Applying Computer-based Simulation

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Applying computer-based simulation toenergy auditing: A case study

Yimin Zhu *

Department of Construction Management, College of Engineering and Computing, Engineering Centre,

Florida International University, Miami, EC 2956, 10555 W. Flagler Street, Miami, FL 33174, USA

Received 15 March 2005; received in revised form 20 July 2005; accepted 29 July 2005

Abstract

Through a case study, this research explores an approach, which uses computer simulation technology, to evaluate different energy conservationalternatives and to assist facility managers to select reliable and feasible solutions. The subject facility is located in the Southeast region of the

United States. One of the major challenges and operation goals of the General Services Administration, who manages the facility, is for that facility

to achieve the Energy Star designation. However, due to the complexity of the facility, the requirements from building occupants, as well as other

difficulties, finding a path for optimizing the operation of the facility in order to achieve the Energy Star designation is not always easy.

This project uses eQuest, a simulation software tool, to create a ‘‘virtual environment’’, in which the operations of the HVAC (heating

ventilation air-conditioning) system and the lighting of the facility are studied. Subsequently, recommendations initially made by experts through

traditional energy audit approaches are evaluated in the ‘‘virtual environment’’ in order to determine the best solution to achieve the goal of the

facility managers. This paper discusses major aspects of the project, including the challenges, the values and the limitations of applying computer

simulation techniques in such a facility with complicated structural, occupancy and operation features.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Energy efficiency; Computer-based simulation; Energy audit

1. Background

Energy consumption of buildings is a research topic that

retains a lot of research attention [e.g., 1–5], since many studies

suggest that energy consumption is one of the major types of

cost throughout the lifecycle of a building. In addition, due to

the rising cost of energy, looking for efficient ways of building

operation such as load management [6] or renewable energy

[7] becomes more and more important for facility managers,

who often require a tool that can assist them for decision-

making regarding various alternatives of a retrofitting project orthe alteration of an existing facility.

Facility managers, nowadays, face many technical chal-

lenges, including identifying problematic areas in a facility,

isolating different types of problems, prioritizing the impact of

those problems, developing solutions and implementing

selected solutions, as well as psychological challenges, i.e.,

to accept risks associated with the selected solution. Once an

energy saving plan is decided and executed, the process is

difficult to reverse. In addition, many energy-auditing studies

are often constrained by budget and time, yet expectations from

the results of those studies are usuallyhigh. All those factors put

a facility manager in a very risky position at the time of making

decisions in order to ensure that, the selected energy

conservation plan will deliver whatever it promises.

In general, energy auditing has been an effective tool that can

assist facility managers to develop their energy saving plans and

to achieve their energy saving goals [e.g., 4,8–12]. However,

many existing energy auditing approaches may overlook theintricate relationships between different factors that will affect

the energy consumption of a large facility [e.g., 10,13], and the

cross-effect that two or more different solutions may result in.

Therefore, a more effective validation tool is needed to fine-tune

the results from an energy auditing process.

Computer-based simulation is accepted by many studies as a

tool for evaluating building energy [e.g., 14,15]. There are

many different types of computer-based simulation tools that

are available for performing whole-building simulation, e.g.,

the application of DOE 2 [10,16] and an expert system for

www.elsevier.com/locate/enbuildEnergy and Buildings 38 (2006) 421–428

* Tel.: +1 305 348 3517; fax: +1 305 348 6255.

E-mail address: [email protected].

0378-7788/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.enbuild.2005.07.007


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supporting energy auditing [17]. eQuest, a quick energy

simulation tool developed by Doe2.com, is ‘‘a sophisticated,

yet easy to use, freeware building energy use analysis tool

which provides professional-level results with an affordable

level of effort’’ (http://www.doe2.com). This is the reason that

this study chooses to use eQuest over many other simulation

tools such as DOE2 (http://www.doe2.com) and EnergyPlus

(http://www.eere.energy.gov/buildings/energyplus/ ).

Another issue related to an energy audit is benchmarking.

