% of the time in the West building and 1.6% of the time in the East building.

The West building used fans 31.9% of the time (52.8% of active time, 3.9% of holiday time).

The West building used AC 2.0% of the time (8.6% of active time, 1.3% of holiday time).

The East building used fans 12.4% of the time (35.2% of active time, 7.7% of holiday time).

The East building used AC 2.4% of the time (8.9% of active time, 0% of holiday time).

The West building used more energy for fans than the East building in every month*

*This is only looking at “complete” months without interruptions from vacations or the start/end of the study period (Apr ‘13, May ‘13, Aug ‘13, Sep ‘13, Oct ‘13, Nov ‘13, Feb ‘14)

As seen in chart 7.04, the West building used more energy for fans than the East building in every “complete” month.

In the highest energy usage months (Aug ‘13 and Sep ‘13), the West building used 45% and 77% more fan energy than the East building.


W E

7.01 Outdoor Temperatures during AC/Fan Usage

Findings• The West building used fans 31.9% of the time (52.8% of active time, 3.9% of holiday time).• The West building used AC 2.0% of the time (8.6% of active time, 1.3% of holiday time).

04/01/15 | CONTRACT NO.N00014-11-1-0391 105

W E

SYSTEM ANALYSIS 2: COOLING

Findings• The East building used fans 12.4% of the time (35.2% of active time, 7.7% of holiday time).• The East building used AC 2.4% of the time (8.9% of active time, 0% of holiday time).


W E

7.02 Average 24-Hour PMV & Supply Air Temperature Profiles

04/01/15 | CONTRACT NO.N00014-11-1-0391 107

W E



7.03 Monthly Mechanical Cooling Energy Use Building Comparison

0

0.4 0.4

0 0

7.9

8.3

5.0

0.

4.1

1.7

2.4

0

1.61.9

0 0

8.2

7.4

3.6 3.7 3.6

0

0.7

West East

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

1

2

3

4

5

6

7

8

9


04/01/15 | CONTRACT NO.N00014-11-1-0391 109

7.04 Monlthy Fans Energy Use Building Comparison


Findings• In the highest energy usage months (Aug ‘13 and Sep ‘13), the West building used 45% and 77% more fan energy than the East

building.

0.1

2.0

3.1

0.5

1.1

6.4

6.9

2.62.3

4.9

0.8

1.6

0.2

1.9

2.6

0

1.9

4.4

3.9

2.5

1.3

1.0 0.90.7

West East

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

1

2

3

4

5

6

7

8

9



7.05 Monthly Louvers Energy Use Building Comparison

0.

0.1

0.

0.

0.

0.

0 0 00.

00.

0.

0.1

0.1

0

0.

0.

0. 0.

0.

0.

0.

0.

West East

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0.12



7.06 Simultaneous AC & Fan Usage Building Comparison

Findings• Simultaneous AC and fan usage occured 1.8% of the time in the West building and 1.6% of the time in the East building.

1.8%98.2%

1.6%98.4%

Simultaneous AC & Fan Usage

W E


System Analysis 3: Lighting8

Charts 8.01 show average profiles of interior and exteior lighting energy for all days, regardless of active/non-active.

Charts 8.02 - 8.03 show daily energy use totals by month for interior lighting and exterior lighting.


04/01/15 | CONTRACT NO.N00014-11-1-0391 113SYSTEM ANALYSIS 3: LIGHTING

Interpretations 114

8.01 Average 24-Hour Interior & Exterior Lighting Load Profiles [W/E] 116

8.02 Monthly Interior Lighting Energy Use Building Comparison 118

8.03 Monthly Exterior Lighting Energy Use Building Comparison 119


Interpretations8

The West building used more interior lighting than the East building

As seen in charts 8.01, the West has higher interior lighting energy use, which occurs mostly during school hours and at night from around 6-7 PM.

This is also reflected in chart 8.02. Nov ‘13 was the only month when the East building used more interior lighting than the West.

Nighttime exterior lighting accounted for a third of overall energy use

In the West building, interior lighting accounted for 8.4% of total energy use and 21% of total lighting energy use. A total of 289 kWh was used for interior lighting.

In the West building, exterior lighting accounted for 32% of total energy use and 79% of total lighting energy use. A total of 1087 kWh was used for exterior lighting.

In the East building, interior lighting accounted for 5.1% of total energy use and 13% of total lighting energy use. A total of 159 kWh was used for interior lighting.

In the East building, exterior lighting accounted for 35% of total energy use and 87% of total lighting energy use. A total of 1067 kWh was used for interior lighting.

Interior lighting energy use spiked during the winter

As seen in chart 8.02, for both buildings the four months with the highest interior lighting energy use were Nov ‘13, Dec ‘13, Jan ‘14 and Feb ‘14.

Between Apr ‘13 and Oct ‘13, the average daily interior lighting energy use was 0.5 kWh/day for the West building and 0.1 kWh/day for the East building.

Between Nov ‘13 and Feb ‘14, the average daily interior lighting energy use was 1.9 kWh/day for the West building and 1.1 kWh/day for the East building.

04/01/15 | CONTRACT NO.N00014-11-1-0391 115SYSTEM ANALYSIS 3: LIGHTING

Exterior lighting energy use steadily increased over the course of the study period

As seen in chart 8.03, West building exterior lighting use either increased or stayed the same every consecutive month during the study period.

The West building daily average exterior lighting energy use was 37% higher in Feb ‘14 than in Mar ‘13.

East building exterior lighting energy use either increased or stayed the same every consecutive month starting from Jun ‘13 until the end of the study period (Feb ‘14).

The East building daily average exterior lighting energy use was 30% higher in Feb ‘14 than in Apr ‘13.


W E

Average 24-Hour Interior & Exterior Lighting Load Profiles8.01

Findings• In the West building, interior lighting accounted for 8.4% of total energy use and 21% of total lighting energy use. A total of 289

kWh was used for interior lighting.• In the West building, exterior lighting accounted for 32% of total energy use and 79% of total lighting energy use. A total of 1087

kWh was used for exterior lighting.

04/01/15 | CONTRACT NO.N00014-11-1-0391 117

W E

SYSTEM ANALYSIS 3: LIGHTING

Findings• In the East building, interior lighting accounted for 5.1% of total energy use and 13% of total lighting energy use. A total of 159 kWh

was used for interior lighting.• In the East building, exterior lighting accounted for 35% of total energy use and 87% of total lighting energy use. A total of 1067

kWh was used for interior lighting.


8.02 Monthly Interior Lighting Energy Use Building Comparison

Findings• Between Apr ‘13 and Oct ‘13, the average daily interior lighting energy use was 0.5 kWh/day for the West building and 0.1 kWh/day

for the East building.• Between Nov ‘13 and Feb ‘14, the average daily interior lighting energy use was 1.9 kWh/day for the West building and 1.1 kWh/day

for the East building.

0.

0.6

0.4

0.1 0.1

0.6

0.70.9

1.0

1.8

2.8

2.1

00.1

0.2

00.1

0.

