Analysis of the Energy 

Performance of the Chesapeake 

Bay Foundation’s Philip Merrill 

Environmental Center 

April 2005 • NREL/TP-550-34830 

B. Griffith, M. Deru, P. Torcellini, and P. Ellis 

National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337

April 2005 • NREL/TP-550-34830 

Analysis of the Energy 

Performance of the Chesapeake 

Bay Foundation’s Philip Merrill 

Environmental Center 

B. Griffith, M. Deru, P. Torcellini, and P. Ellis 

Prepared under Task No. BEC3.4001 
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i 

Acknowledgments 

This work was made possible by the U.S. Department of Energy’s Office of Energy Efficiency and 

Renewable Energy through its High Performance Building’s initiative (HPBi). We appreciate the 

support and guidance of Dru Crawley, program manager, HPBi. The authors would also like to thank 

Roger Perry, building operations manager at the Philip Merrill Environmental Center, for his gracious 

assistance to researchers. In addition, the authors want to recognize the contributions by NREL 

researchers Nicholas Long (he processed and managed the data from the monitoring systems) and Kris 

Lasnik (he helped with revisions for the final version). Finally, we would like to thank the following 

people for their careful review and thoughtful comments: Dr. Michael McCabe of Navigant Consulting 

and Ed Hancock of Mountain Energy Partnership. Ed Hancock also designed and implemented the data 

acquisition system. 
ii 

Contents 

Acknowledgments ........................................................................................................................................i

Contents .......................................................................................................................................................ii

Figures..........................................................................................................................................................v

Tables ........................................................................................................................................................viii

Executive Summary ...................................................................................................................................ix

1 Introduction ..........................................................................................................................................1

1.1 Energy Use in Commercial Buildings in the United States .............................................................1

1.2 High-Performance Buildings Research Objectives..........................................................................2

1.3 Building Evaluation Scope ..............................................................................................................3

1.4 Report Organization.........................................................................................................................3

2 Background...........................................................................................................................................4

2.1 Energy Comparison Data.................................................................................................................4

3 Design Process.......................................................................................................................................5

4 As-Built Building Description .............................................................................................................6

4.1 Building Envelope ...........................................................................................................................8

4.2 Lighting and Daylighting.................................................................................................................9

4.3 Description of Photovoltaic System...............................................................................................11

4.4 Hybrid Natural Ventilation ............................................................................................................12

4.4.1 Hybrid Natural Ventilation – Prior Research.....................................................................14

4.5 Mechanical Systems.......................................................................................................................15

4.5.1 Ground-Source Heat Pumps ...............................................................................................15

4.5.2 Desiccant System.................................................................................................................18

4.5.3 Water Heating.....................................................................................................................18

4.6 Equipment and Water Systems ......................................................................................................22

4.6.1 Potable Water System .........................................................................................................22

4.6.2 Nonpotable Water System ...................................................................................................22

4.6.3 Rainwater Collection Systems.............................................................................................22

4.6.4 Water Conservation and Resource Protection....................................................................22

5 Whole-Building Energy Evaluation..................................................................................................23

5.1 Whole-Building Evaluation Methodology.....................................................................................23

5.2 Whole-Building Monitoring ..........................................................................................................24

5.2.1 Measurement Procedure.....................................................................................................24

5.2.2 Instrumentation ...................................................................................................................24

5.2.3 Data Acquisition .................................................................................................................27

5.2.4 Measured Data Processing.................................................................................................28

5.2.5 Data Handling ....................................................................................................................28

5.2.6 Daylight Saving Time..........................................................................................................30

5.2.7 Filling Missing Data ...........................................................................................................30
iii 

5.2.8 Monitoring System Uncertainty ..........................................................................................35

5.2.9 Monitoring Results..............................................................................................................35

5.3 Development of Building Energy Models .....................................................................................47

5.3.1 EnergyPlus Building Simulation Tool.................................................................................48

5.3.2 Modeling Inputs ..................................................................................................................48

5.3.3 Summary of Baseline Results ..............................................................................................54

5.4 Whole-Building Energy Performance Results...............................................................................56

5.4.1 Site versus Source Energy and Energy Cost Savings..........................................................56

5.4.2 Measured Versus Predictions .............................................................................................57

6 Subsystem Evaluations.......................................................................................................................59

6.1 HVAC Performance Evaluation ....................................................................................................59

6.1.1 Ground-Source Heat Pumps ...............................................................................................60

6.1.2 Natural Ventilation Evaluation...........................................................................................65

6.2 Photovoltaic System Evaluation ....................................................................................................66

6.2.1 Photovoltaic System Evaluation Methods...........................................................................66

6.2.2 Photovoltaic System Evaluation Results .............................................................................66

6.3 Ancillary Daylighting Evaluation ..................................................................................................69

6.3.1 Daylighting Evaluation Methods ........................................................................................69

6.3.2 Daylighting and Lighting Evaluation Results.....................................................................70

7 Recommendations ..............................................................................................................................76

7.1 Design Process...............................................................................................................................76

7.2 Operation .......................................................................................................................................77

7.3 Alterations......................................................................................................................................77

7.3.1 Lighting...............................................................................................................................77

7.3.2 Ground-Source Heat Pumps ...............................................................................................78

7.3.3 Outside Air System..............................................................................................................79

7.3.4 Exterior Shading .................................................................................................................79

7.4 Energy Performance Monitoring and Analysis..............................................................................79

7.4.1 Monitoring Equipment ........................................................................................................79

7.4.2 As-Built Modeling ...............................................................................................................81

7.4.3 Real-Time Modeling............................................................................................................81

8 Conclusions .........................................................................................................................................82

9 References ...........................................................................................................................................83

Appendix A Explanation of Time Map Figure Art .........................................................................85

Appendix B Selected Results from Detailed Monitoring ................................................................87

Appendix C Weather Data for EnergyPlus Modeling.....................................................................96

Appendix D Calibration Schedules for EnergyPlus Modeling .....................................................107

Appendix E Cloud Cover Modeling................................................................................................116

Appendix F Baseline Energy Model Error Analysis.....................................................................117

F.1 Geometry .....................................................................................................................................117

F.2 Input Perturbation Methods for Error Analysis ...........................................................................121
iv 

F.3 Differential Sensitivity Analysis..................................................................................................123

F.4 Monte Carlo Analysis ..................................................................................................................125

F.5 Weather Data Sensitivity .............................................................................................................126

F.6 Overall Uncertainty Estimate.......................................................................................................127

Appendix G List of People Involved in Project..............................................................................130
v 

Figures 

Figure 1-1 Typical site EUIs by end use for office buildings (kBtu/ft2·yr) (DOE 2004)........................2

Figure 2-1 1999 Northeast site energy intensity by building type ..........................................................4

Figure 4-1 Merrill Center north entrance shows rainwater storage tanks and north clerestories............6

Figure 4-2 First- and second-floor plans for the Merrill Center..............................................................7

Figure 4-3 Isometric sketch of the Merrill Center shows building features............................................8

Figure 4-4 T-8 overhead lighting on the first floor (100% indirect) .....................................................10

Figure 4-5 T-8 overhead lighting and task lighting on the second floor (50% direct/50% indirect) ...10

Figure 4-6 Schematic of the PV array system.......................................................................................11

Figure 4-7 PV array and shading caused by building structure ............................................................12

Figure 4-8 Intended airflow pathways for natural ventilation from the design phase...........................13

Figure 4-9 Signage directs occupants to operate windows ...................................................................14

Figure 4-10 Location of heat pump thermal zones..................................................................................17

Figure 4-11 Ground heat exchanger........................................................................................................18

Figure 4-12 Desiccant dehumidification system.....................................................................................18

Figure 4-13 Evacuated-tube solar collector arrays..................................................................................19

Figure 4-14 Water heating boiler configuration......................................................................................20

Figure 4-15 Water heating equipment schematic....................................................................................21

Figure 5-1 Timeline of monitoring system and postoccupancy evaluation ..........................................23

Figure 5-2 Whole-building evaluation flowchart..................................................................................24

Figure 5-3 Electrical monitoring points ................................................................................................25

Figure 5-4 Data collection, storage, and processing .............................................................................29

Figure 5-5 Fraction of first-floor plugs to sum of plugs and lights.......................................................31

Figure 5-6 Lighting load data for main building first floor: left map is the original data, right map is 

corrected ..............................................................................................................................32

Figure 5-7 Receptacle/plug load data for main building first floor: left map is original, right map is 

filled.....................................................................................................................................33

Figure 5-8 Source data and model used to fill data on boiler propane use ...........................................34

Figure 5-9 Summary of utility electricity bills......................................................................................36

Figure 5-10 Energy end-use breakdown by billing cycle........................................................................39

Figure 5-11 HVAC energy end-use breakdown by billing cycle............................................................40

Figure 5-12 Coincident end uses at time of peak demand by billing cycle.............................................43

Figure 5-13 Peak demand: working weekdays ......................................................................................44

Figure 5-14 Peak demand: Saturdays, Sundays, and holidays...............................................................44

Figure 5-15 Time of day at peak demand: working weekdays ..............................................................45

Figure 5-16 Time of day at peak demand: Saturdays, Sundays, and holidays.......................................45

Figure 5-17 January 2002 average daily profile......................................................................................46

Figure 5-18 August 2002 average daily profile.......................................................................................46

Figure 5-19 Data rearrangement for modeling analysis year ..................................................................49

Figure 5-20 System diagram of EnergyPlus model for rooftop packaged unit.......................................53

Figure 5-21 Mean baseline result for site energy consumption by end use ............................................54

Figure 5-22 Mean baseline result for source energy consumption by end use .......................................55

Figure 5-23 Mean baseline result for energy cost by end use.................................................................55

Figure 6-1 Daily HVAC electricity consumption versus average outdoor air temperature ..................59

Figure 6-2 Daily total estimate for heat rejection to ground .................................................................62

Figure 6-3 Daily average ground-loop supply temperature ..................................................................62

Figure 6-4 Short-term response of ground loop: January 18, 2002.......................................................63

Figure 6-5 Short-term response of ground loop: June 27, 2002............................................................63
vi 

Figure 6-6 Temperature difference between water loop supply and outdoor air when greater than 0.0 

and not heating ....................................................................................................................64

Figure 6-7 Distribution of wind direction during periods amenable to natural ventilation over the 

model analysis year .............................................................................................................65

Figure 6-8 Daily PV power production and solar resource...................................................................67

Figure 6-9 Average hourly AC power profile by month for PV power production by billing period ..68

Figure 6-10 Monthly PV power production compared to simulated results ...........................................69

Figure 6-11 Sample of automated photometric measurements on the second floor (September 22, 

2001)....................................................................................................................................70

Figure 6-12 Locations for automated photometric measurements on the second floor (September 22, 

2001)....................................................................................................................................71

Figure 6-13 Handheld photometric measurements on second floor: June 30, 2001: 1:00 p.m. (bright 

diffuse sky with electric lights off)......................................................................................71

Figure 6-14 Handheld photometric measurements on second floor:.......................................................72

Figure 6-15 Handheld photometric measurements on second floor:.......................................................72

Figure 6-16 Glare problems caused by daylighting ................................................................................73

Figure 6-17 Average weekday profile of energy use for second-floor lighting for January and June....74

Figure 6-18 Second-floor ceiling, beams, and ducts...............................................................................75

Figure A-1 Explanation of time map figure art......................................................................................86

Figure B-1 Time map of total site electricity use (kWh/15-min) in Eastern Standard Time in (a) color 

and (b) gray-scale ................................................................................................................87