The Energy Star Program, developed and maintained by the

Environmental Protection Agency in the United States,

provides an excellent source for benchmarking building energy

consumption. The Energy Star Program is a voluntary labeling

program of the United States Environmental Protection Agency

(EPA) and the United States Department of Energy that

identifies energy efficient products, including buildings.

Qualified products exceed minimum federal standards for

energy consumption by a certain amount, or where no federal

standards exist, have certain energy saving features. Such

products may display the Energy Star label. The Energy Starlabel is awarded to buildings that perform in the top 25% in the

country (a score of 75 or better out of 100).

In addition, the Environmental Protection Agency

provides an online tool, the portfolio manager, for energy

evaluation of an existing facility with respect to the Energy

Star designation (http://www.energystar.gov/ ). The software

tool allows a user to manage several facilities at the same

time. To evaluate each facility, a user needs to input facility

data (e.g., the name and the address of a facility and contact

information of a facility manager), facility space information

(e.g., the types of spaces, gross floor area, occupants, the

number of computers and operation hours) and energy meters(e.g., the types of energy, the types of spaces that are

associated with the energy data, time period of the energy

data, the energy data and the associated cost). Once such

information is input to the software, the software tool will

calculate a score for the facility. Therefore, this study will

rely on the online tool provided by the Environmental

Protection Agency for evaluating energy performance of an

existing facility.

In the following, the paper will discuss a case using an

off-shelf software tool, eQuest, as a complementary tool to

validate recommendations from a conventional auditing

process. Details of the modeling process, as well as the results

of using the simulation technology, are also discussed.

2. An introduction to the case

The facility consists of a high-rise tower (of 25 stories), a

bridge crossing the Forsyth Street that connects the high-rise to a

12-story mid-risetower,the mid-risetower and the 1924building

(the historic Rich’s department store) directly connected to the

mid-rise. The total area for these structures is more than 1.4

million square feet (about 130,000 m2) (see Table 1). In addition,

there is a 10-story parking garage attached to the tower building;

four stories of which are located underground and the rest are

above grade and blended in with adjacent office space.

An initial study on the energy performance of the facility

indicates that the facility may not be very far from achieving the

designation of the Energy Star. The initial study, based on the

monthly electricity consumption data from August 2000 to July

2001, showed that the building had a score of 62 for Energy Star

rating, which was lower than the required score, 75, for Energy

Star designation (see Table 2). The analysis also implies that the

energy consumption rate of this building may be above the

national average rate, which is 50 according to the portfoliomanager. In addition, the energy consumption for a typical office

in the USA is US$ 1.5 per square foot (US$ 16.15 per square

meter) per year [18]. For this building, the energy cost per square

footis US$ 0.98(from July 1999 to June 2000) (or US$ 10.55 per

square meter) and US$ 1.15 (from July 2000 to June 2001) (or

US$ 12.38 per square meter), which further supports the

observation that the current status of energy consumption is

better than the national average. Other comparisons using US

office cost indexes [18] also reveal similar results.

Before conducting the computerized simulation of the

energy performance of the facility, an ALERT (assessment of

load and energy reduction techniques) was assembled toperform an assessment of the facility, which resulted in several

recommendations. The experts in the ALERT (assessment of

load and energy reduction techniques) team performed various

studies by working through the facility, interviewing facility

managers, collecting and studying as-built information, study-

ing the building control system and analyzing the data and the

information for developing recommendations.

Among the recommendations, three areas are targeted

for improvements, i.e., heating energy reduction, the HVAC

(heating ventilation air-conditioning) fan runtime reduction

and savings from lighting. Since the implementation of those

recommendations has different cost implications as well,

the facility managers would like to see cost effective solutions,as well as to have a second opinion on the recommendation.

