simulation-powered building energy management and control system

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SIMULATION-POWERED BUILDING ENERGY MANAGEMENT AND CONTROL

SYSTEM

Sung Hong ParkB.A., University of California, Berkeley, 2005

THESIS

Submitted in partial satisfaction ofthe requirements for the degree of

MASTER OF SCIENCE

in

MECHANICAL ENGINEERING

at

CALIFORNIA STATE UNIVERSITY, SACRAMENTO

FALL2010


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2010

Sung Hong ParkALL RIGHTS RESERVED


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SIMULATION-POWERED BUILDING ENERGY MANAGEMENT AND CONTROLSYSTEM

A Thesis

by

Sung Hong Park

Approved by:

__________________________________, Committee ChairDr. Dongmei Zhou

__________________________________, Second ReaderDr. Akihiko Kumagai

____________________________Date


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Student: Sung Hong Park

I certify that this student has met the requirements for format contained in the University format

manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for

the thesis.

__________________________, Graduate Coordinator ___________________

Dr. Kenneth S. Sprott Date

Department of Mechanical Engineering


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Abstract

of

SIMULATION-POWERED BUILDING ENERGY MANAGEMENT AND CONTROL

SYSTEM

by

Sung Hong Park

As efficiency of the equipment improves, there is a need for more sophisticated control

over all equipment to achieve a global optimization. The idea of a simulation-powered building

energy management and control system (SPEMS) to achieve such optimization has interested the

building retrofit community since the 1980s, but there is still no such system available to

consumers today.

In this paper, a 200,000 square foot high-rise office building in San Jose, California, is

used to demonstrate the SPEMS. For this demonstration, DOE-2.2 building energy simulation

was used, and the building model was calibrated to the actual electricity meter interval data for

the year 2007.

The building simulation showed 2.25% energy reduction and the estimated payback time

was 11.4 years. This result is significantly different than the 20% energy reduction result by

Cumali et al. in 1988. The reduction of savings from SPEMS is due to system optimization done


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through smarter EMCS and higher building efficiency standard set by California Title 24

Building Code.

_______________________, Committee ChairDr. Dongmei Zhou

_______________________Date


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TABLE OF CONTENTSPage

List of Tables ................................................................................................................. ix

List of Figures................................ .......................... .......................... ......................... ....... x

Chapter

1. INTRODUCTION .......................... ......................... .......................... ......................... .... 1

1.1 Let There Be Green Industry ........................................................................ 1

1.2 Building Energy Efficiency vs. Renewable Energy Sources .............................. 3

1.3 Further Improvement on Building Energy Efficiency ............... ......................... 5

2. DOE BUILDING SIMULATION ........................ .......................... ......................... ........ 7

2.1 DOE-2.2 ........................... ......................... .......................... ......................... .... 7

2.2 Building Model.............................................. ......................... .......................... 9

3. SPEMS SIMULATION ............................................... ......................... ......................... 16

3.1 Simulation Tactics ........................ .......................... ......................... ................ 16

3.2 Indoor Temperature Set Point Schedule ........................... ......................... ....... 17

3.2.1 Lunchtime Pre-Cool ....................... .......................... ........................ 17

3.2.2 Temperature Set-Back ......................... .......................... ................... 20

3.2.3 The Productive Work Space Temperature and Body Temperature ..... 20

3.3 Running the Simulation at Optimal Indoor Temperature Set Points .................. 22

4. COST EFFECTIVENESS STUDY .......................... .......................... .......................... .. 26

4.1 Implementation Cost .......................... ......................... .......................... ........... 26

4.2 Cost Effectiveness ......................................... ......................... ......................... 26

5. CONCLUSION AND THE FUTURE OF BUILDING CONTROL ......................... ....... 28

5.1 Conclusion ...................................................................................................... 28


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5.2 Comparison with Other Studies........................................ ......................... ....... 28

5.3 Sources of Error............................................................................................... 28

5.4 Future Work of Building Control ........................ .......................... ................... 29

References ......................................................................................................................... 30