Figure B-2 Time map of net site electricity use (kWh/15-min) in Eastern Standard Time in (a) color 

and (b) gray-scale ................................................................................................................88

Figure B-3 Time map of PV electricity generation (kWh/15-min) in Eastern Standard Time in (a) 

color and (b) gray-scale....................................................................................................... 89

Figure B-4 Time map of HVAC energy use for main building (kWh/15-min) in Eastern Standard 

Time in (a) color and (b) gray-scale ....................................................................................90

Figure B-5 Time map of ground-loop pump energy use (kWh/15-min) in Eastern Standard Time in 

(a) color and (b) gray-scale..................................................................................................91

Figure B-6 Time map of first-floor air dry-bulb temperature (°C) in Eastern Standard Time in (a) 

color and (b) gray-scale.......................................................................................................92

Figure B-7 Time map of first-floor air relative humidity (%) in Eastern Standard Time in (a) color 

and (b) gray-scale ................................................................................................................93

Figure B-8 Time map of second-floor air dry-bulb temperature (°C) in Eastern Standard Time in (a) 

color and (b) gray-scale.......................................................................................................94

Figure B-9 Time map of second-floor air relative humidity (%) in Eastern Standard Time in (a) color 

and (b) gray-scale ................................................................................................................95

Figure C-1 Time map of outdoor air dry-bulb temperature (°C) in (a) color and (b) gray-scale...........97

Figure C-2 Time map of outdoor air dew-point temperature (°C) in (a) color and (b) gray-scale ........98

Figure C-3 Time map of outdoor air relative humidity (%) in (a) color and (b) gray-scale..................99

Figure C-4 Time map of horizontal infrared radiation (W/m2) in (a) color and (b) gray-scale ...........100

Figure C-5 Time map of global horizontal solar radiation (W/m2) in (a) color and (b) gray-scale.....101

Figure C-6 Time map of direct normal solar radiation (W/m2) in (a) color and (b) gray-scale...........102

Figure C-7 Time map of diffuse horizontal solar radiation (W/m2) in (a) color and (b) gray-scale....103

Figure C-8 Time map of wind direction (clockwise degrees from North) in (a) color and (b) grayscale...................................................................................................................................104

Figure C-9 Time map of wind speed (m/s) in (a) color and (b) gray-scale .........................................105

Figure C-10 Time map of total and opaque sky cover (tenths of coverage) in (a) color and (b) grayscale...................................................................................................................................106

Figure D-1 Time map of calibration schedule for first-floor receptacles loads, normalized factors in 

(a) color and (b) gray-scale................................................................................................107
vii 

Figure D-2 Time map of calibration schedule for first-floor lighting loads, normalized factors in (a) 

color and (b) gray-scale.....................................................................................................108

Figure D-3 Time map of calibration schedule for second-floor receptacle loads, normalized factors in 

(a) color and (b) gray-scale................................................................................................109

Figure D-4 Time map of calibration schedule for second-floor lighting loads, normalized factors in 

(a) color and (b) gray-scale................................................................................................110

Figure D-5 Time map of calibration schedule for conference pavilion receptacle and lighting loads, 

normalized factors in (a) color and (b) gray-scale.............................................................111

Figure D-6 Time map of calibration schedule for miscellaneous loads (elevator, exterior lighting, 

mechanical room receptacles), normalized factors in (a) color and (b) gray-scale ...........112

Figure D-7 Time map of calibration schedule for blended (first floor plus 18.4% of conference 

pavilion) first receptacle loads, normalized factors in (a) color and (b) gray-scale ..........113

Figure D-8 Time map of calibration schedule for blended (second floor plus 81.6% of conference 

pavilion) second-floor receptacle loads, normalized factors in (a) color and (b) gray-scale114

Figure D-9 Working weekday average plug load profiles by month for the first and second floors of 

the main building...............................................................................................................115

Figure F-1 Baseline building model geometries, (a) box with aspect ratio 1.0, (b) box with aspect 

ratio 4.4, and (c) same as built...........................................................................................119
viii 

Tables 

Table ES-1 Annual Performance Metrics.................................................................................................x

Table 4-1 Building Envelope Construction and Nominal Thermal Properties ......................................9

Table 4-2 LPD by Zone .......................................................................................................................11

Table 4-3 Natural Ventilation System Control Set Points (Chang 2002) ............................................15

Table 4-4 Water-to-Air Heat Pump Design Data.................................................................................16

Table 5-1 Measurement Points and Sensors: Main Building..............................................................26

Table 5-2 Measurement Points and Sensors: Conference Building....................................................27

Table 5-3 Electrical Energy Costs from Utility Billing .......................................................................36

Table 5-4 Comparison of Utility Bills to Measured Data....................................................................37

Table 5-5 Site Energy End-Use Characterization: Monthly ...............................................................39

Table 5-6 HVAC Site Energy Use Characterization: Monthly...........................................................40

Table 5-7 Miscellaneous Site Energy Use Characterization: Monthly...............................................41

Table 5-8 Electrical Demand Characterization....................................................................................42

Table 5-9 Weather Data for Energy Simulations Derived from Measurements..................................50

Table 5-10 Calibration Schedules for Modeling from Measured Data..................................................51

Table 5-11 Envelope Performance Specifications for Baseline Models................................................52

Table 5-12 Electricity Pricing Structure ................................................................................................54

Table 5-13 Summary of Results for Overall Energy Intensity - Baseline Building ..............................56

Table 5-14 Overall Energy Savings Levels ...........................................................................................56

Table 5-15 HVAC&L Energy Savings Levels ......................................................................................57

Table 5-16 Predicted versus Monitored EUIs........................................................................................58

Table 6-1 Heat Pump Cooling and Heating Characterization..............................................................61

Table 6-2 PV Power Performance Summary: November 7, 2001 – November 6, 2002 ....................66

Table 8-1 Energy Savings Summary: November 2001–November 2002...........................................82

Table C-1 Weather Summary Statistic: Measurement versus Baltimore TMY2 .................................96

Table C-2 Comparison of Maximum Solar Radiation Values............................................................104

Table F-1 Baseline Energy Model Results for Site Energy Use ........................................................120

Table F-2 Baseline Energy Model Results for Source Energy Use ...................................................120

Table F-3 Baseline Energy Model Results for Energy Costs.............................................................121

Table F-4 Input Parameters Included in DSA and MCA and Uncertainties......................................122

Table F-5 DSA of Uncertainty in Baseline Model Predictions: Annual Site Energy Use................123

Table F-6 DSA of Uncertainty in Baseline Model Predictions: Annual Source Energy Use ...........124

Table F-7 DSA of Uncertainty in Baseline Model Predictions: Annual Energy Cost ......................125

Table F-8 MCA of Uncertainty in Baseline Model Predictions: Annual Site Energy Use...............126

Table F-9 MCA of Uncertainty in Baseline Model Predictions: Annual Source Energy Use...........126

Table F-10 MCA of Uncertainty in Baseline Model Predictions: Annual Energy Cost......................126

Table F-11 Weather Data Error Analysis for Baseline Energy Model Results for Site Energy ..........127

Table F-12 Weather Data Error Analysis for Baseline Energy Model Results for Source Energy .....127

Table F-13 Weather Data Error Analysis for Baseline Energy Model Results for Energy Costs........127

Table F-14 Overall Error Analysis for Baseline Energy Model Results for Site Energy Use .............128

Table F-15 Overall Error Analysis for Baseline Energy Model Results for Source Energy Use ........128

Table F-16 Overall Error Analysis for Baseline Energy Model Results for Energy Costs..................129
ix 

Executive Summary 

Introduction 

The Chesapeake Bay Foundation (CBF) is dedicated to restoring and protecting the resources of the 

Chesapeake Bay, North America’s largest estuary and a seriously threatened ecosystem. In 2000, CBF 

built the 31,000-ft2

 (2,880-m2) Philip Merrill Environmental Center on a 31-acre (12.5-ha) site in 

Annapolis, Maryland, to serve as foundation headquarters. CBF incorporated numerous highperformance energy-saving features (including a ground-source heat pump system to heat the building in 

winter and help cool it in summer) into the building to minimize its environmental effects on the bay. 

Large, south-facing windows provide additional passive solar heating and reduce lighting loads by 

allowing more use of natural light (daylighting). Building daylighting is monitored by sensors that 

automatically dim some lights when daylighting is sufficient to displace electric lighting. Manually and 

motor-operated windows attempt to use the Chesapeake Bay’s breezes for natural ventilation. An energy 

management system tracks the outdoor temperature and humidity and uses this information to control the 

building’s heating, ventilation, and air-conditioning system. The National Renewable Energy Laboratory 

(NREL) selected the Merrill Center as a technical case study after being contacted by CBF to provide an 

unbiased energy performance evaluation. Because the center has attracted much attention in the 

sustainable design community, an unbiased evaluation was necessary to help designers replicate successes 

and identify and correct problem areas. This report focuses on the monitoring and analysis of the 

building’s overall energy performance. 

Research Goals and Approach 

This report is part of a series of six case studies to develop, document, analyze, and evaluate the processes 

by which highly energy-efficient buildings can be reliably produced. NREL established the Merrill 

Center evaluation with the following goals: 

• Study the overall energy performance of a Leadership in Energy and Environmental DesignTM 

Platinum certified building. 

• Analyze the integration of energy-efficient features, such as natural ventilation, daylighting, groundsource heat pumps, and a photovoltaic (PV) system, into an office building. 

• Apply the International Energy Agency/Solar Heating and Cooling (IEA/SHC) Task 21 Daylighting 

monitoring protocol. 

• Determine how the building performance matches the design expectations. 

NREL collected energy data via monitoring meters and computer simulation to assess the energy 

performance of the building. Electronic data acquisition systems were designed to measure the important 

electrical and thermal flows and local weather conditions. Measurements were collected continuously 

beginning in August 2001; analysis focused on the period from November 7, 2001 through November 6, 

2002. In addition, NREL used computer modeling to develop a benchmark for energy savings. Baseline 

energy modeling determines how the building would perform if it had been built according to the 

minimum code standards of the American Society of Heating, Refrigerating and Air-Conditioning 

Engineers (ASHRAE) under standard ASHRAE/IESNA 90.1-2001. NREL also applied a new 

informational Appendix G contained in Addendum E of the ASHRAE standard. NREL also performed 

error analysis of the baseline energy model that accounted for errors in input, ambiguities in determining 

geometry of baseline buildings, and potential errors in the weather file. NREL used EnergyPlus, a wholebuilding energy analysis software program sponsored by the U.S. Department of Energy, to perform 
x 

computer simulations. Data from an entire year of monitoring measurements were used to assemble an 

annual weather file with 15-minute resolution. Monitoring data were also used to calibrate receptacle and 

other miscellaneous electrical loads. 

Results 

Results of the performance monitoring and baseline analysis are summarized in Table ES-1. Error 

margins and percentage savings are also shown. 

On-site energy production from the PV system makes it important to distinguish between net and total 

energy use. Net energy use is the difference between the total energy use and the energy produced by the 

system. Energy performance is characterized by three categories of annual performance metrics: 

• Annual total or net site energy is the measure of energy use in the building (both net and total). 

• Annual source energy is the measure of energy consumed to produce and deliver the net energy. 

• Annual energy cost is the measure of the annual energy operating expense. 

Results for individual end uses are provided elsewhere in the report. 