Y. Zhu / Energy and Buildings 38 (2006) 421–428422

Table 1

Building physical attributes and operation data

Space name Space type Start date Floor space (m2) Operating

hours/week

Data center Computer

data center

7/26/1999 1040.79 168

Food service Mercantile

and services

7/26/1999 2917.34 40

Office Office 7/26/1999 134241.18 84

Total 138199.31

Table 2

Results of the initial energy efficiency assessment

This building National average

Year ending Your target

25 July 2001

Score 62 75 50

Energy use (k Wh/m2) 248.98 212.42 291.84

Energy cost ($/year) 1704037 1457501 1927760

http://www.doe2.com/http://www.doe2.com/http://www.eere.energy.gov/buildings/energyplus/http://www.energystar.gov/http://www.energystar.gov/http://www.eere.energy.gov/buildings/energyplus/http://www.doe2.com/http://www.doe2.com/


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To meet the needs of facility managers, this project intended

to explore those recommendations by utilizing the computer

simulation tool, eQuest, and the Energy Star evaluation tool, the

portfolio manager. Major activities of the execution plan for the

project include:

1. Modeling by using eQuest.

2. Validating the model with actual electricity data.

3. Evaluating each recommendation, i.e., recommendation 1,

heating energy reduction; recommendation 2, HVAC fan

runtime reduction; recommendation 3, lighting energy

reduction, separately as well as in different combinations.

4. Using the electricity data generated from each recommenda-

tion, as well as their combinations, from step 3 to determine

the Energy Star score by using the portfolio manager.

5. Making the final recommendation.

3. Methodology

In the following, the paper will discuss the methodology

applied in this study, including data collection, modeling and

model assessment.

3.1. Data collection

Data acquisition is primarily concerned with collecting data

relevant to the energy performance of the facility to ultimately

simulate the actual energy behavior of the facility. The data

substantially covers fundamental building characteristics such

as geometrical configuration, internal loads, building shell,

energy systems including the water-side and the air-side of theHVAC (heating ventilation air-conditioning) systems and

operations.

The bulk of collected data is gathered based on the as-built

blueprints (e.g., architectural, electrical, HVAC (heating

ventilation air-conditioning), lighting, etc.) and the specifica-

tions of the facility, while interviewing with facility manage-

ment personnel augments, validates and ensures that data and

information are up-to-date and conforming with the current

building conditions. The building automation system, Metasys

from Johnson Controls, is another substantial data-source for

building operation data. A wealth of data such as operating

hours, and temperature and pressure set points are referenced

from the Metasys.The following are the major types of the building and the

HVAC (heating ventilation air-conditioning) system data that

are collected for assessing building energy performance (for

details please refer the model itself):

Geometrical Configuration and building footprint.

Building shell and construction materials.

Internal loads including occupancy/un-occupancy loads of

employees during day time and after-hours, office equipment,

lighting systems and heating and cooling loads.

Operating schedules including occupancy and after-hour

schedules.

HVAC (heating ventilation air-conditioning) system equip-

ment and operation, including the water-side and the air-side

systems.

3.2. Modeling

The model is created from the building characteristics

acquired from our building survey and the as built documenta-

tions, specifications and drawings.

3.2.1. Geometric modeling

In order to create a building model for the eQuest simulation,

a geometric model of the building is created and then the

characteristics of each modeled space (see Figs. 1 and 2) are

specified accordingly. The layout of the geometric model or the

connectivity of the thermal zones is based on the architecturaland the HVAC (heating ventilation air-conditioning) drawings.

Afterwards, the HVAC (heating ventilation air-conditioning)

system of the building is modeled, followed by the AHUs

(air handling units), the controlling units in this project.

The geometry model of the facility is first created based on

the world coordinates of the facility and then the model is

rotated 388 clockwise according to the azimuth angle of the

Y. Zhu / Energy and Buildings 38 (2006) 421–428 423

Fig. 1. Geometric representation of the facility (south-east view).

Fig. 2. Geometric representation of the facility (south-west view).