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LIST OF TABLES

Page

1. Table 1: The Office of Energy Efficiency and Renewable Energy (EERE) Budget .. 2

2. Table 2: Energy Consumption Comparison ... 25

3. Table 3: Estimated Cost of the Project ... 26

4. Table 4: Economical Summary .. 27


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x

LIST OF FIGURES

Page

1. Figure 1: EMCS Evolution..... 6

2. Figure 2: DOE-2.2 Simulation Flow....... 8

3. Figure 3: Aerial View of Legacy Civic Towers Building... 9

4. Figure 4: Annual Average Hourly Load Profile.... 11

5. Figure 5: Annual Average Hourly Load Profile by End-Use ...12

6. Figure 6: Annual Energy Consumption..... 13

7. Figure 7: Simulated Building Energy Consumption by End-Use..14

8. Figure 8: CEUS Large Office Building Energy Consumption by End-Use..15

9. Figure 9: Weather Independent Internal Load Profile... 17

10. Figure 10: Cooling Tower Cooling ... 19

11. Figure 11: 2007 San Jose Outside Wet Bulb Temperature....20

12. Figure 12: Average Human Body Temperature Profile. 21

13. Figure 13: Energy Demand from Different Temperature Set Point Schedules.. 22

14. Figure 14: Energy Demand Profile by Optimal Schedule.. 23

15. Figure 15: Temperature Set Point Schedule... 24


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Chapter 1

INTRODUCTION

1.1 Let There be Green Industry

The green industry has existed for more than 30 years, since the first oil crisis hit

the United States. The Department of Energy (DOE) was formed after the first oil crisis

in 1977 to research renewable energy and to improve energy efficiency. The main focus

of DOE is to develop alternative energy sources other than fossil fuels, hence majority of

its budget is allocated in scientific research such as fusion and nuclear physics. While

DOE focused on the research, the Office of Energy Efficiency and Renewable Energy

(EERE), an agency under DOE, created the green industry to bring energy efficient

technologies and renewable energy sources to consumers.

EERE runs numerous energy efficiency and renewable energy programs and

historically, put slightly more weight on energy efficiency programs then renewable

energy programs, until recently, as shown in Table 1.


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Programs2006 Budget 2011 Budget

$ (thousands) $ (thousands)Biomass and Biorefinery Systems R&D 89,776 220,000

Building Technologies 68,190 230,698Federal Energy Management Program 18,974 42,272Geothermal Technology 22,762 55,000Hydrogen Technology 153,451 137,000Hydropower 495 40,488Industrial Technologies 55,856 100,000Solar Energy 81,791 302,398Vehicle Technologies 178,351 325,302Weatherization and IntergovernmentalActivities 316,866 385,000Wind Energy 38,333 122,500

TOTAL 1,024,845 1,960,658Table 1: The Office of Energy Efficiency and Renewable Energy (EERE) Budget [1]

In 2006, EERE spent approximately 37% on renewable energy research programs

and 43% on building energy efficiency programs, with more emphasis on the building

energy efficiency programs. The building energy efficiency programs include: building

technologies, industrial technologies, weatherization, and intergovernmental activities.

The building technology program focuses on improving the overall energy

efficiency of new and existing buildings through research, partnerships, and developing

tools for various industries. In past years, EERE focused to improve heating, ventilating,

and air conditioning (HVAC) systems, lighting technology, building design, and other

building technologies. The industrial technology program encourages industries in the

U.S. to reduce their energy consumption by running incentive programs to implement

energy efficient equipment and practices. The weatherization and intergovernmental


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programs are incentive programs designed to encourage implementing energy efficiency

equipment and practices.

With the growing concerns about global warming, the general public is now more

conscious about where the energy they use is coming from and how to conserve to reduce

their carbon footprint. As a result, renewable energy gains more support and has fueled

the solar, hydrogen, and wind energy industries. Public demand for renewable energy

was reflected in the 2011 EERE budget allocation: 37% for energy efficiency and 45%

for renewable energy. Emphasis has now shifted toward renewable energy.

1.2 Building Energy Efficiency vs. Renewable Energy Sources

Improving building energy efficiency and researching renewable energy are both

effective ways to reduce carbon footprints, but improving building energy efficiency is

money better spent to reduce our carbon footprint. Lets compare two different ways to

reduce building electric bill. The first way is to install photovoltaic panels and generate

20% of building electric bill at the facility. The other method is to install more efficient

light bulbs to reduce electric demand permanently.