Table ES-1 Annual Performance Metrics 

Total Site Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Site Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Source Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Energy Cost 

Intensity 

$/ft2·yr 

($/m2·yr)

Baseline Model 53.3 ±7.3 

(604.8 ±82.8) 

53.3 ±7.3 

(604.8 ±82.8) 

159.6 ±20.0 

(1,812 ±227) 

1.20 ±0.17 

(12.87 ±1.8) 

Monitoring 40.2 ±0.3 

(456.8 ±2.9) 

39.9 ±0.3 

(453.2 ±2.9) 

124.3 ±0.7 

(1,412 ±8) 

1.05 ±0.003 

(11.31 ±0.03) 

Savings (24.5 ±14.1)% (25.0 ±14.1)% (22.1 ±12.8)% (12.1 ±14.1)% 

As these percentages show, the Merrill Center performs well relative to the baseline energy model. This 

work identified specific recommendations that would improve the building’s energy performance (see 

Section 7). Some recommendations are related to the design; some involve possible changes to the 

Merrill Center’s systems and operation; others relate to research methods. 

The analysis of monitored data and computer simulation models culminates in conclusions (see Section 8) 

about the performance of the Merrill Center, building monitoring, and the baseline analysis. Conclusions 

drawn in this study include: 

• During cooling, the temperature of water returning from the ground heat exchanger, which supplies 

the water-to-air heat pumps, is often warmer than expected (and sometimes warmer than ambient), 

which indicates a need for additional research on the performance of ground-source heat pumps. 

• Daylight is not harvested as well as it could be on the second floor. 

• The Merrill Center is a good candidate for follow-on research. 

• A long-term monitoring effort can succeed in collecting detailed annual data sets for energy use and 

weather that are suitable for evaluating energy performance. 

• The new Appendix G for standard ASHRAE/IESNA 90.1-2001 is helpful for defining baseline 

energy models. 
xi 

• An analysis of error in the baseline building energy models shows that such models have considerably 

more uncertainty than actual measurements that establish a true energy balance. 

• Significant differences were found between the actual energy performance and predictions made for 

rating purposes. These differences indicate that there is a need to improve protocols for rating and to 

continue efforts to study actual buildings so that design models can better predict performance. 
1 

1 Introduction 

In 1964 a group of Baltimore businessmen who were dissatisfied with the polluted state of the 

Chesapeake Bay, founded the Chesapeake Bay Foundation (CBF), a privately funded organization whose 

primary goal is to restore this seriously threatened ecosystem. The bay has lost to pollution 

approximately 98% of its oysters, 90% of its underwater grasses, 60% of its wetlands, and 50% of its 

forests, according to CBF statistics. CBF built the Philip Merrill Environmental Center in Annapolis, 

Maryland, to serve as its headquarters for the promotion of environmental issues associated with the bay. 

Because the Merrill Center promoted sustainable commercial building design by using high-performance 

energy features, it helped to augment CBF’s mission. 

Among the Merrill Center’s high-performance energy-saving features are a good thermal building 

envelope, passive solar technology and ground-source heat pumps for heating and cooling, natural 

ventilation to reduce energy consumption from the air-conditioning system, and natural light to reduce 

lighting loads. Daylighting controls automatically dim some interior electric lights when natural, exterior 

daylight is sufficient. Other noteworthy features include a 4.2-kW photovoltaic (PV) system that helps to 

offset electrical loads and an array of evacuated-tube solar collectors that provides all the domestic hot 

water (DHW) needs. 

The National Renewable Energy Laboratory (NREL) selected The Merrill Center as a technical case 

study because the building integrates several high-performance energy-saving features into its design. 

NREL staff monitored the building for one year. This report focuses on monitoring results from 

electronic data acquisition systems specified and installed by NREL researchers. NREL was not involved 

in the design phase of the project. Computer modeling was used in the evaluation to establish a 

benchmark for determining energy savings. 

1.1 Energy Use in Commercial Buildings in the United States 

The operation of commercial buildings accounts for approximately 18% of the total primary energy 

consumption in the United States. The total for all buildings is more than one-third of the primary energy 

consumption and more than 70% of the electricity consumption. The operation of buildings in the United 

States results in 38% of U.S. and 9% of global carbon dioxide (CO2) emissions. Electricity consumption 

in the commercial building sector doubled between 1980 and 2002, and it is expected to increase another 

50% by 2025 (DOE 2004). Reducing site energy consumption in commercial buildings through energyefficient and renewable building technologies would significantly reduce primary energy consumption in 

the United States. Site energy is also a concern for the building owner or those responsible for paying the 

utility bills. 

Typical site energy consumption by end use for office buildings is shown in Figure 1-1 (DOE 2004). The 

building’s site energy use intensity (EUI) is 97.2 kBtu/ft2

·yr (1,104 MJ/m2·yr). These numbers are based 

on 1995 data collected by the Energy Information Administration (EIA). Most of the space heating, water 

heating, and cooking are by natural gas; the rest of the energy consumption is electricity. The primary 

energy consumed to generate and distribute the electricity is approximately three times the energy used on 

site. Lighting is the largest primary energy end use; therefore, a primary objective in office buildings 

should be to reduce lighting loads. 
2 

24.3

9.1

5.2

8.7

28.1

1.1

0.4

15.1

5.2

Space Cooling

Space Heating

Other

Ventilation

Water Heating

Lighting

Cooking

Refrigeration

Office Equipment

Figure 1-1 Typical site EUIs by end use for office buildings (kBtu/ft2·yr) 

(DOE 2004) 

1.2 High-Performance Buildings Research Objectives 

The High-Performance Buildings Research project at NREL evaluates commercial building design from a 

whole-building perspective for the U.S. Department of Energy (DOE). NREL’s whole-building 

perspective seeks to understand the integration issues of design and building operation to substantially 

reduce energy costs in commercial buildings and improve other attributes such as occupant satisfaction. 

Documenting the high performance of research-level buildings supplies examples that help to transform 

the marketplace. In addition, documenting common threads and analysis methodologies provides direct 

assistance to industry. The objectives of the research are to: 

• Develop high-performance building design, construction, and operation processes. 

• Provide the tools needed to replicate the processes and design strategies for creating highperformance buildings. 

• Research new technologies for high-performance buildings. 

• Develop standardized metrics and procedures for measuring building energy performance. 

• Measure and document building performance in high-profile examples. 
3 

1.3 Building Evaluation Scope 

NREL collaborated with CBF staff to monitor, evaluate, and document the energy performance of the 

Merrill Center from November 2001 through November 2002. NREL staff did not participate in the 

design process, but performance issues related to design decisions are discussed in this report. The 

Merrill Center has attracted a lot of attention in the sustainable design community, so an unbiased, realworld evaluation of the energy performance was made available so that designers can better learn from 

the project. In addition, problems with building equipment and operations can be identified and perhaps 

be corrected or otherwise be changed to improve the performance of the building. 

Before monitoring began, a plan was developed to measure the overall building energy use and some 

individual energy end uses. The intent of the monitoring was to assess how the building performs as a 

whole system rather than to focus on individual aspects of the design or operation. Designing a 

monitoring project always requires balancing the extra cost and complexity of additional measurements 

with the usefulness of the expected results. For the Merrill Center, the focus was on the whole-building 

performance. The plan specifically did not call for detailed evaluation of several interesting subsystems 

and features of the Merrill Center, including natural ventilation, solar DHW, potable and nonpotable 

water systems, nor all the various sustainability measures that do not directly relate to energy 

performance. Subsystems that were evaluated to some extent include the PV system power, daylighting, 

and ground-source heat pumps. 

1.4 Report Organization 

This report contains an executive summary, eight primary sections, a list of references, and appendices. 

Section 2 provides background information. Section 3 describes the design process. Section 4 details the 

physical components of the Merrill Center. Section 5 contains whole-building energy evaluation methods 

and results. Section 6 discusses the evaluation of specific subsystems. Sections 7 and 8 present 

recommendations and conclusions drawn from this study. The appendices contain a selection of detailed 

data from the monitoring effort, weather data and calibration schedules for the building energy modeling, 

and a description of the error analysis. 
4 

2 Background 

2.1 Energy Comparison Data 

To fully evaluate the Merrill Center’s energy performance, NREL needed comparison data. NREL began 

its analysis by examining data EIA collected on thousands of U.S. commercial buildings. In 1999, EIA 

conducted the Commercial Buildings Energy Consumption Survey (CBECS), a national survey that 

collected information, including building characteristics, energy consumption, and energy expenditures 

for all types of commercial buildings, on thousands of building types in the United States. CBECS 

concluded that office buildings account for 19% of all energy and 25% of all electrical energy used in 

commercial buildings. The survey also showed that all commercial buildings in the Northeast Census 

Region had an average site EUI of 90.3 kBtu/ft2

·yr (1026 MJ/m2·yr). Office building site energy intensity 

averaged 90.7 kBtu/ft2

·yr (1030 MJ/m2·yr), as shown in Figure 2-1. 

90

135.8

104

90.7 84.2

44.4 41.2

$1.19

$1.82

$1.69

$1.82 $1.77

$0.51

$0.79

0

20

40

60

80

100

120

140

160

Warehouse Religious

Worship

Education Retail Lodging Office Public

Assembly

Energy Use Intensity (kBtu/ft2

)

0.0

0.5

1.0

1.5

2.0

2.5

Energy Cost Intensity ($/ft2

)

Energy Use Intensity Energy Cost Intensity

Figure 2-1 1999 Northeast site energy intensity by building type 

Overall, CBECS found that office buildings consumed 1,089 trillion Btu (1,149 million GJ) of combined 

site electricity, natural gas, fuel oil, and district steam or hot water, which makes them the top energy 

consumer of all the commercial building types. Figure 1.1 shows the national end-use distribution. The 

survey also found that most site energy use was for space heating; lighting and cooling are the two next 

most energy-intensive end uses. 

Various categories from the CBECS database were then considered. Similar-sized office buildings had an 

average energy intensity of 83.1 kBtu/ft2

·yr (944 MJ/m2·yr) and recently constructed office buildings had 

an average energy intensity of 78.0 kBtu/ft2

·yr (886 MJ/m2·yr). Although the CBECS survey provided 

data for national and regional comparisons, a local comparison was not established as local data are 

limited. 
5 

3 Design Process 

Because NREL was not involved in the design of this building, this section presents only a brief overview 

of the design process. The design of the Merrill Center was led by the Washington, D.C. office of the 

architecture firm SmithGroup, Inc. Early in the design process, CBF’s design team established a goal of 

achieving a Gold or Platinum rating under the U.S. Green Building Council’s (USGBC) Leadership in 

Energy and Environmental Design (LEEDTM) certification program. The LEED certification goal had a 

strong influence on the building design; however, conserving the Chesapeake Bay was the organization’s 

first priority, which meant that energy features were not optimized when they conflicted with 

conservation features. 

The Sustainable Building Industries Council organized a peer review of the concept design (funded by 

DOE) that included reviewers from the Maryland Energy Administration, Maryland Department of 

Natural Resources, and the World Wildlife Fund. One of these reviewers, Greg Franta, used the 

Energy-10 computer program to conduct an early-phase design analysis. He used an integrated approach 

to design, and in-house engineers worked closely with architects. Benchmarking tours of other green 

projects and CBF’s other educational centers were influential to the final design. 

The team architect, who was already familiar with the design and project goals, administered the 

construction. Unfortunately, the contractor was not equally invested in the project goals had limited 

experience with high-performance building systems. 

After the Merrill Center was built, CBF did reach its goal; the Merrill Center was the first building to be 

awarded a LEED Platinum certification from USGBC under version 1.0 of the LEED program (USGBC 

2003). However, this report focuses on the energy use of the building, which is just one part of the LEED 

rating and CBF’s goals for the building. 