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actual facility. The interior walls in the model are classified into

two types: solid wall and air wall. The interior partitioning in

the model, which forms the space boundaries of thermal zones

of the model, is modeled by two criteria: the thermal zones

according to the HVAC (heating ventilation air-conditioning

system) drawings and the thermal characteristics of the physical

partitions of the floors above or below, for the consistency in

specifying the adjacent space or the space ‘‘next to’’ or on the

other side of the wall. For example (see Fig. 3), the floors of

space B and C can be specified as interior walls that are next to

space A, instead of specifying the entire floor above A as aninterior wall next to space B or C, because the floors of space B

and C may have different thermal characteristics. Although this

will lead to more thermal zones in the model than those

specified in the drawings, it will improve the accuracy of the

simulation results. On the other hand, with space D (see Fig. 3)

acting as a plenum space above space A, the job can be done

more easily, because no matter how busy the partitions of the

spaces above or below are, the plenum space can be shared as

the ‘‘next to’’ type of space for the interior wall specification

and can act as a transition to the next floor.

The plenum spaces above the ceiling are modeled to be the

return air spaces for the AHUs (air handling units). Like otherthermal zones, the plenum layout has to be consistent with the

adjacent spaces on the other side of the ceiling or floor in terms

of geometry. To separate each plenum from one another, air

walls are used as partitions. In the high-rise building, the

plenum spaces are partitioned into mainly three parts, the

plenum in: the tower area, the connector area and the bridge

area. The mid-tower building and the Rich building also have

their own plenum spaces to separate the return air, since they do

not share the same HVAC (heating ventilation air-conditioning)

system.

To simplify the model, the model uses multipliers for typical

floors. However, the complexity of the building, especially the

types of walls that differ from one floor to another, limits the useof the multiplier to only the typical floors of the tower despite

the fact that many other floors have the same, or similar, floor

layout in terms of geometry.

Thermal zones consist of perimeter, core and plenum spaces.

When modeling thermal zones, the perimeter spaces, strips of

space around the building, are modeled as 15 ft (4.6 m) wide;

except for the ones of the bridge, which are modeled as 9 ft

(2.7 m) wide. The HVAC (heating ventilation air-conditioning)

drawings are used when the thermal zones are developed.

The high-rise and the mid-rise have curtain wall systems

with mainly two types of glass. The remainder of the exterior

wall system is masonry concrete blocks masked and concealed

with decorative pre-cast concrete boards on the outside. The

Rich building, with more traditional window openings, is en-

closed with pale-yellow brick veneer exterior walls.

The roofing systems are 8-in. (20 cm) concrete roofing slabs

insulated with felt-bitumen and rigid insulating stucco.

Waterproofing and drainage matting are used to enhance the

resistance against the severe weather conditions. The roofing

systems are finished with polished and honed terrazzo marble.

The interior floors are finished with tufted carpet.

The features of the facility discussed above are major

considerations when developing the geometric model.

3.2.2. Internal loads

The types of internal loads considered in the model include

human occupants, overhead lighting, task lighting, plug-loads

and data centers. The data is mostly collected via surveys. In

many cases, data collected is not directly associated with each

thermal zone; rather the data is the total for a specific floor, or

even the entire building. In such cases, this model uses an

averaging approach based on the percentage of a particularthermal zone area to the total floor area.

When defining internal loads for each thermal zone, eQuest

allows a user to specify many types of information, e.g.,

geometric information about the thermal zones in the model,

energy consumption for equipment and lighting in the zone. In

addition, infiltration methods and day lighting can also be

specified in the model.

3.2.3. Water and air-side systems

The central chiller plant consists of five Trane chillers, four

1310 t units and one 500 t unit, and is located in the basement

of the high-rise, with chilled water pumped throughout thefacility (bridge, mid-rise and 1924 building). An Alfa-Laval

plate heat exchanger allows water-side economizer operation,

with 2000 t of cooling capacity when the outdoor wet-bulb

temperature is 38 8F (3.3 8C) and full cooling tower flow is

maintained. Cooling towers are on the roof. The economizer is

not utilized at the time of this study and thus is not modeled

either.