0.20.1

1.5

0.70.8

1.5

West East

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


04/01/15 | CONTRACT NO.N00014-11-1-0391 119

8.03 Monthly Exterior Lighting Energy Use Building Comparison

SYSTEM ANALYSIS 3: LIGHTING

2.7 2.7 2.7 2.7 2.72.8

3.13.2

3.43.5 3.5

3.7

1.7

3.0 2.9

2.72.8

3.23.3 3.3

3.53.5 3.6

3.8West East

Dai

ly A

vera

ge

Ene

rgy

Usa

ge

[kW

h/d

ay]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


Findings• The West building daily average exterior lighting energy use was 37% higher in Feb ‘14 than in Mar ‘13.• The East building daily average exterior lighting energy use was 30% higher in Feb ‘14 than in Apr ‘13.


Conclusion9

The findings for the Performance Category criteria, introducted in ‘Methodologies: Performance Categories’ on page 12, are summarized on pages 122 - 123. These findings address the following research questions:

• Model Comparison - Do platforms perform as predicted by models?• Standard Comparison - Do platforms perform better than established standards?

Interpretations, which are based on the findings from all sections, are presented on pages 124-127. Pages 124-125 have a table and summary of the seasonal trends in energy use for both buildings throughout the year. Pages 126-127 have a discussion addressing the broad project goal (as stated in the Task 1 Report) of minimizing energy demand from the various building systems as much as possible without adverse effects to occupant comfort and performance. The discussion begins by addressing the following research question:

• W-E Platform Comparison - Is performance consistent across platforms?

Recommendations for future studies are summarized on pages 128-129.

04/01/15 | CONTRACT NO.N00014-11-1-0391 121CONCLUSION

Findings 122

Interpretations 124

Recommendations 128


Findings9

19% 21% 8% 31% 20%

22% 15%5% 35% 22%

656 kWh

686 kWh

714 kWh

472 kWh

289 kWh

159 kWh

1087 kWh

1067 kWh

706 kWh

691 kWh

A HO

A H,O

A,O H

H,A,O

AO3452 kWh

3074 kWh




Demand Models


Plu

gs W

E

Ext

erio

r Li

ght

ing W

E

Inte

rio

r Li

ght

ing W

E

Fans

W

E

Mec

h.

Co

olin

g W

E

TOTA

L W

E


EW

W E

EW

EW











9 Interpretations

Top 3 Energy Usage Groups by MonthMar ‘13 Apr ‘13 May ‘13 Jun ‘13 Jul ‘13 Aug ‘13 Sep ‘13 Oct ‘13 Nov ‘13 Dec ‘13 Jan ‘14 Feb ‘14 TOTAL

Wes

t 1 Ext. L. Ext. L. Fans Ext. L. Ext. L. Mech. Mech. Mech. Fans Ext. L. Ext. L. Ext. L.

2 Plugs Plugs Ext. L. Plugs Plugs Fans Fans Ext. L. Mech. Int. L. Mech. Fans

3 Fans Fans Plugs Fans Fans Ext. L. Ext. L. Fans Ext. L. Plugs Int. L. Plugs

Eas

t 1 Ext. L. Ext. L. Plugs Ext. L. Ext. L. Mech. Mech. Mech. Mech. Mech. Ext. L. Ext. L. Ext. L.

2 Plugs Plugs Ext. L. Plugs Plugs Fans Fans Ext. L. Ext. L. Ext. L. Plugs Plugs Plugs

3 Fans Fans Fans Fans Ext. L. Ext. L. Fans Plugs Plugs Fans Int. L. Mech.

Avg T [°F] 68.7 71.6 72.8 74.9 76.0 76.6 76.7 74.8 72.3 71.7 68.8 69.9 72.9

The table above lists the top 3 highly used energy groups per month for each building. The color scheme is the same from Section 6:

Mechanical Cooling Fans Interior Lighting Exterior Lighting Plugs Louvers

The November 2013 data is excluded due to a likely sensor error, as mentioned in ‘Notes’ on page 19. At the bottom of the table, there are monthly average temperatures recorded from the rooftop weather station. Other measured weather station data can be seen in Section B of the Appendix.

The following interpretations are broken up by time of year, based on both the school calendar and the seasons of Līhu’e, Kauai.

Charts 6.01, on pages 74-75, show the daily average energy use values in [kWh/day] for each month.


Consistent Year-round Load Patterns Consistent overnight load pattern throughout year (Findings 8.01, 3.01)

Consistent energy use throughout day/night all year (Interpretations 6, Charts 6.01-6.13)

2012-2013 School Year, Spring (Mar - May ‘13)

Fans used more often than AC during these months (Interpretations 7, Charts 7.03-7.04 & 6.02-6.04)

Summer Break (Jun - Jul ‘13)

Minimal daytime use. Majority of energy use from consistent year-round loads: exterior lighting and plugs (Charts 6.05-6.06)

2013-2014 School Year, Summer (Aug - Sep ‘13)

AC used the most energy during these two months, which were also the warmest and also the months with highest total energy use (Charts 6.07-6.08)

Fan usage was also the highest during these two months for both buildings (Charts 7.03-7.04 & 6.07-6.08)

2013-2014 School Year, Fall (Oct - Dec ‘13)

Despite the fact that the average temperatures during these months were close to those of the previous Spring, the AC usage was much higher during these months (Charts 7.03 & 6.09-6.11)

2013-2014 School Year, Winter (Jan - Feb ‘14)

These were the first months when interior lighting use was among the top 3 energy use groups (Charts 6.12-6.13)

Fans

Ext. L.

Int. L.

Mech.

Plugs

Fans

Mech.


Interpretations9West/East Platform Comparison: Is performance consistent across platforms?Despite the identical construction of the two Hale Akamai buildings, this study has shown that their energy demand patterns differed. The contrasts in these patterns give clues as to how the buildings were utilized differently, and how those occupant decisions ultimately affected the buildings’ energy use totals.

The most obvious example is the difference in overall energy use between the two school years. As seen in charts 6.01, In April ‘13 and May ‘13 the East building used more energy than the West. But then for every month beginning in Aug ‘13 until the end of the study period (February ‘14), the West building used more energy than the East. This was mainly caused by a shift in AC usage patterns in the West building. Before the summer, the West building rarely used AC; roughly a quarter as much was used by the East building. However, the increase in West building AC use before and after summer (comparing May ‘13 to August ‘13) was twentyfold. Then West building AC usage continued at the higher level for the remainder of the study period.

Along with AC use, fan use was the other main cause for the highest energy months in summer. The West building also used more energy for fans than the East building. In the highest energy usage months (August ‘13 and September ‘13), the percent differences were as high as 45% and 77%. This was despite the fact that the AC use was similar for both buildings.

Lighting usage also generally increased over the course of the study period. Between April ‘13 and October ‘13, the average daily interior lighting energy use was 0.5 kWh/day for the West building and 0.1 kWh/day for the East. Then between November ‘13 and February ‘14, the average daily interior lighting energy use was 1.9 kWh/day for the West building and 1.1 kWh/day for the East building. The East building interior lighting began to be used consistently during school hours starting in November ‘13. Even the exterior lighting, which had a constant daily profile throughout the enitre study period, increased steadily. The West building daily average exterior lighting energy use was 37% higher in February ‘14 than in March ‘13. The East building daily average exterior lighting energy use was 30% higher in February ‘14 than in April ‘13.