As part of the design process, the design team performed initial energy predictions for the building that 

were used as part of the documentation for the LEED certification. These predictions, along with the 

actual performance of the building, are shown in Section 5.4.2. 
6 

4 As-Built Building Description 

The Merrill Center is a 31,000-ft2 (2,880-m2) building that serves as office space for 80 to 90 people, 

provides areas for training and education, and includes a small conference pavilion. The conference 

pavilion is separate from the main building and is connected by an enclosed walkway on the second floor. 

A kitchen and dining area for the staff are located in the conference pavilion. The south wall is mostly 

glass to provide a view of the bay, daylighting, and passive solar heating. North-facing clerestory 

windows provide additional daylighting and are operable for natural ventilation. Individual heating, 

ventilation, and air-conditioning (HVAC) systems are controlled by an energy management system 

(EMS). The EMS allows the building operator to monitor and control the HVAC and lighting 

(HVAC&L) systems from a desktop computer. Figure 4-1 shows a picture of the Merrill Center’s 

entrance as viewed from the north. Figure 4-2 shows the plans of the first and second floors. The second 

floor is open to the first floor along the south wall of the main building. The overall length of the 

building in the east-west direction is 220 ft (67 m). 

The design philosophy of the Merrill Center was sustainability from cradle to grave, wherein all materials 

used in construction are environmentally safe or recyclable throughout the lifetime of the building. In 

addition, resources were chosen from local suppliers to reduce vehicle pollution from unnecessary 

transportation. Many of these materials are also considered rapidly renewable, given their relatively 

quick rate of replenishment (< 10 years). Forest materials used in building construction were either 

drawn from sustainable forests or certified by the Forest Stewardship Council. 

CBF’s commitment to protecting the bay extends beyond the building; CBF also promotes 

environmentally sound transportation options for its employees. Showers, lockers, and bicycle storage 

areas enable employees to walk, bike, or kayak to work. CBF also provides natural gas and hybrid 

vehicles for errands. Videoconferencing and a telecommuting policy minimize transportation, and CBF 

arranges carpooling and has lunch delivered daily. 

Figure 4-3 depicts the Merrill Center and highlights many of the energy design measures. Located at 

grade level under the first floor are a storage area, mechanical rooms, and an open air parking area, which 

prevents polluted runoff from entering the bay. 

 

Figure 4-1 Merrill Center north entrance shows rainwater storage tanks and 

north clerestories 
7 

Figure 4-2 First- and second-floor plans for the Merrill Center 
8 

Figure 4-3 Isometric sketch of the Merrill Center shows building features 

4.1 Building Envelope 

Table 4-1 summarizes the high-performance thermal envelope used in the Merrill Center. The roof and 

most of the walls use structural insulated panels (SIPs). The SIPs are composed of oriented strand board 

(OSB) panels that sandwich expanded polystyrene foam. SIPs have foam cores with a thickness of either 

5.5 in. (0.14 m) or 7.5 in. (0.19 m). In many places the interiors are left unfinished, and the OSB is 

exposed to the inside. Windows use spectrally selective, low-e glazing with wood frames. 
9 

Table 4-1 Building Envelope Construction and Nominal Thermal Properties 

Envelope Component Construction 

(Outside to Inside) 

R-factor

h·ft2·°F/Btu

(m2·K/W)

6.5-in. (165-mm) 

Exterior Wall 

1⁄2-in. (13-mm) plywood, 51⁄2-in. (140-mm) SIP foam, 

1⁄2-in. (13-mm) plywood, 1⁄2-in. (13-mm) gypsum 

sheathing board 

28 

(4.9)

8.5-in. (216-mm) 

Exterior Wall and BuiltUp Roof 

1⁄2-in. (13-mm) plywood, 71⁄2-in. (191-m) SIP foam, 

1⁄2-in. (13-mm) plywood, 1⁄2-in. (13-mm) gypsum 

sheathing board 

38 

(6.7)

Interior Wall 

1⁄2-in. (13-mm) gypsum sheathing board, insulated 

metal frame, 1⁄2-in. (13-mm) gypsum sheathing 

board 

9.3 

(1.6)

Interior Floor 

6-in. (152-mm) sound absorption blanket, 3⁄4-in. (19-

mm) wood subfloor, 1⁄4-in. (6 mm) Masonite 

underlayment, 1⁄4-in. (6 mm) tile finish flooring 

19 

(3.3)

Exterior Floor 2-1⁄2-in. (64-mm) dense insulation, 3⁄4-in. (19-mm) 

wood subfloor, 1⁄4-in. (6 mm) tile finish flooring 

10 

(1.8)

Windows Double-pane, 0.7-in. (18-mm) Argon-filled insulating 

glass with low-e coating. Shading Coef = 0.47 

4.1 

(0.7)

South-facing windows form a passive part of the lighting and HVAC systems. In the summer, overhangs 

shade the glass from the high summer sun and shield the facility from unnecessary solar gains. (See 

Figure 4-7.) 

4.2 Lighting and Daylighting 

The first-floor office lighting is provided by indirect T-8 lamps with electronic ballasts. The first row of 

lights on the south side of the building near the windows is dimmable and controlled by photocells. 

Figure 4-4 shows the building interior when the first row of lights is off. The second-floor office 

luminaires are 50% direct and 50% indirect. The first two rows of lamps on the south side are controlled 

by photocells. Figure 4-5 shows the overhead fixtures for the second floor. Installed lighting power 

densities (LPDs) for the offices and conference lighting zones are shown in Table 4-2. Task lighting is 

provided at all workstations. The lights in the normally occupied areas and the exterior lights are 

controlled by the building automation system and have manual/timed override switches. These controls 

are in parallel, such that if one is on, the lights are on. The timed override switches are push buttons—

when the button is pushed, the switch enables the lights for a set time interval. The restroom lights are 

compact fluorescent lamps on occupancy sensors. Daylighting provides considerable natural light to the 

second-floor offices, the conference pavilion, and the lobby. 
10 

Figure 4-4 T-8 overhead lighting on the first floor (100% indirect) 

Figure 4-5 T-8 overhead lighting and task lighting on the second floor 

(50% direct/50% indirect) 
11 

Table 4-2 LPD by Zone 

Lighting Zone LPD 

W/ft2 (W/m2) 

First-Floor Offices 1.2 (12.9) 

Second-Floor Offices 1.6 (17.2) 

Conference Room 1.4 (15.1) 

4.3 Description of Photovoltaic System 

A 4.2-kW, thin-film PV system is mounted on the south side of the building. The panels are inclined at 

an angle of 30° and face approximately south. Three inverters feed the energy into one of the building 

electrical panels on the second floor, as shown in Figure 4-6. There is no storage or net metering1

because loads connected to these panels immediately use all the electricity generated. The building 

structure and conference pavilion partially shade the PV panels during the summer, which reduces the 

system production (see Figure 4-7). The system production improves in the winter because of the low 

sun angle, the large amount of reflection off the bay, and the bright diffuse southern sky. 

Figure 4-6 Schematic of the PV array system 

 

1

 Many utility companies give building owners credit for excess power produced by their PV systems through a netmetering program. 

PV Arrays 

Inverters 

To Building 

Loads
12 

Figure 4-7 PV array and shading caused by building structure 

4.4 Hybrid Natural Ventilation 

Figure 4-8 shows the design intent for natural ventilation in the Merrill Center, which uses operable 

windows and a geometry that is designed to take advantage of the breezes off the bay for natural 

ventilation. Low- and mid-height windows on the south side are grouped in banks of four and operated 

with hand cranks. Windows located high on the north side have motorized operators that are controlled 

by the building’s EMS. Figure 4-9 shows a photograph of one of the signs in the office areas that reads 

“Open Windows.” It is used to inform occupants when conditions are appropriate to open the manually 

operated windows. The natural ventilation system is a hybrid or mixed-mode system in that fans are often 

used to help move ventilation air. These fans exhaust air on the north side. A fan on the second floor 

exhausts air at a rate of 2,800 cfm (1.32 m3

/s). A fan on the first floor exhausts air at a rate of 5,600 cfm 

(2.64 m3

/s). This ventilation system represents an air change rate of about one air change per hour 

(ACH). Control of the hybrid natural ventilation is discussed in Section 4.4.1 and listed in Table 4-3. 

Control is based on sensor data for interior temperatures, interior humidity, and outdoor temperature. The 

north, motorized windows also have moisture sensors that detect when it is raining so that the windows 

can be automatically closed. 
13 

Figure 4-8 Intended airflow pathways for natural ventilation from the design 

phase 
14 

Figure 4-9 Signage directs occupants to operate windows 

4.4.1 Hybrid Natural Ventilation – Prior Research 

Chang (2002) studied the natural ventilation of the Merrill Center. He used monitoring equipment, 

simplified modeling, and occupant surveys for his evaluation. Chang concluded that the mixed-mode 

ventilation approach used at the Merrill Center shows good promise for application in commercial 

buildings in the eastern United States. He found that occupants were content with the natural ventilation. 

His monitoring results indicated that from May 16, 2001 to December 31, 2001, natural ventilation was 

used 34% of weekday working hours. Chang’s study occurred during the first year of occupation and 

found problems with the control system; he helped the building manager improve natural ventilation 

controls and predicted that operation would improve over the second year. Chang found that the original 

design natural ventilation set point, when outdoor temperatures were below 77°F (25°C), was too high. 

Table 4-3 provides a summary of building operation strategies developed by Chang (as of December 

2001). 
15 

Table 4-3 Natural Ventilation System Control Set Points (Chang 2002) 

Condition Strategy 

Natural Ventilation 

Outside air between 46° and 72°F (7.7° and 22.1°C), natural ventilation is available. 

During this period, assist fans may or may not be turned on. Occupants open 

manually operated windows at their own discretion. 

Assist Fans Interior temperatures > 72°F (22.1°C), fans are turned on. When temperature < 

68°F (19.9°C), fans are turned off. 

Summer Cooling The heat pump set point lies between 77° and 78°F (24.9° and 25.4°C). Humidity is 

controlled to 50% with a 10% range. 

Winter Cooling 

Allow interior to reach 81°F (27.1°C) before A/C heat pumps or natural ventilation is 

used. Once interior temperatures drop to 78°F (25.4°C), system is turned off. 

Once interior relative humidity reaches 65%, humidification is turned off. 

Once interior drops to 50% relative humidity, humidification is turned on. 2

Chang noted that winds tend to flow from the northwest when outdoor conditions are good for natural 

ventilation and contrasted this with the designer’s intent to take advantage of winds that blow off the bay 

from south to north. Chang also noted that the electric lights were often on when daylighting was 

sufficient. He also tested a technique that uses a low-cost, Web-based camera to monitor the manual 

operation of windows. He indicated that, despite some difficulties, the approach showed promise. 

4.5 Mechanical Systems 

The Merrill Center uses ground-source heat pumps as the primary source of heating and cooling. Fin and 

tube radiators connected to a propane boiler provide supplementary heating in the entryways, middle 

landing of the main stairway, and along the glazed south wall of the main building. This section describes 

these systems. 

4.5.1 Ground-Source Heat Pumps 

Table 4-4 lists the characteristics of the individual air-handling units served by a single groundwater loop. 

Figure 4-10 shows the locations of thermal zones served by heat pumps in the main occupied areas of the 

building. Eighteen heat pumps and one air conditioner are connected in parallel to the ground loop. The 

heat pumps are packaged water-to-air units with integral, constant volume air systems. The cooling only 

air conditioner (ACU-1) cools a computer server room. Two small heat pumps (HP-1 and HP-18) serve 

unoccupied areas on the ground floor and are not shown in Figure 4-10. 