In the primary loop, the configuration of chillers and

pumps are parallel in that one chiller is served by one pump

for the condensation cycle and another pump for the

evaporation cycle. The configurations of the primary pumps

and secondary pumps are serial. The data required for

simulating the working conditions of chillers, pumps andcooling towers is obtained from equipment specifications,

design documents and surveys.

The air-side units are floor-by-floor variable speed air

handlers with chilled water coils. Interior zones are controlled

by VAV (variable air volume) boxes to maintain interior space

conditions at reasonable comfort levels (with no heating

available) and exterior zone control is supplemented with

PIUs (powered induction units) with heating coils. Minimal

outdoor air is provided and no air-side economizers are used.

The data for simulating the operation of air-side units is

obtained from equipment specifications, design documents

and surveys.


Fig. 3. Thermal zone design.


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3.3. Model assessment

The model was assessed based on one-year (2001)electricity data, because that year the operation was relatively

stable. Before or after that year, there were operation changes

made to the HVAC (heating ventilation air-conditioning)

system and eQuest does not support the co-existence of various

operations for simulation, which limits our use of actual

electricity data.

Paired-samples t -tests were applied to compare the data

from simulation and from the actual electricity bills to

determine if there is a significant difference. A statistics

software tool, SPSS version11, was used to facilitate the

analysis.

Table 3 shows 12-m electricity data collected from twosources, i.e., the actual electricity bills (labeled as ‘‘Actual’’)

and the simulation model (labeled as ‘‘Simulated’’). Fig. 4

descriptively demonstrates the patterns of the two data series.

The two patterns are very similar except for data for April,

August and September, which show some irregularities. In

order to verify that there is no significant difference between the

two data series paired sample tests were performed. The results

of the tests from the SPSS software are shown in Tables 4–6.

The probability value, 0.652, labeled as ‘‘Sig. (two-tailed)’’

in Table 4 indicates that there is no significant difference

between the two data sets at the significant level of 0.05.

Meanwhile, the correlation analysis shows that these two data

sets are significantly correlated (see Table 5). The statistics

(see Table 6) also show that the means for the two data sets are

very similar. The statistics analysis indicates that the model has

generated viable data.

4. Optimization

Before the recommended energy conservation plans were

input to the model for evaluation, observations were made

about the existing facility operation and maintenance. It has

been noticed that the fans of AHUs (air handling units) are

locked into manual mode, meaning that they run all the time

regardless of what the Metasys tells them to do. This has

negative ramifications as to how much fan energy is wastedespecially during unoccupied periods. Also, the Metasys

programming does not map the control of the PIUs (powered

induction units) to an unoccupied setting for controlling

heating, which results in the PIUs (powered induction units)

supplying heating energy even when the central AHU (air

handling unit) fans are off. This means that these terminal units

operate during after-hours to maintain space temperatures at

72 8F (22 8C) or above all the time. Implementation of heating

setback involves mapping the unoccupied period control of the

PIUs (powered induction units) to Metasys, so that the

space heating temperature setpoint is 72 8F (22 8C) during

occupied periods and 68 8F (20 8C) during unoccupied periods


Table 3

Electricity data for model assessment (units: k Wh 1,000,000)

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

Actual 4.24 3.48 3.30 3.10 2.73 2.92 3.00 2.81 2.82 2.51 2.90 3.85

Simulated 4.20 3.46 3.32 2.72 2.75 2.80 2.98 3.12 2.58 2.60 3.14 3.68

Fig. 4. Actual vs. simulated electricity usage.

Table 4

Paired-samples t -test

Paired differences t d.f. Sig. (two-tailed)

Mean Standard deviation Standard error mean 95% confidence inter-

val of the difference

Lower Upper

Actual vs. simulated 0.0256 0.19125 0.05521 0.1459 0.1971 0.463 11 0.652

Table 5

Paired-samples t -test correlations

N Correlation Level of significance

Actual vs. simulated 12 0.925 0.05

Table 6

Paired-samples t -test statistics

Mean N Standard deviation Standard error mean

Actual 3.1381 12 0.50087 0.14459

Simulated 3.1125 12 0.48769 0.14078


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(all other times). These two approaches do not cost too much to

implement.