One possible explaination for the increased energy usage across all systems is that the building occupants gradually stopped taking advantage of the energy efficient features of the building. The case of louver usage strongly suggests this. For both buildings, April ‘13, which was the first full month of occupancy, was the month with highest louver use, as seen in chart 7.05. But then louver use in the West building ceased after Aug ‘13, with the exception of small energy totals in Dec ‘13 and Feb ‘14. Louver use in the East building continued, but at a small rate. This drop in louver use didn’t coincide with a drop in AC or fan use, so the buildings were still in need of cooling.

04/01/15 | CONTRACT NO.N00014-11-1-0391 127SECTION TITLECONCLUSION

What are the possibilities for reducing energy demand?Based on the discussion from the previous page, it is difficult to predict whether building occupants will fully take advantage of all the energy reducing features of a building. Therefore, when looking for possibilities for energy demand reduction, it’s easier to go after predictable, repetitive patterns of energy demand which don’t rely on occupant decisions. An example in this case would be nighttime exterior lighting.

Nighttime exterior lighting accounted for roughly a third of overall energy use for both buildings. As seen in charts 3.03 - 3.05, the exterior lighting load profile stayed the same for all days, including weekends and holidays.

There were also several months when fans in the West building were left on overnight. The three months with the highest overnight fan energy use (August ‘13, September ‘13 and December ’13), were also the three highest energy use months for the West building. This can be seen in charts 6.07, 6.08 and 6.11.

Finally, the plug load was consistent throughout the night and day throughout the entire study period. However, without knowing the details of what is being plugged in, it is difficult to find strategies for reducing demand.

In terms of looking for ways to reduce demand during school hours, one approach is to look back at months or periods of time when energy demand was comparative low. For example, February ‘14 had the lowest energy use among “complete” months for the East building. However, for the West building more energy was used in February ‘14 than in April ‘13 or May ‘13. The load profiles in Charts 6.13 show that the discrepancy in energy use between buildings occured in the afternoon. In the West building, AC and fan use increased in the afternoon while interior lighting use continued from the morning. Meanwhile in the East building, AC, fan and interior lighting use ceased between 3:00 PM and 5:00 PM. Although more information about class schdules and building use is needed to make specific recommendations, this approach gives clues as to where or when to look for proven strategies.

There are other examples of energy demand reduction possibilies suggested by the data. From August to October 2013, there were lunchtime dips in AC use in the East building, but not in the West building. This can be seen in charts 6.07 - 6.09. Next, the air supply temperature profiles reveal that AC use stopped at 4:00 PM on average in the West building but at 3:00 PM on average in the East building. This can be seen in charts 7.02.


Recommendations9

RECOMMENDATION 2Investigate use of Fans / AC / Louvers interaction to determine operating states and auto/manual triggering

DESCRIPTION OF ISSUE/OPPORTUNITYBoth Frogs appear to be using more than the Optimal model AC energy and less than the Optimal model Fan energy. Both platforms were designed to use fans and louvers up and until a specified setpoint at which the louvers closes and the AC system turns on. The current study attempted to use the Louvers energy use as an indicator of use, but the power demand was so small that system activation did not necessarily align with system use -- and it did not indicate status of the louver as open or closed. This hampered efforts to understand how louvers and fan use correspondended to AC use.

STRATEGIES & TACTICS1 - Observe louvers / fan / AC states through manual data collection

Direct a building administrator to evaluate system ‘states’ five times a day for 2 weeks - 1month to confirm system actions and interactions

2 - Add state sensors to louvers to determine when are in closed/open positionInstall state sensors to each louvers in independent combination and connect sensor to building CPU and/or collect manually at intervals

RECOMMENDATION 1Investigate Air Supply Distribution differences between W & E Frog

DESCRIPTION OF ISSUE/OPPORTUNITYThe W Frog showed greater than 10% increases in distribution temperature more than 50% of the time, particularly during later months in the study. This may represent a problem in system operations and should be investigated to ensure optimal performance of cool air distribution.

STRATEGIES & TACTICS1 - Conduct site visit to test distribution temperatures at various locations to determine the cause of the temperature rise

Turn system on and manually measure supply temperature and output temperature. Walk length of distribution and visually identify disturbances to plenum that may be causing changes in temperature.

2 - Add additional sensors to determine location and triggers of issues over timeInstall additional air temperature sensors at every 10’ to isolate problem zones


RECOMMENDATION 3Adjust Thermal Comfort (TC) model assumptions and verify

DESCRIPTION OF ISSUE/OPPORTUNITYThe thermal comfort model assumptions used in this report were based on the best understanding of user occupant behavior.

Clothing insulation of 0.35 (t-shirt & shorts)Metabolic rate: 65W/m2 (sitting, light activity)

However, using these assumptions led to thermal comfort models that showed user occupants turning AC on when the conditions were already below the comfort guidelines in the platform. These findings do not make sense and need to be investigated.

STRATEGIES & TACTICS1 - Adjust thermal comfort model assumptions to fit curvature to inferred user comfort response behavior based on use of AC.

Clothing insulation - 0.50 (Knee-length skirt, short-sleeved shirt, sandals)Metabolic rate: 95W/m2 (standing, light activity)See Appendix for how these assumptions were determined and exhibit 9.02)

2 - Confirm user TC satisfaction through additional methodsSend out user surveys five times a day for 2 weeks - 1 month to confirm comfort / discomfort at various times of day and across weather conditions

RECOMMENDATION 4Investigate user behavior of platforms related to occupancy and building system manipulation

DESCRIPTION OF ISSUE/OPPORTUNITY4 of 9 performance measures showed greater than 20% variation between the E & W Frog platforms. Since the platforms share the same orientation, microclimate, and facility attributes, it is inferred that a portion of the variation is attributable to user behavior. Currently user behavior is completely inferred through building system usage patterns as measured through energy use over time. However, these measurements don’t distinguish between manual and automatic system responses and they can mislead perceptions of active hours when systems are inadvertently left on over time. A better understanding of user behavior will help confirm what variation is attributable to building design / system ops versus user behavior.

STRATEGIES & TACTICS1 - Collect building scheduling information and attendance records

Select an administrator to provide building scheduling and attendance information at each cycle period during the school year

2 - Collect automatic system setpoints and control logicSend out user surveys five times a day for 2 weeks - 1 month to confirm comfort / discomfort at various times of day and across weather conditions

3 - Install occupancy/utilization sensors to determine building active hours Install infrared occupancy sensors at building entrance/exits and connect to building CPU for data aggregation and communication


Acknowledgments

The present work benefited from the input of Dr. Sara Cerri, ONRG grantee (ONRG grant N62909-13-1-N233, Task 2.a)from Cureggio, Italy, who provided additional data analysis, specifically related to the monthly load profiles.