Figure 4-11 diagrams the ground heat exchanger (although only half the wells are drawn). The ground 

loop is connected to four circuits; each circuit has 12 vertical geothermal wells or boreholes. Each heat 

pump package is plumbed in parallel with the ground loop. Water is circulated with redundant parallel 

pumps. The loop pumps are variable speed and are controlled to maintain a pressure set point determined 

by the EMS. The operator manually adjusts the pressure set point to achieve a high temperature 

difference across the wells. As various heat pump packages cycle on or off, that portion of the water loop 

is opened or closed, which affects the pressure. Outside air is introduced via two air handlers, one in the 

main building and one in the conference pavilion. The air handlers provide the supply and return air 

streams for outside and relief air; they contain a heat recovery wheel and a desiccant wheel. (The heat 

recovery and desiccant systems are not being used.) Fresh outdoor air is introduced to the individual heat 

 

2

 The building operator has since adopted a fan assist mode for winter cooling when windows are left closed, but the 

assist fans are run to introduce outside air from infiltration. 
16 

pump air units at the maximum rates shown in Table 4-4. The constant volume outdoor air system has 

two stages (in addition to off) and a CO2 sensor determines which stage is needed. The total nominal 

cooling capacity is 115 tons (404 kW) or 270 ft2

/ton (7 m2/kW). 

Table 4-4 Water-to-Air Heat Pump Design Data 

Heat Pump 

Water Loop 

Flow Rate 

gal/min; gpm 

(L/s) 

Air System 

Flow Rate 

ft3/min; cfm 

(m3/s) 

Design Outside 

Air Ventilation 

ft3/min; cfm 

(m3/s) 

Nominal 

Cooling Power 

kBtu/h

(kW) 

Nominal 

Heating Power

kBtu/h

(kW) 

HP-1 2.5 

(0.16)

339.0 

(0.16)

106.0 

(0.05)

10.9 

(3.19)

9.8 

(2.86)

HP-2 16.0 

(1.01)

1,865.0 

(0.88)

360.0 

(0.17)

63.5 

(18.61)

63.4 

(18.58)

HP-3 28.0 

(1.77)

3,814.0 

(1.80)

360.0 

(0.17)

129.3 

(37.91)

115.0 

(33.69)

HP-4 16.0 

(1.01)

1,865.0 

(0.88)

381.0 

(0.18)

63.5 

(18.61)

63.4 

(18.58)

HP-5 28.0 

(1.77)

3,836.0 

(1.81) 

360.0 

(0.17)

129.3 

(37.95)

115.0 

(33.72)

HP-6 16.0 

(1.01)

1,865.0 

(0.88)

848.0 

(0.40)

63.5 

(18.61)

63.4 

(18.58)

HP-7 16.0 

(1.01)

1,865.0 

(0.88)

805.0 

(0.38) 

63.5 

(18.61)

63.4 

(18.58)

HP-8 13.0 

(0.82)

2,161.0 

(1.02)

339.0 

(0.16)

60.3 

(17.67)

59.2 

(17.34)

HP-9 28.0 

(1.77)

3,984.0 

(1.88)

339 

(0.16)

130.5 

(38.24)

115.7 

(33.92)

HP-10 28.0 

(1.77)

4,026.0 

(1.90)

0.0 

(0.0)

130.6 

(38.29)

115.8 

(33.95)

HP-11 16.0 

(1.01)

2,458.0 

(1.16)

191 

(0.09)

80.2 

(23.52)

83.1 

(24.36)

HP-12 28.0 

(1.77)

4,047.0 

(1.91)

0.0 

(0.0)

130.8 

(38.35)

116.0 

(33.99)

HP-13 28.0 

(1.77)

3,475.0 

(1.64)

254.0 

(0.12)

121.5 

(35.61)

113.5 

(33.28)

HP-14 15.0 

(0.95)

1,611.0 

(0.76)

403.0 

(0.19) 

49.4 

(14.47) 

61.1 

(17.91) 

HP-15 9.0 

(0.57) 

1,102.0 

(0.52) 

445.0 

(0.21) 

37.3 

(10.93) 

39.2 

(11.49) 

HP-16 12.0 

(0.76) 

1,208.0 

(0.57) 

275.0 

(0.13)

40.6 

(11.89)

45.2 

(13.26)

HP-17 14.0 

(0.88)

1,547.0 

(0.73)

403.0 

(0.19)

45.3 

(13.29)

51.3 

(15.04)

HP-18 2.5 

(0.16)

381.0 

(0.18)

0.0 

(0.0) 

13.5 

(3.96)

14.6 

(4.27)

ACU-1 3.8 

(0.24)

509.0 

(0.24)

0.0 

(0.0) 

14.8 

(4.34) none 

Total 320.0 

(20.2)

42,000.0 

(19.8)

5,870.0 

(2.77)

1,378.0 

(404)

1,308.0 

(383)
17 

2nd Floor

N 

¿

1st Floor

Conference 

Center

Figure 4-10 Location of heat pump thermal zones 

HP-2 

AC-1 HP-11

HP-3 

HP-4

HP-5

HP-6 HP-7

HP-14 

HP-13 

HP-12 

HP-11

HP-8

HP-9

HP-10

HP-15 HP-16

HP-17
18 

Figure 4-11 Ground heat exchanger 

4.5.2 Desiccant System 

The outdoor air handlers for the building include a solid, wheel desiccant dehumidification system (see 

Figure 4-12). The desiccant wheel is regenerated by heating air with a hot-water coil that uses boilerfired hot water. There is also a thermal wheel for energy exchange. 

Desiccant

Wheel

Thermal

Wheel Heating

Coil

Outside Air Supply Air

Outside

Regen Air

Regen Exhaust

Regen

Coil

Figure 4-12 Desiccant dehumidification system 

4.5.3 Water Heating 

There are two water heating systems in the Merrill Center: a solar thermal system for DHW and a 

propane boiler system for backup space heat, cistern freeze protection, and desiccant regeneration. 
19 

Domestic Hot Water 

The solar hot-water system consists of four arrays of 30 evacuated-tube solar collectors on the roof of the 

main building (see Figure 4-13). The collectors are inclined at an angle of 39° from the horizontal and 

face approximately south. A glycol loop circulates fluid from the roof to two hot water tanks that are 

plumbed in parallel. One tank is for potable and the other for nonpotable water. The tanks have electric 

backup heaters. The system provides DHW for sinks, showers, two dishwashing machines, and a clothes 

washing machine. Because the overall water use is very low, all the DHW needs of the building are met 

by the solar collectors; the backup heating coils have never come on. 

Figure 4-13 Evacuated-tube solar collector arrays 

Auxiliary Space Heating 

Figure 4-14 diagrams the boiler and pumping configuration; diagrams the terminal units and shows 

nominal heating capacities. This system is used in addition to space heating by the ground-source heat 

pumps. A propane-fired boiler heats water. The unit has a nominal thermal efficiency of 90%. Its rated 

input is 860 kBtu/h (252 kW). Hot water serves a combination of passive finned tube radiators and 

unitary heaters. The finned tube heaters are mostly located on the first floor along the south wall 

underneath the expansive windows. The enclosed walkway on the second floor, which connects the main 

building with the conference pavilion, also has finned tube radiators. The unitary heaters are used in 

vestibule entry areas, a mudroom, ground-floor storage and mechanical rooms, and restrooms in the 

conference pavilion. A heat exchanger arrangement allows the boiler to protect the rainwater cisterns 

against freezing. Freeze protection has not yet been necessary. The boiler-fired hot water is also 

configured to regenerate desiccant wheels in the outdoor air handler. 
20 

Hydronic Loop 

Pumps 

1 & 2

Boiler 

Unit #1

Fin Tube Radiators

Outdoor

Temp

Sensor

Fan Coils

Cistern Freeze Protection

Air

Handling 

Unit Desiccant

Regen

Air Preheat

Hydronic 

System Pumps 

3 & 4

Figure 4-14 Water heating boiler configuration 
21 

Figure 4-15 Water heating equipment schematic 
22 

4.6 Equipment and Water Systems 

Equipment includes all connected plug loads that are not associated with heating, cooling, ventilation, or 

lighting HVAC&L. Potable and nonpotable water systems are also included in the equipment loads. 

4.6.1 Potable Water System 

The potable water system provides water that is safe for drinking. The source of potable water is an onsite well with a residential-size pumping arrangement; no city water is available. The water is filtered and 

metered before being supplied to the kitchens and drinking fountains. Potable water is used quite a bit in 

the kitchens because lunch is served daily to encourage building occupants to stay on-site to reduce the 

environmental costs of transportation. All the lunch dishes are washed in the kitchen with potable water. 

4.6.2 Nonpotable Water System 

The nonpotable water system supplies water for fire protection, clothes and body washing, and landscape 

watering. Nonpotable water for washing is supplied to the restroom sinks, locker room showers, clothes 

washing machine, laundry tub, and hose bibs on the outside of the building. A residential-size pump 

provides pressure for this system. Using large cisterns with rainwater collection eliminated the need to 

install a large water main for fire protection. 

Several water conservation devices help minimize nonpotable water use. The restroom sinks are equipped 

with low-flow faucets and infrared control sensors for automatic operation. Composting toilets, which 

were purchased for all the restrooms, use minimal water to maintain appropriate levels of moisture for 

decomposition. The clothes washing machine is horizontal axis. 

4.6.3 Rainwater Collection Systems 

Three rainwater collection systems serve all the nonpotable water needs of the building. The largest 

system collects rainwater from the roof of the main building. The rainwater passes through a particulate 

and organic matter reduction filter before being stored in three, 6,500-gal (24.6-m3

) cisterns on the north 

side of the building, as shown in Figure 4-1 and Figure 4-3. After the storage tanks, the water is 

chlorinated and carbon filtered. A minimum water level is maintained for the fire protection system. If 

the water falls below the minimum required for fire protection, it is made up with well water. Under 

normal operating and weather conditions, there is an ample supply of rainwater. Excess rainwater flows 

off as stormwater through an overflow drain. A heat exchanger, which is controlled by the EMS and 

connected to the boiler hot-water system, protects the cistern water from freezing. This freeze protection 

system did not operate during the monitoring period. Two smaller systems collect rainwater from the 

downspouts of the main building and conference pavilion for landscape watering and for washing boots 

and equipment. 

4.6.4 Water Conservation and Resource Protection 

Potable and nonpotable water systems are part of the building’s water conservation measures. Water is 

designated at its point of use as either potable or nonpotable, and in combination with water conservation 

devices such as waterless composting toilets, provides a successful strategy for conserving water 

resources. Another important feature is the parking design that reduces harmful runoff from surfaces into 

Chesapeake Bay by placing parking under the building and using gravel surfacing for parking outside the 

building. Remaining stormwater runoff flows through a bioretention stormwater treatment system 

designed to treat oils, and then flows through a constructed wetland. 

 
23 

5 Whole-Building Energy Evaluation 

5.1 Whole-Building Evaluation Methodology 

NREL used detailed energy metering and compared collected data with the monthly metering of utilities 

to evaluate the Merrill Center. NREL has monitored the building continuously since August 2001. This 

section discusses measurement procedures, data processing, and results. Figure 5-1 details a timeline of 

the monitoring effort. 