Photovoltaic panels are the iconic green energy of today and it is a good example

to address the problems in renewable energy. For an example, if a facility operation

manager installs photovoltaic (PV) panels to supply 25% of a 14-story office building in

San Jose, it takes 29 years to break even without tax incentives and additional 6.7 years to

break even pollutant created during PV panel production. The available solar radiance in

San Jose is 5.48 kWh/m2/day and that would require 36,339 square feet of photovoltaic


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panels at an average efficiency of 20%. The estimated project cost would be $2.5 million,

without government incentives [2]. Based on PG&Es Billing Schedule E19S summer

peak electricity price of 0.15217 dollar per kWh, it would take 29 years without tax

incentives, ($7,295.41 per month of savings equals 349 months) to break even. If one

considers that the expected lifetime of photovoltaic system is 25 to 30 years, the building

would hardly break even on this investment. Another consideration: in the process of

manufacturing photovoltaic panels, energy has been inputted, this is called embodied

energy. The payback time for manufacturing photovoltaic panels is 6.7 years [3].

Including the payback period for embodied energy, photovoltaic panels end up producing

more of a carbon footprint than if they were not used.

Building energy efficiency, on the other hand, has better monetary return rate.

For an example, upgrading lighting equipment is a common retrofit project among many

businesses. Title 24 requires 1.0 W/square foot for use in office spaces. If a T8 32W

bulb is used, which is the industry standard for office lighting for 200,000 square feet of

office space, it would require 6,250 32W 4-foot T8 bulbs ($2.49) to keep the entire space

at 90 lumens, and a color-rendering index (CRI) of 85. If T5 fixtures were installed

instead, it would need 6,250 28W 4-foot T5 bulbs ($9.75), effectively reducing 120,000

kWh per year at a cost of $61,000. It would only take 3 years to break even. The

payback for the embodied energy of fluorescent light bulbs is only approximately 50

hours.


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1.3 Further Improvement on Building Energy Efficiency

Building energy efficiency improvement is a much better monetary and

environmental return today. For many years, different industries focused on improving

individual component efficiency. In the last decade, with cheaper and higher processing

power available, the building energy management control system (EMCS) was developed

to optimize the whole system efficiency. The first generation of EMCS was focused on

optimizing performance of the individual HVAC component. Now, EMCS involves the

broader picture of the HVAC system and optimizes the system based on readings from

individual components. The modern EMCS can now run an HVAC system based on

occupancy and equipment schedules, and optimize the system based on those factors.

SPEMS is the next generation of EMCS, and the idea for this system has

interested the industry for over 20 years, to further savings from buildings. For the last

20 years, as shown in Figure 1, EMCS have evolved from traditional component control

to equipment optimization and HVAC system optimization [4]. The next improvement

for EMCS is global optimization using computer simulations.


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Figure 1: EMCS Evolution

In 1988, Cumali et al. implemented simulation-powered building management

and control systems to actual buildings, and the result showed a 20% energy reduction

from three large office buildings [5]. The result demonstrated attractive savings, but still,

no EMCS with building simulation capability is available on the market today.

In the past 20 years, significant progress has been made in building energy

efficiency and it is questionable that the same rate of savings could be achieved from a

modern building. Building codes have become more rigorous: the building must be more

insulated, EMCS and sensors together achieve HVAC system optimization, system

component efficiency has been improved, and internal building load has been reduced

from efficient plug loads. Using DOE 2.2 building energy simulation of calibrated

building in San Jose, electrical energy savings from the simulation-powered energy

management and control system (SPEMS) were explored and compared with electrical

energy savings from study done by Cumali et al.

Traditional

Application

Equipment

Optimization

System

Optimization

Global

Optimization

Sophistication

Savings

EMCS Evolution


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Chapter 2

DOE BUILDING SIMULATION

2.1 DOE-2.2

For more than 30 years, building designers and research communities have used

building energy simulation to create energy efficient buildings, assess energy savings

from implementing energy efficient measures, and estimate the size of HVAC equipment.

The DOE building simulation software is the most well-known software in the industry

for its accuracy and usefulness in designing and retrofitting buildings. The DOE building

simulation software was developed by James J. Hirsch in collaboration with Lawrence

Berkeley National Laboratory. From numerous revisions, the current software version is

DOE-2.2.