04/01/15 | CONTRACT NO.N00014-11-1-0391 131ACKNOWLEDGMENTs

OCTOBER 2014

TASK 4.2 FINAL REPORT APPENDIXKAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL

PREPARED BY:

MKThinkMark R. Miller, AIA LEEDAP

CEO, Director of Innovation Services

PREPARED UNDER CONTRACT TO:

Office of Naval ResearchDr. Richard CarlinDepartment Head, Code 33

PREPARED FOR:

Hawaii Natural Energy InstituteUniversity of Hawaii at ManoaDr. Rick RocheleauDirector

TASK 4.2 FINAL REPORT APPENDIX- KAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL

04/01/15 | CONTRACT NO.N00014-11-1-0391 TABLE OF CONTENTS 3

Table of Contents

A Solar Radiation 4

B Weather Summary 8

C Performance Category Extremes 26

D Weather Extremes 46

E A-E Relationships 72

4 TASK 4.2 FINAL REPORT APPENDIX- KAWAIKINI NEW CENTURY PUBLIC CHARTER SCHOOL

The annual actual horizontal solar radiation incident on the test platform roof and compare to annual solar radiation measured at Lihue Airport for a previous 5 year period.

A pyranometer was installed on a weather station at Kawaikini to measure onsite solar radiation and insolation. The data was collected at 5-minute intervals.

No calculations were performed on the solar irradiance (W/m2) data. To calculate solar insolation (Wh/m2) each irradiance data point was multiplied by (5/60) since it was assumed that each data point was constant for the preceding 5 minute period. These values were then summed cumulatively to yield a running solar energy total in Watt-hours per square meter.

The sensor has a maximum around 1277 W/m2, which was confirmed by the manufacturer.

Measured cumulative solar insolation was potentially less than modeled due to sensor maximum value threshold.

Solar RadiationA

04/01/15 | CONTRACT NO.N00014-11-1-0391 5SOLAR RADIATION

A-0.1 Solar Radiation, April 2013 - March 2014 vs. Model 6

A-0.2 Solar Radiation, 24-Hour Average 7


Solar Radiation, April 2013 - March 2014 vs. ModelA-0.1

ObservedModel (2009-2010 solar radiation)

Cum

ulat

ive

Inso

lati

on

[Pea

k S

un H

our

s]

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr2013 - 2014

Findings• Available solar radiation on site during the study period averaged 4.6 kWh/m2 (4.6 peak sun hours) and was 89% of the mod-

eled value. Measured cumulative solar insolation was potentially less than modeled due to sensor maximum value threshold.

04/01/15 | CONTRACT NO.N00014-11-1-0391 7SOLAR RADIATION

Solar Radiation, 24-Hour Average*A-0.2

Findings• Q2 had the highest available solar radiation, followed by (in decreasing order): Q1, Q4, Q3* Solar radiation sensor maximum detection limit of 1277 W/m2 (confirmed by manufacturer)

1277 761.456 786.739 609.381 639.287

0.6 1 0.641 0.842 1

201.317 209.033 227.177 149.969 162.513

310.932 257.225 282.769 207.492 224.089

CLIENT HNEISHEET #

PROJECT 01 HNEI Part One- Kawaikini NCPCS

CHART Kawaikini Q4 Observed Solar IrradianceDATE - UPDATE 07/10/2014

PREPARED BY admin

DIMENSIONS 1d 2d 3d T V SCALE

DATA INPUTS

Observed Q1 Average Q2 Average Q3 Average Q4 AverageITEM

ATTRIBUTESensor (1/128) - Solar Radiation

(W/m^2)

Sensor (1/128) - Solar Radiation

(W/m^2)


(W/m^2)


(W/m^2)


(W/m^2)

MEAS. DEVICEKawaikini - Weather Station - Solar

Rad

Kawaikini - Weather Station - Solar

Rad


Rad


Rad


Rad

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - - - - -

Irrad

ianc

e

Observed

Q1 Average

Q2 Average

Q3 Average

Q4 Average

00:0

006

:00

12:0

018

:00

00:0

003

:00

09:0

015

:00

21:0

0-‐‑250

0

250

500

750

1000

1250

1500

DATA OUTPUTS

STAT Observed Q1 Average Q2 Average Q3 Average Q4 AverageMAX

MIN

AVE

STDEV

FINDINGS

NOTES

2013-03-21 2014-03-15 2013-03-21 2013-05-28 2013-05-29 2013-11-04 2013-11-05 2013-12-21 2013-12-22 2014-03-15


This section includes an analysis of environmental conditions to identify dominant microclimate typologies, annual averages, monthly averages, and environmental extremes.

Weather SummaryB

04/01/15 | CONTRACT NO.N00014-11-1-0391 9WEATHER SUMMARY

B-1.1 Outdoor Temperature, Histogram 10

B-1.2 Outdoor Temperature, 24-Hour Averages 11

B-1.3 Outdoor Temperature, Monthly Averages 12

B-2.1 Outdoor Relative Humidity, Histogram 13

B-2.2 Outdoor Relative Humidity, 24-Hour Averages 14

B-2.3 Outdoor Relative Humidity, Monthly Averages 15

B-3.1 Solar Radiation, Histogram 16

B-3.2 Solar Radiation, 24-Hour Averages 17

B-3.3 Solar Radiation, Monthly Averages 18

B-4.1 Wind Speed, Histogram 19

B-4.2 Wind Speed, 24-Hour Averages 20

B-4.3 Wind Speed, Monthly Averages 21

B-5.1 Wind Direction, Histogram 22

B-5.2 Wind Direction, 24-Hour Averages 23

B-5.3 Wind Direction, Monthly Averages 24


Outdoor Temperature, HistogramB-1.1%

of

dat

a

0

0.5

1.0

1.5

2.0

2.5

3.0

Temperature [°F]55 60 65 70 75 80 85


Outdoor Temperature, 24-Hour AveragesB-1.2

Hottest Day (8/25/13)Coldest Day (1/23/14)Annual Average

Tem

per

atur

e [°

F]

55

60

65

70

75

80

85

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Outdoor Temperature, Monthly AveragesB-1.3A

vera

ge

Tem

per

atur

e [°

F]

67

68

69

70

71

72

73

74

75

76

77

78

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb2013 - 2014


Outdoor Relative Humidity, HistogramB-2.1%

of

dat

a

0

1

2

3

4

5

6

Relative Humidity [%]40 45 50 55 60 65 70 75 80 85 90 95 100


Outdoor Relative Humidity, 24-Hour AveragesB-2.2

Most Humid Day (2/14/14)Least Humid Day (1/22/14)Annual Average

Rel

ativ

e H

umid

ity

[%]

45

50

55

60

65

70

75

80

85

90

95

100

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Outdoor Relative Humidity, Monthly AveragesB-2.3A

vera

ge

Rel

ativ

e H

umid

ity

[%]

81

82

83

84

85

86

87

88

89

90

91

92

93



Solar Radiation, HistogramB-3.1%

of

dat

a

0

0.5

1.0

1.5

2.0

2.5

3.0

Solar Irradiance [W/m2]0 200 400 600 800 1000 1200


Solar Radiation, 24-Hour AveragesB-3.2

Sunniest Day (7/5/13)Least Sunny Day (2/14/14)Annual Average

So

lar

Irra

dia

nce

[W/m

2 ]