9/01 10/01 11/0112/01 1/02 2/02 3/02 4/02 5/02 6/02 7/02 8/02 9/02 10/02 11/0212/02

8/1/2001 12/30/2002

11/1/2001

15 min. Interval

Started

6/5/2002 - 7/4/2002

1st Floor Transducer Error

3/7/2002

Boiler Run

 Relay Install

11/7/2001 - 11/6/2002

Modeling/Analysis Year

12/3/2002

Weather Station

Removed

Figure 5-1 Timeline of monitoring system and postoccupancy evaluation 

NREL used monitoring data for weather and operational schedules to develop a computer-simulated 

baseline model of a conventional energy code-compliant building. An as-built model of the building 

could not be developed for this project because of the complexity of the HVAC systems and the late 

addition of water source heat pumps in EnergyPlus. Developing an as-built model and comparing it to a 

baseline model would have been helpful, but we were only able to compare the actual measurements to a 

well-calibrated baseline model. The objective of the monitoring was to measure the performance of the 

building over the course of an entire year. For reference purposes, we used the actual measured weather 

data to compare this information with a base-case simulation of a typical code-compliant building. 

Performance metrics analyzed include site energy savings, source energy savings, and energy cost 

savings. The flow chart in Figure 5-2 shows how the measured data were used in the models and the 

process used to obtain the simulation results. 
24 

Measured Data Models Measured Results

Simulated Results Percent Savings

Site End Uses

Site Weather

Base-Case Model with 

Site Weather with AsBuilt Geometry

Measured Site Use

Measured Net 

Source Use

Measured Net 

Site Use

Base-Case Energy Costs 

Base-Case Site Use

Source Use Savings

Site Use Savings

Energy Costs Savings

Extract Equipment 

Loads and Schedules

PV Production Measured PV Production

Utility Bills Monthly Energy Costs

Base-Case Source Use

Base-Case Error 

Analysis Using Input 

Perturbation Methods

Figure 5-2 Whole-building evaluation flowchart 

5.2 Whole-Building Monitoring 

This section discusses the instrumentation and data collection techniques related to the data acquisition 

system that was installed in the Merrill Center. 

5.2.1 Measurement Procedure 

NREL focused on a measurement and verification plan that included a task schedule, a summary of the 

building, a list of objectives, a list of equipment needed, and measurement points. The system was 

designed to measure the important electrical and thermal flows in the buildings and local weather 

conditions. NREL specified and installed all the equipment, but it was purchased by CBF. The plan 

included specifying that electrical services be designed and wired so that key end uses, such as lighting 

and plug loads, are served by separate electrical panels. 

5.2.2 Instrumentation 

There are two separate components of the data acquisition system: one for the main building and a 

second for the conference pavilion. Figure 5-3 shows the individual points where electrical energy was 

monitored. Table 5-1 lists the points measured in the main building and Table 5-2 lists the points 

measured in the conference pavilion. The weather data were measured on top of the conference pavilion. 

The selection of sensors was based on successful application of such instruments on previous NREL 

building monitoring projects. NREL used watt-hour transducers to measure electrical energy. 
25 

PV System

Main Panel

HVG

LP2A

LPG-B

LPG

WM1

WM9

WM7

WM6

LV2A

PR1/RG1

LP1A

TGA/RP

WM12

WM13

WM14

WM15

Elevator WM2

Ground Loop Pumps WM3

Hot Water Pumps 3-4 WM4

Misc WM5

Site Lighting

Garage Lighting

Conference Building

Conference Lights 

and Plugs

Lights

WM10

T2-A

2nd Floor plugs

WM11

TG-B

LVG

RP1B

RP2

WM8

PR2/RG2

Heat Pumps 8-14

1st Floor Lights

1st Floor Plug Loads

Backup Water Heater WM16

Mech Room Loads

Figure 5-3 Electrical monitoring points 
26 

Table 5-1 Measurement Points and Sensors: Main Building 

Description Units Sensor Sensor 

Accuracy Notes 

Ground-Loop Water Supply 

Temperature °C Type-T 

thermocouple ±0.54°C 

Ground-Loop Water Return 

Temperature °C Type-T 

thermocouple ±0.54°C 

Heat Pumps 4 and 5 Return 

Air Temperature °C Type-T 

thermocouple ±0.54°C Installed 03-07-03 

Heat Pump 4 Mixed Air 

Temperature °C Type-T 

thermocouple ±0.54°C Installed 03-07-03 

Heat Pump 4 Supply Air 

Temperature °C Type-T thermocouple ±0.54°C Installed 03-07-03 

Heat Pump 5 Mixed Air 

Temperature °C Type-T thermocouple ±0.54°C Installed 03-07-03 

Heat Pump 5 Supply Air 

Temperature °C Type-T thermocouple ±0.54°C Installed 03-07-03 

AH1 Process Out to Building 

Air Temperature °C Type-T thermocouple ±0.54°C Installed 03-07-03 

Boiler Relay On Status On/Off Relay/Pulse count Installed 03-07-02 

1st-Floor Zone Air 

Temperature °C Vaisala Humitter 

50Y ±0.45°C Shielded 

1st-Floor Relative Humidity % Vaisala Humitter 

50Y ±6% ±3%: 10%–90% RH

±6%: 90%–100% RH

2nd-Floor Zone Air 

Temperature °C Vaisala Humitter 50Y ±0.45°C Shielded 

2nd-Floor Relative Humidity % Vaisala Humitter 

50Y ±6% ±3%: 10%–90% RH±6%: 90%–100% RH

Heat Pumps 8–14 kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

2nd-Floor Lights and 

Receptacles kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

1st-Floor Lights and 

Receptacles kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Heat Pumps 2–5, Pumps, 

Compressor kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Site and Garage Lights kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Mechanical Room Loads kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Conference Building 

(HP = this – lights + plugs) kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

1st-Floor Receptacles kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

2nd-Floor Receptacles Minus 

PV kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

PV Production kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Backup Water Heaters kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Elevator kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Ground-Loop Water Pumps kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Hot-Water Pumps 3–4 kWh WattNode P-series ±0.45% read. Add ±0.05% full scale 

Reference Temperature °C Campbell AM25T ± 0.2°C 
27 

Table 5-2 Measurement Points and Sensors: Conference Building 

Description Units Sensor Sensor 

Accuracy Notes 

Outside Air Dry-Bulb 

Temperature °C Type-T, wire ±0.54°C Shielded 

1st-Floor Zone Air 

Temperature °C Vaisala Humitter 50Y ±0.45°C Shielded 

1st-Floor Relative Humidity % Vaisala Humitter 50Y ±6% ±3%: 10%–90% RH

±6%: 90%–100% RH

2nd-Floor Zone Air 

Temperature °C Vaisala Humitter 50Y ±0.45°C Shielded 

2nd-Floor Relative Humidity % Vaisala Humitter 50Y ±6% ±3%: 10%–90% RH

±6%: 90%–100% RH

Horizontal Solar Radiation W/m2 Li-Cor LI200X ±5% ±3% typical 

Vertical (South-Facing) Solar 

Radiation W/m2

 Li-Cor LI200X ±5% ±3% typical 

Outside Air Thermister 

Temperature °C Vaisala Humitter 50Y ±0.5°C Shielded 

Outside Relative Humidity % Vaisala Humitter 50Y ±6% ±3%: 10%–90% RH

±6%: 90%–100% RH

Wind Speed m/s R.M. Young Wind Sentry 

03001 ±0.5 m/s Threshold 0.5 m/s 

Wind Direction degrees R.M. Young Wind Sentry 

03001 ±5° Threshold 0.8 m/s 

Conference Lights and Plugs kWh WattNode P-series ±0.45% 

reading 

Add ±0.05% of full 

scale 

Thermocouple Reference 

Temperature °C Campbell AM25T ±0.2°C 

We determined some electrical end uses by calculating the differences between other meters rather than 

metering them directly, which reduced the number and redundancy of sensors. For example, we 

determined the energy use of the heat pumps that serve the conference pavilion (HP-6, HP-7, HP-15, HP16, and HP-17) by subtracting the conference lights and plug readings from the aggregated readings for 

the entire conference pavilion. Other examples are first-floor lighting that was calculated from the 

difference between the first-floor plugs, and the combined first-floor plugs and lights. Three minor heat 

pumps that do not serve occupied areas were not metered. These included HP-1 and HP-18, which are 

combined with the miscellaneous mechanical room loads, and ACU-1, which is combined with the firstfloor receptacles. 

5.2.3 Data Acquisition 

The sensors are continuously monitored by two dataloggers. One datalogger measures sensors in the 

main building; the other collects data from the conference pavilion and weather station. The dataloggers 

take measurements every 20 seconds and report totaled or averaged results every 15 minutes. A 

telephone modem allows the 15-minute data to be retrieved automatically every day. The data are stored 

as text files. 
28 

5.2.4 Measured Data Processing 

This section discusses additional processing of the raw data from the data acquisition system. In many 

cases, the data sought are not measured directly, but are obtained indirectly by using direct measurements 

to perform calculations. This section also provides additional detail on how monitored data were handled 

and processed for daylight saving time (DST), filling missing data, generating inputs for modeling, and 

calculating uncertainty. 

5.2.5 Data Handling 

Figure 5-4 diagrams the steps involved in retrieving and processing data from the Merrill Center. Data 

are automatically retrieved from each datalogger daily. These raw data files are held on NREL servers 

with daily backup. Another program ensures that the data were received properly and without errors. If 

errors are discovered, the program sends an e-mail to NREL for attention. 

After the data are checked, a custom computer program called SortData3 reads the raw text files, cleans 

the data to remove duplicates, and outputs clean new text data for an integer number of days. These text 

files are then read by a custom program called HPBAnalyzer.pro4

, where results are analyzed and 

visualized. 

 

3

 SortData is a custom data analysis application written by Nicholas Long (NREL) in the Delphi programming 

language. 

4

 HPBAnalyzer is a custom data analysis application written by Brent Griffith (NREL) in an interpreted language 

called Interactive Data Language by Research Systems Incorporated, Boulder, CO. 
29 

Main Bld.

Datalogger

Conference

Pavilion

Datalogger

PC208W.exe

(automatically

collects data daily

over telephone

CBFMAIN1.DAT

Raw data file

appended and

stored on NREL

server

CBFCONF1.DAT

Raw data file

appended and

stored on NREL

server

SortData.exe

NREL application to sort and

organize data

PC208W.exe

(automatically

collects data daily

over telephone

modem)

SortData.exe

NREL application to sort and

organize data

Main.txt Main_vars.txt Conf.txt Conf_vars.txt

HPBAnalyzer.pro

NREL application for reading, combining, post-processing,

visualizing, and plotting data.

Summary

text data

Figure art

modem)

Figure 5-4 Data collection, storage, and processing 
30 

5.2.6 Daylight Saving Time 

This section describes how NREL handled DST in the data monitoring analysis. The measured data are 

in Eastern Standard Time for the entire year; however, real behavior of the occupants and building 

operation follows DST in the summer. EnergyPlus (see Section 5.3.1) uses weather and scheduling data 

in Standard Time, but it allows for DST modeling when the schedules are input in Standard Time by 

automatically adjusting Standard Time to DST. Therefore, measured data related to schedules for 

modeling were processed in Standard Time (as if there were no DST). 

To simplify scheduling data sets and computing average daily profiles, some data were rearranged (very 

slightly) to give the appearance of no DST. Data that were obtained when DST was in effect were shifted 

backward by 1 hour. The shifting of data involves corrupting 2 hours of data per year. For the spring 

DST shift (forward), an hour’s worth of data are truncated, and for the fall DST shift (backward), an 

hour’s worth of data are generated by replicating the values measured just before the new hour. This 

shifted data are referred to as DST data sets. 

The DST data sets were used to generate results for daily profiles and schedules for modeling input. The 

use of monitored data in simulations is discussed in Section 5.3.2, but developing schedules without 

concern for DST is much more convenient. EnergyPlus was run with its own corrections for DST, which 

correct schedules, so that during the modeling, the shifting described in this section is essentially undone. 

The DST data sets were not used for weather data because these were recorded in Standard Time. 