DOE-2.2 software is composed of four subprograms as shown in Figure 2. The

BDL Processor subprogram takes user input files and accesses a library to generate the

BDL file. The simulation process uses the BDL file and the local weather data to run the

LOADS subprogram to simulate the building heat load, then the SYSTEMS subprogram

simulates the HVAC system load, and finally, the ECONOMICS subprogram calculates

the hourly energy consumption and its cost [6].


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Figure 2: DOE-2.2 Simulation Flow Chart

The user input file is composed of the year of building simulation run,

construction materials, various equipment schedules, building design, HVAC system, and

building zoning pattern. It has intuitive names of variables more easily readable by a

human.

The main purpose of the BDL Processor subprogram is to translate a human-

readable user input file to a machine-readable BDL file and access a library of material

properties and various equipment performance curves. The user can simply define the

construction material, such as Polyurethane insulation, in the user input file, and the BDL

Processor inserts the thermal properties of Polyurethane from the library to the BDL file.

It works the same way for equipment components; the user can simply define a chiller

type in the user input file and the BDL Processor accesses the proper compressor

performance curve and includes it in the BDL file.

As previously mentioned, the DOE-2.2 simulates the building using three

subprograms: LOADS, SYSTEMS, and ECONOMICS. The LOADS subprogram uses a


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BDL file and weather data to calculate the hourly heating and cooling load for a user-

defined building model based on the weather data provided. The SYSTEMS subprogram

calculates the HVAC system performance to meet the user-defined heating and cooling

set point. The ECONOMICS subprogram then calculates the energy consumption and its

cost based on the LOADS and SYSTEMS results. When the building simulation process

is complete, DOE-2.2 generates an Output Report.

2.2 Building Model

To demonstrate the savings from SPEMS, a DOE-2.2 building model of a real

building in San Jose was created. The building is Legacy Civic Towers in San Jose, as

shown in Figure 3. It is a 14-story building with a basement, totaling 200,674 square feet.

Figure 3: Aerial View of Legacy Civic Towers Building [7]


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The building is open from 6 a.m.to 6 p.m., Monday through Friday. For the last

two years, the average building occupancy rate was at 60% of its maximum capacity.

The buildings HVAC system runs at a standard variable air volume with a hot water

reheat system. One 500-Ton York chiller and one 250-Ton Trane chiller supply cools the

chilled water loop, while two gas furnaces heat the hot water loop.

This building was chosen because it is a good representation of a typical high-rise

office building in the San Jose area. The facility was built in 1997, during the dot-com

economic boom era and has participated in multiple PG&E commercial retrofit programs

to keep the building efficiency up to date.

Based on the whole facility survey done by ADM Associates, Inc., in 2009, the

DOE-2.2 building model was created. The whole facility survey includes detailed

information on building construction, HVAC equipment, plug-in equipment,

miscellaneous equipment, facility operating schedules, and EMCS operating setup.

In order to reflect the actual building using a computer model, the model must be

calibrated based on the actual building energy consumption. By running the DOE-2.2

simulation of the building model using the year 2007 weather data from National Oceanic

and Atmospheric Administration (NOAA) and comparing it against the 2007 15-min kW

interval data from PG&E, the model was calibrated to match within 10% of the actual

billing data. The calibration involved adjusting occupancy rate, plug-in equipment load,

and miscellaneous loads.


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Figure 4: Annual Average Hourly Load Profile

The Figure 4 shows the difference between annual average hourly electric

demand profile from the electric interval data and the simulation result. The hourly

profile calibration calibrates building power demand for every hour. The simulation

hourly profile has an average of a 106% match to the actual building hourly profile. The

building electric demand is allocated to represent the actual building electric demand as

shown in Figure 5, below.

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00400.00

450.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

Annual Average Hourly Load Profile

Billing Data

Simulation


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Figure 5: Annual Average Hourly Load Profile by End-Use

The building electric demand is broken up into seven different end-use categories:

space cool, heat rejection, ventilation, pumps, exterior use, plug loads, and lighting. The

space cool is the energy demand from chillers; heat rejection is the energy used by

cooling tower; ventilation is the energy demand from air handling units and exhaust fans;

pumps are the energy consumed by various hydro pumps associated the HVAC system

and domestic hot water; exterior use is the buildings exterior lighting; plug loads are

miscellaneous equipment connected to building via electrical outlet; and lighting is the

demand from lighting fixtures.