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Solar Radiation, Monthly AveragesB-3.3A

vera

ge

So

lar

Rad

iati

on

[W/m

2 ]

300

320

340

360

380

400

420

440

460



Wind Speed, HistogramB-4.1%

of

dat

a

0

2

4

6

8

10

12

14

Wind Speed [m/s]0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5


Wind Speed, 24-Hour AveragesB-4.2

Windiest Day (11/10/13)Least Windy Day (12/21/13)Annual Average

Win

d S

pee

d [

m/s

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Wind Speed, Monthly AveragesB-4.3A

vera

ge

Win

d S

pee

d [

m/s

]

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0



Wind Direction, HistogramB-5.1

Trade Winds

% o

f d

ata

0

1

2

3

4

5

6

7

8

9

10

11

Wind Direction [degrees]0 90 180 270 360


Wind Direction, 24-Hour AveragesB-5.2

NortherlyEasterlySoutherlyWesterlyAnnual Average

Win

d S

pee

d [

m/s

]

0.5

1.0

1.5

2.0

2.5

3.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00


Wind Direction, Monthly AveragesB-5.3

WesterlySoutherlyEasterlyNortherly

% o

f Ti

me

0

10

20

30

40

50

60

70

80

90

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


This page is intentionally blank.


This section looks at days where performance cateogries were within 5% of their annual maximum or minimum (extreme performance days). Then the percentage differences in weather attributes (compared to the annual average) on those days were examined to understand the magnitude of their potential contribution to the measured performance. category.

Charts on the left side are for the West Frog, and charts on the right side are for the East Frog.

Performance Category ExtremesC

04/01/15 | CONTRACT NO.N00014-11-1-0391 27PERFORMANCE CATEGORY EXTREMES

C-0.1 Total Energy Use [W/E] 28

C-0.2 Mechanical Cooling Energy Use [W/E] 30

C-0.3 Fan Energy Use [W/E] 32

C-0.4 Lighting Energy Use [W/E] 34

C-0.5 Plug Energy Use [W/E] 36

C-0.6 Thermal Comfort [W/E] 38

C-0.7 Carbon Dioxide [W/E] 40

C-0.8 Air Supply [W/E] 42

C-0.9 Daylighting [W/E] 44

W E


Total Energy UseC-0.1

5.6

−8.1

4.8 2.7

−2.4

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60

Air Temperature Relative Humidity Solar Radiation Wind Speed Wind Direction

W E


9.8

−5.1

4.41.6

−12.2

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Mechanical Cooling Energy UseC-0.2

18.3

−9.0

6.11.4

−6.7

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


5.90

2.80.1

−6.9

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Fan Energy UseC-0.3

16.3

−8.8

5.01.4

−9.8

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


9.0

−3.1

3.7 5.0

−9.5

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Lighting Energy UseC-0.4

−11.9

10.7

−4.6 −5.8

16.9

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


−7.7

17.2

−7.3 −8.1

19.5

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Plug Energy UseC-0.5

12.0

−3.8

4.1 4.1

−17.2

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


9.3

−7.6

4.5 4.3

−18.9

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ge

[%]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Thermal ComfortC-0.6

−9.82−5.18

−8.93−14.76

31.96

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


−10.62−4.94

−9.95

−17.09

39.39

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Carbon DioxideC-0.7

-1.67

5.04

-23.87

-42.88

57.79

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−50

−40

−30

−20

−10

0

10

20

30

40

50

60

70


W E


-2.59

6.58

-32.32

-41.76

38.79

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−50

−40

−30

−20

−10

0

10

20

30

40

50

60

70


W E


C-0.8 Air Supply

5.47

−6.39

39.51

10.89

−24.13

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


−2.54

3.30

−5.67 −5.00 −4.34

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


C-0.9 Daylighting

−4.04

9.34

−57.32

−13.47

25.93

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−70

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50


W E


-3.74

8.38

-59.97

-4.96

16.36

Diff

eren

ce o

f W

eath

er A

ttri

but

es o

n E

xtre

me

Hig

h D

ays

as

Co

mp

ared

to

the

Ann

ual A

vera

ges

[%

]

−70

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50



This section looks at days where weather metrics were within 5% of their annual maximum or minimum (extreme weather days). The extreme weather days were compared against annual averages. Then the building performance categories on those days were examined to understand how the buildings were impacted on days of extreme weather events.

The active days charts show the percentage difference in performance attributes on extreme weather days as compared to the annual average.

Active days charts on the left side are for the West building, and active days charts on the right side are for the East building.

Weather ExtremesD

04/01/15 | CONTRACT NO.N00014-11-1-0391 47WEATHER EXTREMES

D-1.1 Outdoor Air Temperature, Extremes 48

D-1.2 Outdoor Air Temperature, Extreme 24-Hour Averages 49

D-1.3 Hottest Active Days [W/E] 50

D-1.4 Coldest Active Days [W/E] 52

D-2.1 Outdoor Relative Humidity, Extremes 54

D-2.2 Outdoor Relative Humidity, Extreme 24-Hour Averages 55

D-2.3 Most Humid Active Days [W/E] 56

D-2.4 Least Humid Active Days [W/E] 58

D-3.1 Solar Radiation, Extremes 60

D-3.2 Solar Radiation, Extreme 24-Hour Averages 61

D-3.3 Sunniest Active Days [W/E] 62

D-3.4 Cloudiest Active Days [W/E] 64

D-4.1 Wind Speed, Extremes 66

D-4.2 Wind Speed, Extreme 24-Hour Averages 67

D-4.3 Windiest Active Days [W/E] 68

D-4.4 Least Windy Active Days [W/E] 70


Outdoor Air Temperature, ExtremesD-1.1

82.5

73.0

61.2

5% Highest

Average

5% Lowest

Tem

per

atur

e [°

F]

60

62

64

66

68

70

72

74

76

78

80

82

84


Outdoor Air Temperature, Extreme 24-Hour AveragesD-1.2

Annual Average5% Hottest Days5% Coldest Days

Tem

per

atur

e [°

F]

60

65

70

75

80

85

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

W E


Hottest Active DaysD-1.3

22.7

-6.75

5.14

21.2

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60

Total Power CO2 Supply Air Ceiling Illuminance

W E


20.5

-5.7

5.7

15.4

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Coldest Active DaysD-1.4

1.4

11.2

6.9

0.6

20.6

1.2

22.9

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50

AC Power Fan Power Light Power CO2 Supply Air Illuminance PMV

Kawaikini West Frog Performance Statistics for 5% Coldest Active Days

W E


1.46.0

2.0 0.5

13.2

1.4

48.5

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini East Frog Performance Statistics for 5% Coldest Active Days


Outdoor Relative Humidity, ExtremesD-2.1

100

86.6

63.9

5% Highest

Average

5% Lowest

Rel

ativ

e H

umid

ity

[%]

60

65

70

75

80

85

90

95

100

105


Outdoor Relative Humidity, Extreme 24-Hour AveragesD-2.2

Annual Average5% Most Humid Days5% Least Humid Days

Rel

ativ

e H

umid

ity

[%]