Weather does not adhere to the social convention, and EnergyPlus weather data are not in DST. 

5.2.7 Filling Missing Data 

The reality of experimental research is that measured data are often incomplete. The measurements at the 

Merrill Center have been reliable with no periods where data were lost or uncollected. Because the data 

set is very nearly complete, it is appropriate to correct the remaining problems. There are two main 

problem areas: (1) the electricity used for the first-floor receptacles and lights and (2) boiler operation. 

First-Floor Receptacles and Lights 

The transducer that measures the electrical loads for the first-floor receptacles (plugs) experienced 

intermittent malfunctions during two separate periods and resulted in lost data. The exact cause of the 

problem was not determined. The first period extended from about 8:00 p.m. on June 5, 2002 through 

about 9:00 a.m. on July 4, 2002. The second extended from about 11:00 a.m. on November 23, 2002 

through about 12:00 noon on February 21, 2003. The loss of these data also affects the first-floor light 

loads because the loads for lights and plugs are measured together and the lighting load is determined by 

subtracting the plug loads. 

To extrapolate values for plugs and lights, NREL analyzed periods of good data to develop typical daily 

profiles. Averaged curves were developed that describe the ratio of the plug loads to the combination of 

plug and light loads. Three separate curves were formulated for weekdays, Saturdays, and Sundays. 

Holidays were grouped with Sundays. Figure 5-5 shows the average curves used to correct the data. 
31 

0.5

0.6

0.7

0.8

0.9

1.0

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Time of Day

Fraction of Plug Loads 

Weekday

Saturday

Sunday/Holiday

Figure 5-5 Fraction of first-floor plugs to sum of plugs and lights 

Figure 5-6 shows the entire first-floor lighting data set as a time map with the original problem data on 

the left and the corrected data on the right. Similarly, Figure 5-7 shows the entire first-floor plug data set 

with the measured data on the left and the corrected data on the right. Appendix A provides an 

explanation of the figure art used in Figure 5-6 and Figure 5-7. The image-based data visualizations in 

the figures have time-of-day across the horizontal (from midnight to midnight) and day along the vertical 

(from about November 1, 2001 at the top to about April 29, 2003 at the bottom). The colors used in these 

figures represent actual values (in kilowatt-hours) as shown in the scales depicted as a colorbar. 
32 

Figure 5-6 Lighting load data for main building first floor: left 

map is the original data, right map is corrected5

 

 

5

 Figure is color, inaccurate if reproduced in black and white 
33 

Figure 5-7 Receptacle/plug load data for main building first floor: left map is 

original, right map is filled6 

Boiler Operation and Propane Consumption 

The transducer that measures boiler operation was installed on March 7, 2002. To fully characterize the 

energy used for this type of heating, NREL used data for the boiler operation and associated propane use 

for the period before transducer installation, which extended from November 7, 2001 to March 7, 2002. 

The boiler consumes gas at a fixed rate, so a transducer that measures burner start time (when the unit has 

cycled on) provides the total energy into the system. 

The two measured variables available are: (1) the hot-water circulation pump energy use in kilowatthours per 15-minute period and (2) the minutes of boiler operation per 15-minute period. The desired 

quantity is propane consumption with the same 15-minute data frequency as the other measurements. The 

boiler was assumed to be operating at its full rated capacity (determined from product literature) of 

860 kBtu/h (252 kW). Where boiler operation times are available, these running times are converted to 

energy use by directly multiplying by the appropriate rate. 

 

6

 Figure is shown in color, inaccurate if reproduced in black and white. 
34 

The boiler is used to supply hot water to fin-tube heaters located in perimeter zones and unit heaters in 

various locations as a supplement to and backup for the heat-pump-based air system. The boiler cycles on 

and off to meet a hot-water fluid set point that is controlled by the central EMS. The data were filled by 

analyzing the number and duration of boiler operation events. A boiler event is determined by initiating 

supplementary heating that may extend for varying amounts of time depending on the conditions. Events 

are characterized by examining electrical data for the circulation pumps, which are usually off. 

Circulation pumps that are on indicate a boiler event. The duration of the event is shown in the pump 

data, but these do not directly indicate the boiler cycling. The boiler will cycle during an event. 

Therefore, a model was developed to determine propane energy used as a function of the duration of a 

boiler event by assuming a direct relationship between the amount of cycling and the length of a time that 

heating is required. The model was developed by examining the trends shown in 27 boiler events that 

occurred after the boiler operation transducer was installed (from March 7, 2002 to November 6, 2002). 

A pump energy use in excess of 0.09 kWh in a 15-minute period was used as a threshold to determine 

when a boiler event was occurring. NREL used these data to generate a linear model of energy use as a 

function of the duration of boiler event. The model was then applied to fill propane energy use for the 67 

boiler events that occurred before the boiler operation transducer was installed. Figure 5-8 shows the 

source data and the line fitted to these data to create the model. 

Assuming a propane price of $1.20/gal ($0.32/L), the propane use for heating is about $845 for the model 

analysis year. Propane was also used for cooking, which was not measured, so the split between cooking 

and heating was not known. Actual propane expenditures were $827.08, but there was an unknown 

amount of carryover from the previous year. However, the estimate of $845 is consistent with propane 

expenditures and provides some confidence in the model for propane use. (Note that the propane use is 

minor compared to overall electricity; expenditures for propane are just 2.6% of those for electricity, but 

site energy use for only heating end use is split about evenly between propane and electricity. See 

Section 6.1.1 and Table 5-6.) 

0

250

500

750

1000

1250

1500

1750

2000

0 200 400 600 800 1000

Duration of boiler event (minutes)

Propane Energy Use, MJ/event

Source Data

Fill Model

Figure 5-8 Source data and model used to fill data on boiler propane use 
35 

5.2.8 Monitoring System Uncertainty 

The accuracy levels for measured data points are listed in Table 5-1 and Table 5-2. These uncertainty 

estimates are for the sensors and do not always represent how well the actual variable was measured. 

Most are derived from manufacturer specifications. Thermocouple-based temperature measurement 

accuracy is a function of the wire’s uniformity (±0.5°C, special limits of error), voltage measurement 

accuracy (±0.009°C), voltage measurement resolution (±0.001°C), and reference junction temperature 

measurement (±0.2°C). Voltage-to-temperature errors were modeled with a slope of 1.19°C/mV. 

Combining these uncertainties in quadrature leads to an estimate of measurement accuracy of ±0.54°C for 

the type-T thermocouple sensors. 

Uncertainty arises for monitoring results from transducer inaccuracies and the modeling used to 

determine boiler propane consumption. The propane consumption is determined indirectly from boiler 

run time measurement and modeling, and is therefore assigned a relatively high uncertainty of ±10% (by 

assumption). Individual electricity measurements are 0.5% based on the manufacturer’s data. Total 

values form a summation of as many as seven individual measurements, but the errors are assumed to be 

independent and to not increase the level of uncertainty. 

5.2.9 Monitoring Results 

This section presents results from the detailed monitoring. First, the electrical measurements are 

compared to utility bills. Results are then summarized for a one-year period and then broken down into 

monthly and daily periods. Finally, results for specific systems in the Merrill Center are discussed. 

Appendices contain a collection of figures showing results in more detail. Appendix A provides an 

illustration of the time map figure art used to depict a year’s worth of 15-minute data. Appendix B 

contains figures that show selected results from detailed monitoring. Appendix C contains figures that 

show the resulting weather data used in the weather file for EnergyPlus modeling. Appendix D contains 

figures that show the calibrated schedules. 

Utility Metering 

This section presents results from monthly utility metering of electricity. The CBF provided copies of the 

utility bills. The electricity costs for the analysis year are listed in Table 5-3. Figure 5-9 plots the data in 

Table 5-3. The billing data show that demand charges are comparable in size to energy charges, with 

47.5 % of the charges billed for energy, 47.3% for demand, and 5.2% for fixed charges and taxes. 
36 

Table 5-3 Electrical Energy Costs from Utility Billing 

Costs 

Billing Cycle Month Energy ($) Demand ($) Fixed, Taxes ($) 

November 2001 1,085.91 825.55 134.18 

December 2001 1,119.48 990.66 134.43 

January 2002 1,043.78 1,147.08 132.98 

February 2002 1,156.89 895.07 135.36 

March 2002 1,054.36 964.59 133.28 

April 2002 939.28 990.66 130.97 

May 2002 1,235.67 1,236.26 135.13 

June 2002 1,631.49 1,860.30 137.67 

July 2002 1,755.31 1,845.99 139.60 

August 2002 1,589.82 1,860.30 137.36 

September 2002 1,300.17 1,490.12 135.51 

October 2002 1,011.39 764.72 132.28 

Total 14,923.55 14,871.30 1,618.75 

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

NovemberDecember

January

February

March

April

May

June

July

August

September

October

Month

Utility Charge

energy charge demand charge fixed charges, taxes

Figure 5-9 Summary of utility electricity bills 
37 

The billing data also provide a method of checking that the monitored data are valid. Table 5-4 shows a 

comparison of the utility data to the measured data from the monitoring. The measured data were 

analyzed in bins that correspond to the billing cycles. Because the exact time the meter was read is not 

known, we assumed, for convenience, that the transition between billing periods occurred at midnight so 

that the billing period from November 7, 2001 to December 10, 2001 ends at the beginning of December 

10. 

Table 5-4 Comparison of Utility Bills to Measured Data 

Billing Period Billing Data Measured Data 

Month Days Start End Energy 

(kWh) 

Demand 

(kW) 

Energy 

(kWh) 

Demand 

(kW) 

November 2001 33 11/7/2001 12/10/2001 27,500 95 27,389 90.8 

December 2001 31 12/10/2001 1/10/2002 28,200 114 28,267 119.7 

January 2002 28 1/10/2002 2/7/2002 26,300 132 26,220 136.3 

February 2002 32 2/7/2002 3/11/2002 29,400 103 29,487 106.6 

March 2002 29 3/11/2002 4/9/2002 26,700 111 26,891 111.4 

April 2002 29 4/9/2002 5/8/2002 23,700 114 23,745 117.0 

May 2002 33 5/8/2002 6/10/2002 29,100 126 28,625 126.0 

June 2002 30 6/10/2002 7/10/2002 32,400 130 32,238 132.1 

July 2002 23 7/17/2002 8/9/2002 34,900 129 34,633 128.1 

August 2002 31 8/9/2002 9/9/2002 32,000 130 31,619 129.4 

September 2002 29 9/9/2002 10/8/2002 29,600 116 29,535 123.2 

October 2002 30 10/8/2002 11/7/2002 25,400 88 25,216 88.2 

Total 345,200 343,865 

Results shown in Table 5-4 provided assurances that monitored electricity data are robust. The total 

billed electrical energy was 345,200 kWh and the total from monitoring was 343,865 kWh. Although it is 

difficult to say which method of measuring electricity is more accurate, the fact that they agree to within 

0.4% (in aggregate for the year) indicates that the monitored data for electricity appear reliable. The 

differences in demand are somewhat larger and probably arise because of differing integration times; the 

utility’s demand metering is over 30-minute periods and the monitoring time period is over 15-minute 

periods. Typically, this would produce a smaller demand on the 30-minute interval compared with the 

15-minute interval. 

Results shown in Table 5-4 can also be analyzed to determine the electrical utility load factor for the 

Merrill Center. The load factor is defined as the ratio of the actual electrical energy used (in kWh) to 

electrical energy that would have been consumed were the peak demand rate used for the entire period. 

The electrical utility system benefits from load factors closer to unity. For the one-year monitoring 

period, the load factor was 0.30. The average load factor over the monthly periods was 0.35 with a 

maximum of 0.49 and a minimum of 0.29. 