This building houses a server room that is running 24 hours a day, seven days a

week, therefore at night, the building still demands approximately 160 kW. The majority

of that demand is from the mainframe computers and cooling associated with them. The

0

50

100

150

200

250

300

350

400

450

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

Annual Average Hourly Load Profile by End-Use

Space Cool

Heat

Rejection

Ventilation

Pumps

Exterior Use


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plot also shows slowly increasing demand at 6 a.m.as employees come to work and

slowly decreases at 5 p.m.as employees leave.

Once the hourly electric demand is calibrated, the next step is to calibrate electric

energy consumption. The hourly profile gives a daily demand of the electricity, whereas

the annual electric energy consumption gives the monthly profile of electricity

consumption. Figure 6 presents the comparison of annual electric energy consumption

between 2007 Bills and simulation results. On average, the simulation electric energy

consumption agrees to the actual electric bills by 101%.

Figure 6: Annual Energy Consumption

-

50,000

100,000

150,000

200,000

250,000

1 2 3 4 5 6 7 8 9 10 11 12

kW

h

Month

Annual Energy Consumption

2007 Bills

Simulation Result


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Figure 7: Simulated Building Energy Consumption by End-Use

The buildings annual electric energy consumption by end-use is shown in Figure

7 above. For this particular building, the plug load is the largest part of the annual

electric consumption, at 37%. HVAC equipment, a combination of space cool, heat

rejection, ventilation, and pumps, is the second largest, at 36%. The lighting fixtures take

up 25% and the exterior lights make up the last 2%. This ratio is comparable to Itrons

California Commercial End-Use Survey (CEUS) result of large office buildings in

California shown in Figure 8.

Space Cool

10%

Heat Rejection

0%

Ventilation

12%

Pumps

14%

Exterior Use

2%

Plug Loads

37%

Lights

25%

Energy Consumption by End-Use


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Figure 8: CEUS Large Office Building Energy Consumption by End-Use [8]

The difference in plug loads by comparing results in Figure 7 and Figure 8 comes

from the mainframe computer hosted at the Legacy Partners building. The mainframe

server computers run 24 hours a day, seven days a week, and therefore take up large

portions of the total consumption. The general office building in CEUS does not include

facilities with mainframe servers; rather it categorizes separately as high-tech facility.

HVAC

42%

Exterior Use

3%

Plug Loads

30%

Lighting

25%

CEUS End-Use Consumption


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Chapter 3

SPEMS SIMULATION

3.1 Simulation Tactics

The simulation of the simulation-powered energy management and control system

(SPEMS) is a challenge because there is no function available in DOE-2.2 to simulate

such EMCS. SPEMS is designed to create numerous temperature schedules and run

DOE-2.2 simulation, and uses the temperature schedule that saves the most electricity. In

order to demonstrate the savings from the SPEMS, a simplified approach was taken to

mimic how SPEMS would control indoor temperature. 20 indoor temperature set point

schedules were created based on three factors: Outside weather condition, internal loads,

and the productive workspace temperature. Multiple DOE-2.2 runs were made and the

schedule with the highest savings for every month was chosen to run.

The reason for using the indoor temperature set point is that, in the DOE-2.2

simulation, indoor temperature set point controls the supply of cool air and directly

controls the HVAC system run-time. The indoor temperature depends on human

occupancy, plug loads, lighting loads, sunlight, and the latent heat from walls and

windows. The indoor cooling load varies by time of day. Sunlight and latent heat from

the walls and windows depend on the weather and it affects the performance of chillers.

DOE-2.2 runs system optimization at a given condition, and one can lower the

temperature set point to draw more cooling load inside and indirectly control the chillers

at any given time. Conversely, one can increase the indoor temperature to rest the

chillers.


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3.2 Indoor Temperature Set Point Schedule

The indoor temperature set point schedules were created based on three energy

savings strategies. The first, is to pre-cool running chillers during lunchtime (Noon to 1

a.m.) to reduce chiller runtime during the peak time. The second is to start temperature

set back as occupancy rates drop in the building. The last strategy is to slowly lower the

temperature set point in the morning until body temperatures reach the optimal level of

98.6F.

3.2.1 Lunchtime Pre-Cool

The lunchtime pre-cool is a strategy to run chillers during lunchtime when there is

less internal load and when the cooling tower efficiency is higher. During that time, the

building occupancy drops as employees leave to have lunch. The following plot in

Figure 9 shows the change in occupancy rate and plug load demand.