Another more expensive approach, which may result in more

energy savings, is to reduce the lighting energy consumption in

the facility. Current lighting practices and schedules do not

reflect optimized usage and, if monitored and adjusted, may

provide significant energy savings. Main overhead office lights

are supposed to have regular occupancy schedules, yet the

reality indicates that lighting schedules extend after-hours to

allow cleaning crews to perform their jobs but with a huge time

window. This is essentially due to lighting controllers, which

are centralized on each floor so that a whole floor has to be on if

after-hour lighting operation is desired. Compounded with

these practices, the high-rise cafeteria lighting set up

illuminates approximately 7 W/f 2 (96 W/m2) and does not

make advantageous use of daylight harvesting of the perimeter

lighting coming through the northern and southern cafeteria

glass curtain walls. On the other hand, a threshold of 1.8 W/f 2

(19 W/m2) has been documented based on as-built lighting

drawings and could provide sufficient illumination for thecafeteria area.

Multiple recommendations are considered to optimize

lighting practices at the facility. Having lights on more than

12 h/day is not effective energy management, so stringent

occupancy schedules are to be enforced to control the facility

lighting systems. For after-hour office demands and/or

janitorial purposes, it is ideal to decentralize lighting

controllers (localization) or install occupancy-sensing light

controls similar to the Environmental Protection Agency

lighting retrofitting project to suit occupancy loads in the

workplace and trim on energy consumption. This can be

accomplished through means of detecting human presence andsignal to turn lights on by floor zones for a specified period.

Decent energy savings can be observed if normal occupancy

schedules have been adapted to control lighting on the bridge

area as well. Furthermore, daylight harvesting along with as-

designed lighting operations has to be considered when

improving cafeteria lighting. Although it is difficult for eQuest

to simulate the schedule, it is possible to find out the results of

reducing lighting in the cafeteria area by reducing the number

of watts per square footage.

Meanwhile, upon reviewing the Environmental Protection

Agency’s overhead lighting metered bill for 30 October 2002, a

power density figure of 0.88 W/f 2 (9.47 W/m2) has been

measured after the completion of the Environmental ProtectionAgency’s lighting retrofitting project. Office overhead lighting

load of 1.2 W/f 2 (12.92 W/m2) has been substituted for

Environmental Protection Agency’s calibrated figure in all

office zones.

In addition, normal occupancy schedule (6:00 a.m. to

6:00 p.m. with automatic shutdown for the night) takes place

at the bridge area to regulate lighting instead of the 24 h/day,

7 days/week current operational mode. These features are thenintroduced to the eQuest model for evaluation. Fig. 5 and

Table 7 show the results of the evaluation.

Table 7 includes four data series by month. The first is the

series of actual data collected from electricity bills of the

facility. The second, third and fourth series include data

generated from the simulation model after optimization steps

were introduced to the model respectively, i.e., heating

setpoints, the fan operation and lighting energy reduction.

Resetting heating setpoints was first introduced to the

system, which resulted in a 4% energy reduction (see Table 7)

compared to the total energy consumption of the ‘‘Actual’’ data

series. Then optimizing fan operation was added to the model,which resulted in 17% of energy reduction cumulatively com-

pared to the actual total energy consumption. On top of these

two strategies, lighting energy reduction was introduced to the

model. There was a total energy saving of 22% observed from

the simulation.

5. Energy evaluation

Three data series in Table 7, i.e., the Heating, the

Heating + Fan and the Heating + Fan + Lighting, were then

used as three situations of energy consumption and were input in

the portfolio manager separately for the evaluation. Table 8

below shows the results. The model shows that whenimplementing various energy-saving scenarios the facility can

achieve Energy Star label status of 75 or above. Upon inspection,


Fig. 5. Electricity consumption by different scenarios.