60

65

70

75

80

85

90

95

100

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

W E


Most Humid Active DaysD-2.3

-3.89

9.37

-3.22

-57.5

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−70

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50


W E


-2.3

12.0

-3.5

-49.9

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−70

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50


W E


Least Humid Active DaysD-2.4

0.9

6.2

5.94.0 4.9

0.7 0.8

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini West Frog Performance Statistics for 5% Least Humid Active Days

W E


0.5

1.8 1.6 0.7 0.60.1

1.1

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini East Frog Performance Statistics for 5% Least Humid Active Days


Solar Radiation, ExtremesD-3.1

295

198

68

5% Highest

Average

5% Lowest

So

lar

Rad

iati

on

[W/m

2 ]

50

100

150

200

250

300


Solar Radiation, Extreme 24-Hour AveragesD-3.2

Annual Average5% Sunniest Days5% Cloudiest Days

So

lar

Irra

dia

nce

[W/m

2 ]

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

W E


Sunniest Active DaysD-3.3

56.7

4.60

-0.257

44.0

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−40

−30

−20

−10

0

10

20

30

40

50

60

70

80


W E


71.1

3.7

-1.2

37.8

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−40

−30

−20

−10

0

10

20

30

40

50

60

70

80


W E


Cloudiest Active DaysD-3.4

1.1

10.3

10.0

3.0

8.6

3.3

21.9

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini West Frog Performance Statistics for 5% Cloudiest Active Days

W E


0.85.3

13.4

4.2

7.02.3

23.8

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini East Frog Performance Statistics for 5% Cloudiest Active Days


Wind Speed, ExtremesD-4.1

3.71

1.33

0.19

5% Highest

Average

5% Lowest

Win

d S

pee

d [

m/s

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


Wind Speed, Extreme 24-Hour AveragesD-4.2

Annual Average5% Windiest Days5% Least Windy Days

Win

d S

pee

d [

m/s

]

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

W E


Windiest Active DaysD-4.3

-1.79-4.79

-1.09-5.19

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


9.3

-4.0-1.2

-7.6

Diff

eren

ce o

f P

erfo

rman

ce A

ttri

but

es o

n E

xtre

me

Wea

ther

Day

s a

s C

om

par

ed t

o t

he A

nnua

l Ave

rag

e [%

]

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60


W E


Least Windy Active DaysD-4.4

0.2

3.8

7.3 8.5

6.82.3

7.0

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini West Frog Performance Statistics for 5% Least Windy Active Days

W E


1.1 2.3

4.26.3

0.4

2.2

9.0

Dev

iatio

n fr

om A

nnua

l Ave

rage

[% o

f max

dev

iatio

n]

50

40

30

20

10

0

10

20

30

40

50


Kawaikini East Frog Performance Statistics for 5% Least Windy Active Days


A-E Relationships

This section looks at the correlation strength between building performance (assets, A) and changes in weather (environmental conditions, E). These A-E relationships indicate what metrics affect each other in context of the building performance. In addition, the clustering of data points reveals how building performance responds to the full variation of weather conditions, particularly where building systems limit performance along operational thresholds (e.g. power ratings).

The data points are daily averages.

Coefficient of determination (r2) values above 0.10 are listed in the findings. Correlations calculated but below 0.10 are not noted in the findings.

E

04/01/15 | CONTRACT NO.N00014-11-1-0391 73A-E RELATIONSHIPS

vs.E-#.1 E-#.2 E-#.3 E-#.4 E-#.5 E-#.6 E-#.7

Panel Feed Power [W/E]

Condensing Unit Power

[W/E]

Ceiling Fans Power

[W/E]

Main Light-ing Power

[W/E]

Predicted Mean Vote

[W/E]

Carbon Dioxide [W/E]

Air Supply Temperature

[W/E]

1Outdoor

Air Temperature

74 76 78 80 82 84 86

2Outdoor Relative Humidity

88 90 92 94 -- 96 98

3 Solar Radiation 100 102 104 106 108 110 112

4 Wind Speed -- -- -- -- -- 114 116

5 Rainfall 118 120 122 124 -- -- --

W E


Outdoor Air Temperature vs. Panel Feed PowerE-1.1

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Panel Feed Power vs. Outdoor Air Temperature (DailyAverages)

DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Panel Feed Power [kW]ITEM

ATTRIBUTE Sensor (1/128) - Air Temperature (°F) Sensor (1/32) - Power (kW)

MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini West - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Outdoor Air Temp [°F]

Panel Feed P

ow

er [

kW

]

R squared = 0.05

64 66 68 70 72 74 76 780

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Panel Feed Power vs. Outdoor Air Temperature (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Panel Feed Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini East - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Panel Feed P

ow

er [

kW

]

R squared = 0.04

64 66 68 70 72 74 76 780

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Outdoor Air Temperature vs. Condensing Unit PowerE-1.2

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Condensing Unit Power vs. Outdoor Air Temperature(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini West - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.15

64 66 68 70 72 74 76 782.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Condensing Unit Power vs. Outdoor Air Temperature(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini East - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.44

68 69 70 71 72 73 74 75 76 77 782.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Outdoor Air Temperature vs. Ceiling Fans PowerE-1.3

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Ceiling Fans Power vs. Outdoor Air Temperature (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini West - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.27

64 66 68 70 72 74 76 78‑0.2

0

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Ceiling Fans Power vs. Outdoor Air Temperature (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini East - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.16

66 67 68 69 70 71 72 73 74 75 76 77 78

0

0.2

0.4

0.6

0.8

‑0.2

1

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Outdoor Air Temperature vs. Main Lighting PowerE-1.4

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Main Lighting Power vs. Outdoor Air Temperature(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini West - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.03

64 66 68 70 72 74 76 780

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Main Lighting Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Main Lighting Power vs. Outdoor Air Temperature (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini East - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.02

65 66 67 68 69 70 71 72 73 74 75 76 77 780

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Main Lighting Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Outdoor Air Temperature vs. Predicted Mean VoteE-1.5

R squared = 0.646

PM

V

−5

−4

−3

−2

−1

0

1

Outdoor Temperature [°F]62 64 66 68 70 72 74 76 78 80

W E


R squared = 0.844

PM

V

−5

−4

−3

−2

−1

0

1

Outdoor Temperature [°F]62 64 66 68 70 72 74 76 78 80

W E


Outdoor Air Temperature vs. Carbon DioxideE-1.6

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 CO2 vs. Outdoor Air Temperature (Daily Averages)DATE - UPDATE 09/09/2014

PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Air Temperature (°F) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini West - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.07

64 66 68 70 72 74 76 78300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Outdoor Air Temp [°F] CO2 [ppm]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 CO2 vs. Outdoor Air Temperature (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Air Temperature (°F) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini East - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.08

64 66 68 70 72 74 76 78300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Outdoor Air Temp [°F] CO2 [ppm]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Outdoor Air Temperature vs. Air Supply TemperatureE-1.7

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Air Supply Temperature vs. Outdoor Air Temperature(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Air Temperature (°F) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini - West - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.56