Figure 9: Weather Independent Internal Load Profile

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

%ON

Hour

Occupancy

Lighting

Equipment


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The lighting equipment tends to be powered on during the business hour in an

open office configuration because it is ineffective to install occupancy sensors on every

lighting fixture in an open office space. Therefore, EMCS controls the light in that type

of office.

As occupancy rates fall, equipment demand falls due to office equipment going

into idle mode. For example, personal computers consume less energy when it lowers the

CPU clock speed to use less CPU and power down monitors when no one is using them.

Copy machines goes to a sleep mode when they are not used for more than an hour. An

automatic system like this can bring the plug load down during lunch.

Outside wet bulb temperature is closely related to cooling tower efficiency and it

is better to run the tower when the wet bulb temperature is lower. As outside wet bulb

temperature increases, the cooling capacity on a cooling tower falls, as it becomes

difficult to remove the heat using the evaporation of water. Figure 10 shows how the

outside wet bulb temperature affects the cooling capacity of the cooling tower. As the

wet bulb temperature increases, cooling capacity drops.


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Figure 10: Cooling Tower Cooling Capacity [9]

Based on the 2007 NOAA San Jose weather record, an average wet bulb

temperature plot was created as shown in Figure 11. Three average hourly wet bulb

profiles are displayed: January, July, and the annual average. The outdoor wet bulb

temperature peaks at 1 p.m., so by reducing the cooling tower run-time during this hour,

the building can potentially save energy. The January profile is an example of a winter

wet bulb temperature profile, and the July profile is an example of a summer wet bulb

temperature profile.

757

704

631

558

400

450

500

550

600

650

700

750

800

66 68 70 72

Coo

lingCapacityinGPM

Outside Wet Bulb Temperature

Cooling Tower Cooling Capacity


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Figure 11: 2007 San Jose Outside Wet Bulb Temperature

3.2.2 Temperature Set-Back

Starting at 5 p.m., employees begin to leave work, and the temperature set back

strategy should begin to reduce chiller run-time. As shown in Figure 9, occupancy,

lighting, and equipment loads fall after 5 p.m. For example, when the SPEMS predicts

lower demand ahead, it forces the chillers to run at their highest efficiency even if it is a

lower cooling supply. If at 4:30 p.m. the internal set point temperature is at 74F and the

chiller needs to run at 90% load to meet that cooling load, SPEMS can decide to run the

chiller at 85% load because the chiller efficiency is higher at that load, but it produces

enough cooled air to keep the internal temperature at 75F.

3.2.3 The Productive Work Space Temperature and Body Temperature

The indoor temperature set point and body temperature together are an important

factor for keeping employees productive and comfortable. In many studies of

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

F

Hour

Outside Wet Bulb Temperature

July

Average

January


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productivity at work, researchers found an inverted U-shape relationship centered

between 72F (22C) and 77F (25C) [10]. As the indoor temperature deviates from

that range, productivity falls by 2% per every degree Celsius. Hence, the temperature

between 72F to 77F is the range of temperature and SPEMS can fluctuate without

sacrificing productivity.

Thermal comfort is another factor related to work productivity. The comfortable

temperature is relative to the temperature difference between body and ambient. Body

temperature varies throughout the day; it is lowest at 4 a.m. and highest between 4 p.m. to

6 p.m. as shown in Figure 12.

Figure 12: Average Human Body Temperature Profile [11]

Optimal body temperature is 98.6F. The body feels different depending on if

body temperature is below or above optimal temperature. In the morning, the body

97.2

97.4

97.6

97.8

98.0

98.2

98.4

98.698.8

99.0

99.2

99.4

99.6

99.8

100.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Bo

dyTemperature

(F)

Hour


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temperature is below the optimal temperature, therefore, the person prefers a warmer

ambient temperature until their body temperature reaches the optimal level. Warmer

ambient temperature minimizes body heat loss so it helps body to reach the optimal level.

In the afternoon, the body temperature is well above optimal levels, so the cooler ambient

temperature feels better as it helps the body to regulate its temperature.

3.3 Running the Simulation at Optimal Indoor Temperature Set Points

Based on the three energy saving strategies described in the previous section, 20

temperature schedules were created and compared against the baseline. Based on

independent DOE-2.2 runs of different temperature schedules, SPEMS chooses the

optimal temperature schedule to run the building.