Table 7

Metered and simulated electricity usage (units: k Wh 1,000,000)

Month 1 2 3 4 5 6 7 8 9 10 11 12 Total % Saved

Actual 4.24 3.48 3.30 3.10 2.73 2.92 3.00 2.81 2.82 2.51 2.90 3.85 37.66

Heating 3.97 3.22 3.12 2.70 2.75 2.80 2.98 3.12 2.58 2.60 2.94 3.40 36.18 4.00

Heating + fan 3.30 2.68 2.72 2.41 2.42 2.45 2.60 2.77 2.22 2.32 2.52 2.71 31.12 17.00

Heating + fan + lighting 3.20 2.59 2.58 2.27 2.26 2.30 2.43 2.58 2.07 2.16 2.39 2.62 29.45 22.00


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using only the low-cost solutions, resetting heating setpoints or

fan operation, can reduce the overall energy consumption

significantly, but may not guarantee an Energy Star rating for the

facility considering variations in the simulated results. When

applying a combination of all three strategies, considerable

savings canbe attained, which offers a high possibility of leading

to the Energy Star designation (see Table 8)

6. Conclusion

The study has indicated that computer-based simulation is a

valuable technique to assist facility managers in determining

energy conservation solutions. This project, combining the

power of the portfolio manager developed by the Environ-

mental Protection Agency and eQuest by DOE2.com, has

analyzed the operation of an existing facility in order to

formulate methods for achieving the Energy Start designation.

Based on expert observations and qualitative analysis, three

strategies are formulated. This project is to quantify their

cumulative results to determine whether they are sufficient for

the energy saving goal. According to the study, resetting

heating setpoints alone will not lead to sufficient energy savingsto achieve the designation. The combination of resetting

heating setpoints and controlling the fan operation of air

handling units will result in sufficient energy savings, which

may be enough for getting the designation. However, a

combination of the three strategies will most likely be sufficient

enough for achieving the Energy Star designation.

In addition, through this study, some observations regarding

the use of the simulation tool have been made. Although

computer-based simulation offers a valuable tool to energy

studies like the one performed in this project, the process of

creating the simulation model is very time consuming and

resource demanding. Especially, for a complex facility such as

the facility, correctly defining thermal zones becomes a majorchallenge, because thermal zones will significantly affect the

simulation results. On the other hand over defining thermal

zones will complicate the work. Another challenge is acquiring

data, which is the base for building a viable model. The success

of a simulation project is often at the mercy of the availability of

data, such as as-built building data, system specifications,

operation schedules and so on. This requirement may put

some facility off the limit for simulation, as there might be

tremendous difficulty in getting proper data. Otherwise, the

accuracy of the model will be compromised. In addition, there

are still limitations in the tools, which prohibit the model from

reflecting the reality. For example, there is only one type of

operation schedule that can be used in one simulation

calculation. However, in realty, during the time period that a

model covers, there might be several different operation

schedules of the same type. In existing simulation tools such as

eQuest, this cannot be modeled. All these limitations of the

existing tools will eventually contribute to the disparity

between the results from the actual electricity bills and

the models.

Nevertheless, the simulation offers a reusable and effective

tool for energy efficiency studies. For example, the cross-effect

of two energy saving plans such as lighting energy reduction

and HVAC fan operation schedule can be calculated by the

simulation model, while a manual-based audit may overlook

such effect.

The study has also observed that existing simulation tools

are in general lack of capabilities to integrate with other

software tools such as cost estimation. It would be more helpful

to facility managers, if a simulation tool can also generate a cost

estimate with respect to a specific energy conservation solution,

because eventually cost savings are one of the most convincingparameters that facility managers will consider in order to make

a decision of selecting an energy conservation solution.

Acknowledgements

The author would like to express thanks to the General

Services Administration field office for providing as-built

information, as well as participation in various surveys. The

author also would like to thank General Services Administra-

tion for providing financial support for this project.

References

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Table 8

Energy Star evaluation results

Solution Floor space (m2) Actual energy

intensity (k Wh/m2)

Score

Heating 138199.31 262.25 66

Heating + fan 138199.31 245.23 77

Heating + fan +

lighting

138199.31 213.39 80


8/8

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