64 66 68 70 72 74 76 7855

60

65

70

75

80

85

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Air Supply Temperature [°F]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Air Supply Temperature vs. Outdoor Air Temperature(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Air Temp [°F] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Air Temperature (°F) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Air Temp Kawaikini - East - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.48

64 66 68 70 72 74 76 7855

60

65

70

75

80

85

DATA OUTPUTS

STAT Outdoor Air Temp [°F] Air Supply Temperature [°F]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


E-2.1 Outdoor Relative Humidity vs. Panel Feed Power

99.953 1.282

68.29 0.162

86.645 0.416

5.479 0.251

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Panel Feed Power vs. Outdoor Relative Humidity (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Panel Feed Power [kW]ITEM

ATTRIBUTE Sensor (1/128) - Relative Humidity (%) Sensor (1/32) - Power (kW)

MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini West - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Outdoor Relative Humidity [%]

Panel Feed P

ow

er [

kW

]

R squared = 0.00

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

0

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Panel Feed Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


99.953 1.446

68.29 0.166

86.65 0.382

5.456 0.257

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Panel Feed Power vs. Outdoor Relative Humidity (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Panel Feed Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini East - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Panel Feed P

ow

er [

kW

]

R squared = 0.00

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

0kW

0.25kW

0.5kW

0.75kW

1kW

1.25kW

1.5kW

1.75kW

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Panel Feed Power [kW]

MAX % kW

MIN % kW

AVE % kW

STDEV % kW

FINDINGS

NOTES

W E


Outdoor Relative Humidity vs. Condensing Unit PowerE-2.2

99.953 5.135

74.716 3.3

87.3 3.728

5.008 0.198

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Condensing Unit Power vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini West - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.04

7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

2.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Condensing Unit Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


95.853 4.429

75.469 2.79

86.078 3.445

4.321 0.296

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Condensing Unit Power vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini East - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.23

76 78 80 82 84 86 88 90 92 94 962.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Condensing Unit Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


Outdoor Relative Humidity vs. Ceiling Fans PowerE-2.3

99.953 0.744

72.722 0.02

87.036 0.23

5.067 0.15

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Ceiling Fans Power vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini West - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.10

72.5 75

77.5 80

82.5 85

87.5 90

92.5 95

97.5 10

00

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Ceiling Fans Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


99.699 0.807

75.469 0.01

87.218 0.297

4.677 0.139

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Ceiling Fans Power vs. Outdoor Relative Humidity (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini East - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.06

77.5 80

82.5 85

87.5 90

92.5 95

97.5

0kW

0.2kW

0.4kW

0.6kW

0.8kW

1kW

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Ceiling Fans Power [kW]

MAX % kW

MIN % kW

AVE % kW

STDEV % kW

FINDINGS

NOTES

W E


E-2.4 Outdoor Relative Humidity vs. Main Lighting Power

99.953 0.71

72.722 0.01

87.864 0.427

5.211 0.172

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Main Lighting Power vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini West - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.00

72.5 75

77.5 80

82.5 85

87.5 90

92.5 95

97.5 10

00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Main Lighting Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


99.953 0.712

75.507 0.048

89.533 0.583

5.237 0.158

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Main Lighting Power vs. Outdoor Relative Humidity(Daily Average)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini East - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.00

77.5 80

82.5 85

87.5 90

92.5 95

97.5 10

00kW

0.1kW

0.2kW

0.3kW

0.4kW

0.5kW

0.6kW

0.7kW

0.8kW

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Main Lighting Power [kW]

MAX % kW

MIN % kW

AVE % kW

STDEV % kW

FINDINGS

NOTES

W E


E-2.6 Outdoor Relative Humidity vs. Carbon Dioxide

99.953 600.799

68.29 347.846

86.645 402.119

5.479 42.099

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 CO2 vs. Outdoor Relative Humidity (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Relative Humidity (%) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini West - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.13

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


99.953 592.713

68.29 349.757

86.645 387.434

5.479 30.888

CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 CO2 vs. Outdoor Relative Humidity (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Relative Humidity (%) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini East - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.11

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


E-2.7 Outdoor Relative Humidity vs. Air Supply Temperature

99.953 83.012

68.29 64.538

86.645 75.731

5.479 3.293

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Air Supply Temperature vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Relative Humidity (%) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini - West - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.02

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


99.953 82.831

68.29 56.984

86.645 76.239

5.479 4.028

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Air Supply Temperature vs. Outdoor Relative Humidity(Daily Averages)


PREPARED BY rsamad


DATA INPUTS

Outdoor Relative Humidity [%] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Relative Humidity (%) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - RH Kawaikini - East - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.01

7072

.5 7577

.5 8082

.5 8587

.5 9092

.5 9597

.5 100

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Outdoor Relative Humidity [%] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


E-3.1 Solar Radiation vs. Panel Feed Power

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 Panel Feed Power vs. Solar Radiation (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Panel Feed Power [kW]ITEM

ATTRIBUTE Sensor (1/128) - Solar Radiation (W/m^2) Sensor (1/32) - Power (kW)

MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Solar Radiation [W/m^2]

Panel Feed P

ow

er [

kW

]

R squared = 0.00

50 100

150

200

250

300

350

400

450

500

550

600

0

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Panel Feed Power vs. Solar Radiation (Daily Averages)DATE - UPDATE 09/08/2014

PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Panel Feed Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini East - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Panel Feed P

ow

er [

kW

]

R squared = 0.01

50 100

150

200

250

300

350

400

450

500

550

600

0

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Solar Radiation vs. Condensing Unit PowerE-3.2

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Condensing Unit Power vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.06

100

200

300

125

150

175

225

250

275

2.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Condensing Unit Power vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini East - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.26

120

140

160

180

200

220

240

260

280

300

2.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Solar Radiation vs. Ceiling Fans PowerE-3.3

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Ceiling Fans Power vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.14

100

150

200

250

300

350

400

450

500

550

600

0

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Ceiling Fans Power vs. Solar Radiation (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini East - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.11

100

150

200

250

300

350

400

0

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Solar Radiation vs. Main Lighting PowerE-3.4

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Main Lighting Power vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.02

100

150

200

250

300

350

400

450

500

550

600

‑0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Main Lighting Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


624.667 0.71

55.781 0.01

188.886 0.427

64.463 0.172

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Main Lighting Power Use vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.02

100

150

200

250

300

350

400

450

500

550

600

‑0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Main Lighting Power [kW]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


Solar Radiation vs. Predicted Mean VoteE-3.5

R squared = 0.122

PM

V

−5

−4

−3

−2

−1

0

1

Solar Radiation [W/m2]0 50 100 150 200 250 300 350

W E


R squared = 0.227

PM

V

−5

−4

−3

−2

−1

0

1

Solar Radiation [W/m2]0 50 100 150 200 250 300 350

W E


Solar Radiation vs. Carbon DioxideE-3.6

624.667 600.799

45.84 343.13

199.318 399.905

64.944 42.363

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 CO2 vs. Solar Radiation (Daily Averages)DATE - UPDATE 09/08/2014

PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Solar Radiation (W/m^2) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini West - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.05

50 100

150

200

250

300

350

400

450

500

550

600

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Solar Radiation [W/m^2] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


624.667 588.597

45.84 344.113

199.318 385.136

64.944 31.099

CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 CO2 vs. Solar Radiation (Daily Averages)DATE - UPDATE 09/08/2014

PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Solar Radiation (W/m^2) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini East - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


CO

2 [

ppm

]

R squared = 0.06

50 100

150

200

250

300

350

400

450

500

550

600

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Solar Radiation [W/m^2] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


Solar Radiation vs. Air Supply TemperatureE-3.7

624.667 83.012

45.84 64.538

199.318 75.731

64.944 3.293

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Air Supply Temperature vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Solar Radiation (W/m^2) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini - West - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.18

50 100

150

200

250

300

350

400

450

500

550

600

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


624.667 82.831

45.84 56.984

199.318 76.239

64.944 4.028

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Air Supply Temperature vs. Solar Radiation (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Solar Radiation [W/m^2] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Solar Radiation (W/m^2) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Solar Rad Kawaikini - East - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -


Air

Supply

Tem

peratu

re [

°F]

R squared = 0.09

50 100

150

200

250

300

350

400

450

500

550

600

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Solar Radiation [W/m^2] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


Wind Speed vs. Carbon DioxideE-4.6

6.698 600.799

0.495 343.13

2.161 399.905

1.22 42.363

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 CO2 vs. Wind Speed (Daily Averages)DATE - UPDATE 09/09/2014

PREPARED BY rsamad


DATA INPUTS

Wind Speed [m/s] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Air Speed (m/s) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Wind Speed Kawaikini West - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Wind Speed [m/s]

CO

2 [

ppm

]

R squared = 0.19

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5 6

6.5

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Wind Speed [m/s] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


6.698 588.597

0.495 344.113

2.161 385.136

1.22 31.099

CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 CO2 vs. Wind Speed (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Wind Speed [m/s] CO2 [ppm]ITEM

ATTRIBUTE Sensor (1/128) - Air Speed (m/s) Sensor (1/8) - CO2 Concentration (ppm)

MEAS. DEVICE Kawaikini - Weather Station - Wind Speed Kawaikini East - Room Air CO2

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Wind Speed [m/s]

CO

2 [

ppm

]

R squared = 0.16

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5 6

6.5

300

350

400

450

500

550

600

650

DATA OUTPUTS

STAT Wind Speed [m/s] CO2 [ppm]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


Wind Speed vs. Air Supply TemperatureE-4.7

6.698 83.012

0.495 64.538

2.161 75.731

1.22 3.293

CLIENT HNEISHEET #


CHARTKawaikini West Frog Q4 Air Supply Temperature vs. Wind Speed (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Wind Speed [m/s] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Air Speed (m/s) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Wind Speed Kawaikini - West - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Wind Speed [m/s]

Air

Supply

Tem

peratu

re [

°F]

R squared = 0.23

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5 6

6.5

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Wind Speed [m/s] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


6.698 82.831

0.495 56.984

2.161 76.239

1.22 4.028

CLIENT HNEISHEET #


CHARTKawaikini East Frog Q4 Air Supply Temperature vs. Wind Speed (DailyAverages)


PREPARED BY rsamad


DATA INPUTS

Wind Speed [m/s] Air Supply Temperature [°F]ITEM

ATTRIBUTE Sensor (1/128) - Air Speed (m/s) Sensor (1/1) - Air Temperature (°F)

MEAS. DEVICE Kawaikini - Weather Station - Wind Speed Kawaikini - East - HVAC-End_temp_air

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Wind Speed [m/s]

Air

Supply

Tem

peratu

re [

°F]

R squared = 0.21

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5 6

6.5

55

60

65

70

75

80

85

DATA OUTPUTS

STAT Wind Speed [m/s] Air Supply Temperature [°F]

MAX %

MIN %

AVE %

STDEV %

FINDINGS

NOTES

W E


E-5.1 Rainfall vs. Panel Feed Power

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 Panel Feed Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Panel Feed Power [kW]ITEM

ATTRIBUTE Sensor (1/128) - Rainfall (in/5min) Sensor (1/32) - Power (kW)

MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini West - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Panel Feed P

ow

er [

kW

]

R squared = 0.01

0 1 2 3 4 5 6 7 80

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Rainfall [in/day] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Panel Feed Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Panel Feed Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini East - Panel Feed - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Panel Feed P

ow

er [

kW

]

R squared = 0.00

0 1 2 3 4 5 6 7 80

0.25

0.5

0.75

1

1.25

1.5

1.75

DATA OUTPUTS

STAT Rainfall [in/day] Panel Feed Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Rainfall vs. Condensing Unit PowerE-5.2

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 Condensing Unit Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini West - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.00

0 1 20.

25 0.5

0.75

1.25 1.

51.

752.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Rainfall [in/day] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Condensing Unit Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Condensing Unit Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini East - Condensing Unit - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Condensin

g U

nit

Pow

er [

kW

]

R squared = 0.07

00.

20.

40.

60.

8 11.

21.

42.5

3

3.5

4

4.5

5

5.5

DATA OUTPUTS

STAT Rainfall [in/day] Condensing Unit Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Rainfall vs. Ceiling Fans PowerE-5.3

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 Ceiling Fans Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini West - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.03

0 1 20.

25 0.5

0.75

1.25 1.

51.

750

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Rainfall [in/day] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Ceiling Fans Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Ceiling Fans Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini East - Ceiling Fans - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Ceilin

g F

ans P

ow

er [

kW

]

R squared = 0.01

0 1 20.

25 0.5

0.75

1.25 1.

51.

750

0.2

0.4

0.6

0.8

1

DATA OUTPUTS

STAT Rainfall [in/day] Ceiling Fans Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


Rainfall vs. Main Lighting PowerE-5.4

CLIENT HNEISHEET #


CHART Kawaikini West Frog Q4 Main Lighting Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini West - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.01

00.

5 11.

5 22.

5 33.

5 44.

50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Rainfall [in/day] Main Lighting Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

W E


CLIENT HNEISHEET #


CHART Kawaikini East Frog Q4 Main Lighting Power vs. Rainfall (Daily Averages)DATE - UPDATE 09/10/2014

PREPARED BY rsamad


DATA INPUTS

Rainfall [in/day] Main Lighting Power [kW]ITEM


MEAS. DEVICE Kawaikini - Weather Station - Rain Kawaikini East - Lighting Main Space - Power

SENSOR TYPE

SENSOR REF

TEST LOCATION

TEST INTERVAL

TEST DURATION - -

Rainfall [in/day]

Main

Lig

hti

ng P

ow

er [

kW

]

R squared = 0.00

00.

5 11.

5 22.

5 33.

5 44.

50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

DATA OUTPUTS

STAT Rainfall [in/day] Main Lighting Power [kW]

MAX

MIN

AVE

STDEV

FINDINGS

NOTES

hawaii energy and environmental technologies (heet) initiative · 2017-05-01 · 6 task 4.2 final...

Documents