Figure 13: Energy Demand from Different Temperature Set Point Schedules

Figure 13 shows hourly demand for a 2007 peak day average. The peak day was

defined by California Public Utilities Commission (CPUC), the hottest three consecutive

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

Energy Demand from Different Temperature Set Point Schedule


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weekdays in 2007, which are July 17 to July 19. As shown above, 20 different

temperature schedules create different demand profiles. Even with the same temperature

schedule, depending on the weather, demand can be different.

Figure 14 compares the peak day average profile to the baseline schedule, the

75F constant temperature set point schedule, and the July optimal schedule.

Figure 14: Energy Demand Profile by Optimal Schedule

In the morning, the optimal schedule demands less energy because lower

temperature set points provide comfortable temperatures until body temperature reaches

its optimal 98.6F. The cooling load continues to increase throughout the morning to

cool the building until noon. From noon to 2 p.m., the energy demand stays constant as

the pre-cool pays off by slowly increasing the temperature set point. At 4 p.m., SPEMS

anticipates the lower cooling demand after 5 p.m. and starts the set back process.

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

Energy Demand Profile by Optimal Schedule

Baseline75F

Optimum


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The average indoor temperature set point for the July optimal schedule during

business hours is 74.8F but the average indoor temperature between 11 a.m. and 4 p.m.

is 72.5F. The plot in Figure 15 shows the difference between the baseline and optimal

temperature set point schedules.

Figure 15: Temperature Set Point Schedule

The result of running SPEMS on the building mode is presented in Table 2. The

annual energy savings from the SPEMS is 52,254.36 kWh, which is a 2.25% energy

reduction. The building is in PG&E rate schedule E19S, which charges $0.15217 per

kWh during the summer. The monetary annual savings is $7,951.55.

65

70

75

80

85

90

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Temp

(F)

Hour

Temperature Setpoint

Optimum Schedule


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Month Baseline(kWh)

SPEMS(kWh)

Savings(kWh)

% Reduction

Jan 178,360.69 175,152.61 3,208.08 1.80%Feb 161,804.56 159,092.47 2,712.08 1.68%

Mar 190,674.90 186,478.07 4,196.83 2.20%

Apr 185,931.65 180,807.08 5,124.57 2.76%

May 204,114.26 197,789.72 6,324.54 3.10%

Jun 205,737.04 200,071.64 5,665.40 2.75%

Jul 220,771.72 215,409.32 5,362.39 2.43%

Aug 227,791.59 221,881.21 5,910.38 2.59%

Seo 198,779.49 195,799.50 2,979.99 1.50%

Oct 196,154.30 191,964.31 4,189.99 2.14%

Nov 177,464.92 172,974.90 4,490.02 2.53%

Dec 175,102.85 173,012.75 2,090.09 1.19%

TOTAL 2,322,687.96 2,270,433.59 52,254.36 2.25%

Table 2: Energy Consumption Comparison


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Chapter 4

COST EFFECTIVENESS STUDY

4.1 Implementation Cost

The estimated cost of implementing SPEMS is $90,480.00 for a 200,000 square

foot office building. The cost breakdown is listed in Since there are no EMCS with

building simulation available to the consumer today, a control engineer must integrate the

building simulation to the existing EMCS. More sensors are required to be installed on-

site to provide more feedback to SPEMS. In order to create a building model, a full

building audit is needed. On top of the cost of implementing the SPEMS, there is also

cost involved to commission the project, creating a report and a user manual, and training

employees.

Components Material ($) Hours Rate ($/hour) T&M Cost ($)

Integration with BAS/EMS 5,000.00 80 120 14,600.00Sensor Installation 15,000.00 240 60 29,400.00Building Audit 1,000.00 80 120 10,600.00Computer Modeling 160 120 19,200.00Commissioning 80 120 9,600.00Report/manual 1,000.00 40 80 4,200.00Training 24 120 2,880.00

Total Cost ($) 90,480.00

Table 3: Estimated Cost of the Project

4.2 Cost Effectiveness

Table 4 provides the economical summary and indicates that SPEMS

implementation requires 11.4 years to break even, based on annual energy savings. The

Database for Energy Efficiency Resources (DEER) estimates the expected useful life of


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typical HVAC equipment at 15 years and the SPEMS can break even before its predicted

lifetime.

Cost $90,480

Energy Savings 52 MWhAnnual Cost Savings $7,952

Simple Payback Time 11.4 year

Table 4: Economical Summary


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Chapter 5

CONCLUSION AND THE FUTURE OF BUILDING CONTROL

5.1 Conclusion

The payback period for SPEMS is 11.4 years which makes it difficult to sell to

customers because a payback period over 10 years is considered to be too long. Because

of the sophistication of the SPEMS implementation, cost is high as shown in Chapter 4.

In order to market proliferate SPEMS, the payback period needs to be less than 3 years,

and otherwise facility managers find it difficult to justify the project cost.

5.2 Comparison with Other Studies

Unfortunately, SPEMS showed only a 2.25% energy reduction from the Legacy

Civic Towers building. In 1988, Cumali et al. implemented simulation-powered EMCS

on three large office buildings and they were able to achieve a 20% energy reduction.

The major source of this discrepancy between these two numbers is from the building

code of higher efficiency standard enforced by local governments. A modern building,

like Legacy Civic Towers, has better insulation, better glazing, more efficient office

equipment, and more efficient HVAC components. Along with these technological

improvements, EMCS can optimize the system, so savings from global optimization have

diminished compared to Cumalis experiment in 1988.

5.3 Sources of Error

In this analysis, the SPEMS were simplified and it may not demonstrate its full

capability. The proper way to simulate the SPEMS, temperature schedule and energy

consumption should be a multi-variable function, and SPEMS should run numerous


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DOE-2.2 simulationsiteratively to find the optimal temperature schedule. SPEMS should

create a new optimized schedule on a daily basis.In this paper, for simplicity, monthly

optimized schedules were created.

5.4 Future Work of Building Control

It was difficult to create a DOE-2.2 simulation of SPEMS as envisioned by author

of this paper. The SPEMS of the future should be able to communicate with local

weather stations and the utility companys server. Employees in the building would have

a radio frequency identification (RFID) card so the SPEMS can monitor building

occupancy level. Based on the building occupancy, SPEMS could shut down lights and

plug loads in vacant rooms, also estimating the cooling load required for the next hour.

SPEMS also would monitor indoor CO2 levels to control the percent of outside air intake

and air recirculation to minimize the quantity of air to be conditioned. The local weather

data could be automatically downloaded by SPEMS and used on its DOE-2.2 simulation

runs. The building could have photovoltaic panels, and based on an hourly electric rate,

SPEMS can decide whether to store, sell, or use the electricity being generated. If all

these capabilities were simulated, the savings could be large enough to be worth

implementing SPEMS on all buildings.


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REFERENCES

1. Office of Chief Financial Officer.Department of Energy FY 2011 Congrational BudgetRequest. Washington DC: Depart of Energy, 2010, 25.

2. Cooler Planet. Cooler Planet. http://solar.coolerplanet.com/Articles/solar-calculator.aspx(accessed June 23, 2010).

3. Corkish, Richard. "Can Solar Cells Ever Recapture the Energy Invested in theirManufacture?" Solar Progress (Photovoltaics Special Research Center), 1997: 16-17.

4. Thielman, Davis. "System Optimization - The Global Approach to HVAC Control." FirstSymposium. 1984.

5. Cumali, Z. "Global Optimization of HVAC System Operations in Real Time."ASHRAETransaction 94(1), 1988: 729-1744.

6. Simulation Research Group.DOE-2 Home. http://gundog.lbl.gov/dirsoft/d2whatis.html(accessed June 23, 2010).

7. Microsoft.Bing Maps. http://www.bing.com/maps/ (accessed July 30, 2010).8. Itron, Inc. California Commerical End-Use Survey. Consultant Report, Itron, Inc, 2006,

158.

9. Evapco. "LSTB/LPT Forced Draft Cooling Towers." Evapco, 22.

10.Encyclopaedia Britannica.Animal Heat. Vol. 2, inEncyclopaedia Britannica, 11thEdition, 49. 1911.

11.Seppanen, Olli, William J. Fisk, and David Faulkner. Control of Temperature for Healthand Productivity in Offices. Institute of Heating Ventilating and Air Conditioning,


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Helsinki Univsersity of Technology, Berkeley: Lawrence Berkeley National Laboratory,

2004, 2.

simulation-powered building energy management and control system

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