Annual Monitoring Results 

Site energy use was determined from analysis of monitoring results from November 7, 2001 to November 

6, 2002. There are two forms of site energy use, net and total, where net gives credit for on-site 

production from solar electric PV panels. Uncertainty in the measured values for site energy use is 

estimated at just 0.6% because of the high accuracy in the electricity measurements and the low quantity 

of propane consumed. The purchased electricity was 1,173 MMBtu (1,238 GJ), the estimated propane 
38 

consumption was 64.2 MMBtu (67.7 GJ), and the electricity generated on site from PV was 9.1 MMBtu 

(9.6 GJ = 2,676 kWh). Therefore, the total site energy use was 1,247±7.5 MMBtu (1,315 GJ) for the 

period from November 7, 2001 to November 6, 2002. The total site EUI was 40.2±0.2 kBtu/ft2

 (457±2.7 

MJ/m2

). The net site energy use was 1,238 MMBtu (1,306 GJ) with intensity of 39.9±0.2 kBtu/ft2

(453±2.7 MJ/m2

). 

Source energy use is determined from net site energy use with assumed multipliers. Electricity 

conversion efficiency is assumed at 31%. Propane conversion is assumed at 93%. The level of these 

assumptions was determined from data in the Energy Information Agency’s Annual Energy Outlook (EIA 

2003). Although such conversions cannot be considered very accurate, their errors were not propagated 

when uncertainty was estimated. In the present report, source energy consumed for electricity is 

determined from net (purchased) electricity, which gives credit for the PV generated on site. The net 

source energy use for electricity was 3,785±15 MMBtu (3,993±16 GJ) and 69±6.9 MMBtu (72.8±7.3 GJ) 

for propane for a total of 3,854±22 MMBtu (4,066±23 GJ) for the period from November 7, 2001 to 

November 6, 2002. Therefore, the source energy intensity was 124±0.7 kBtu/ft2

 (1.41±0.01 GJ/m2). 

Energy costs are determined from utility electricity bills and analysis of propane use. The total electricity 

expenditures were $31,413.60. The results of analysis for propane use for heating was $875 ±$87 with an 

assumed price of $1.20/gal ($0.32/L). The result from the monitoring effort for the period from 

November 7, 2001 to November 6, 2002 is $32,288.60 ±$87. This translates into an energy cost intensity 

of $1.04/ft2

±$0.003/ft2 ($11.31/m2±$0.03/m2). 

The annual totals and breakdown for individual end uses are presented in Table 5-5, Table 5-6, and Table 

5-7, and graphically in Figure 5-10 and Figure 5-11. 

Monthly Monitoring Data 

Monthly totals for measured site energy are divided into major end-use categories that are listed in Table 

5-5 and shown graphically in Figure 5-10. HVAC energy includes propane. Energy use for HVAC 

includes equipment such as heat pumps and water pumps. The breakdown of the HVAC components is 

listed in Table 5-6 and shown graphically in Figure 5-11. Miscellaneous energy use is characterized in 

Table 5-7; it includes energy use for the elevator, the exterior and garage lighting, and the mechanical 

room and storage room receptacles. 

Plug loads are the largest energy end-use category, followed closely by HVAC. Lighting energy use does 

not show clear seasonal dependence, which suggests that daylight is not being harvested very well. 

Miscellaneous loads are significant and show seasonal dependence that indicates there could be heating 

loads associated with the elevator (oil heating) and the ground-floor receptacles. 

HVAC energy use does show seasonal dependence: more energy is used during the cooling season. Site 

energy use for heating is split evenly between propane and electric heat pumps. In the winter, the heat 

pumps on the second floor use much less electricity than those on the first floor, but the situation is 

reversed during the summer. The reversal is likely because the open area between the first and second 

floors allows warm air to move up to the second floor. 
39 

Table 5-5 Site Energy End-Use Characterization: Monthly 

Billing Period HVAC 

(kWh) 

Office 

Lights 

(kWh) 

Plugs 

(kWh) 

Misc. 

(kWh) 

Conf. 

Pavilion 

Lights &

Plugs 

(kWh) 

PV 

Energy 

Produced 

(kWh) 

Total 

Site 

Energy 

(kWh) 

November 2001 3,291 6,433 11,770 3,406 3,361 301 27,960

December 2001 10,791 5,927 10,246 3,622 3,011 230 33,367

January 2002 7,718 5,811 9,669 3,570 2,799 224 29,343

February 2002 9,842 7,739 10,623 3,328 3,005 334 34,203

March 2002 8,327 6,243 9,817 2,907 2,907 183 30,018

April 2002 3,012 5,958 9,899 2,331 2,745 190 23,755

May 2002 7,081 6,125 10,373 2,197 3,060 208 28,628

June 2002 13,136 5,416 9,202 1,776 2,896 189 32,237

July 2002 14,356 6,645 9,244 1,807 2,800 196 34,656

August 2002 12,483 5,429 9,303 1,767 2,850 211 31,621

September 2002 10,706 5,430 9,210 1,607 2,828 246 29,535

October 2002 6,452 6,066 9,978 2,426 2,592 164 27,350

Total 107,195 73,222 119,334 30,744 34,854 2,676 362,673

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

NovemberDecember

January

February

March

April

May

June

July

August

September

October

kWh/month 

HVAC Office Lighting

Office Receptacles Miscellaneous

Conf. Pav. Lights and Receptacles

Figure 5-10 Energy end-use breakdown by billing cycle 
40 

Table 5-6 HVAC Site Energy Use Characterization: Monthly 

Billing Period 

1stFloor 

Heat 

Pumps 

(kWh) 

2ndFloor 

Heat 

Pumps 

(kWh) 

Conf. 

Pavilion

Heat 

Pumps 

(kWh) 

Ground 

Loop 

Pumps 

(kWh) 

Boiler 

Propane 

(kWh) 

HotWater 

Pumps 

(kWh) 

Total 

HVAC 

Electricity 

(kWh) 

Total 

HVAC 

Site 

Energy 

(kWh) 

November 2001 1,163 567 461 514 573 13 2,719 3,291 

December 2001 2,845 902 1,318 511 5,100 115 5,691 10,791 

January 2002 2,174 487 1,412 460 3,124 61 4,594 7,718 

February 2002 3,235 471 664 667 4,717 88 5,125 9,842 

March 2002 3,531 471 571 575 3,128 50 5,199 8,327 

April 2002 1,144 632 658 566 11 2 3,001 3,012 

May 2002 1,700 3,164 1,440 777 0 0 7,081 7,081 

June 2002 3,375 5,697 2,320 1,745 0 0 13,136 13,136 

July 2002 4,129 6,822 2,537 844 24 0 14,332 14,356 

August 2002 3,591 5,637 2,231 1,024 0 0 12,483 12,483 

September 2002 2,993 4,978 1,752 982 0 0 10,706 10,706 

October 2002 2,829 429 486 536 2,134 38 4,318 6,452 

Total 32,709 30,256 15,851 9,201 18,810 367 88,385 107,195 

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

November

December

January

February

March

April

May

June

July

August

September

October

kWh/month 

1st-Floor Heat Pumps 2nd-Floor Heat Pumps Conf. Pav. Heat Pumps 

Ground Loop Pumps Boiler Propane HW Circ. Pumps

Figure 5-11 HVAC energy end-use breakdown by billing cycle 

 
41 

Table 5-7 Miscellaneous Site Energy Use Characterization: Monthly 

Billing Period Elevator 

(kWh) 

Exterior Lighting 

(kWh) 

Mechanical 

Room Loads 

(kWh) 

Totals 

(kWh) 

November 2001 421 1,564 1,421 3,406 

December 2001 424 1,210 1,988 3,622 

January 2002 405 1,171 1,994 3,570 

February 2002 444 1,105 1,779 3,328 

March 2002 407 1,033 1,467 2,907 

April 2002 375 947 1,010 2,331 

May 2002 367 991 839 2,197 

June 2002 316 809 651 1,776 

July 2002 271 880 655 1,807 

August 2002 252 783 732 1,767 

September 2002 267 707 633 1,607 

October 2002 340 931 1,155 2,426 

Total 4,289 12,132 14,324 30,746 

The better resolution of the monitored data (compared to the utility meter) allows the time that demand 

charges incur to be characterized and the end uses at the time of demand to be composed. Table 5-8 

summarizes the situation at peak demand for the period from November 7, 2001 to November 6, 2002. 
42 

Table 5-8 Electrical Demand Characterization 

Billing Period 

Peak 

Demand: 

Date and 

Time* 

HVAC 

(kW) 

Lights 

(kW) 

Plugs 

(kW) 

Elevator 

(kW) 

Exter. 

Lights 

& Mech. 

Room 

(kW) 

Conf. 

Pavilion 

Lights 

& Plugs 

(kW) 

PV 

Power 

(kW) 

Total 

(kW) 

November 2001 12/3/01 

9:00 a.m. 27.2 18.3 18.7 12.2 10.9 4.5 −0.9 90.8 

December 2001 1/1/02 

8:00 a.m. 79.6 15.7 14.0 0.5 5.1 4.7 −0.0 119.7 

January 2002 2/5/02 

9:00 a.m. 83.6 18.5 19.5 2.3 6.4 6.9 −0.8 136.3 

February 2002 3/4/02 

8:00 a.m. 61.2 19.3 13.1 2.3 6.0 4.8 −0.1 106.6 

March 2002 4/1/02 

8:00 a.m. 66.7 19.1 12.7 3.0 4.7 5.2 −0.0 111.4 

April 2002 4/18/02 

11:15 a.m. 66.5 17.8 22.5 2.2 2.7 6.4 −1.1 117.0 

May 2002 6/6/02 

1:15 p.m. 80.3 17.2 20.9 1.1 2.6 4.1 −0.9 125.9 

June 2002 6/24/02 

11:30 a.m. 85.7 17.3 19.1 2.2 2.5 5.8 −0.9 132.1 

July 2002 7/22/02 

1:45 p.m. 85.1 16.7 18.9 0.5 2.5 5.4 −1.1 128.1 

August 2002 8/15/02 

2:00 p.m. 85.7 17.1 20.3 0.6 3.1 3.3 −0.7 129.4 

September 2002 10/2/02 

11:15 a.m. 77.9 17.6 20.9 0.6 1.8 6.8 −2.3 123.2 

October 2002 10/29/02 

10:00 a.m. 34.8 20.4 23.4 0.5 4.2 5.1 −0.0 88.2 

* In Eastern Standard Time (add one hour for DST when appropriate) 

Figure 5-12 shows the situation at the time of peak demand for each billing cycle. HVAC energy use is 

consistently the largest contributor to demand charges. HVAC demand can be somewhat managed 

through controls, which suggests that demand charges might be reduced by changing thermostat set points 

over the day. Usually, the contributions from plugs and lights are uniform throughout the year. The 

elevator made a substantial contribution for the November 2001 cycle, but not for other billing cycles. 

The PV system reduced demand an average of 0.7 kW (the most power reduced was 2.3 kW). The 

number of kilowatts reduced by the PV system is far below the nominal capacity of 4.2 kW. For 3 of the 

12 months, the PV system did not reduce demand at all, which suggests that PV systems do not reduce 

demand charges as much as might be expected. 
43 

0

20

40

60

80

100

120

140

160

November

December

January

February

March

April

May

June

July

August

September

October

Peak Demand (kW) 

HVAC Lights

Plugs Elevator

Exterior Lights and Mech. Room Conf. Pavilion Lights and Plugs

PV Production

Figure 5-12 Coincident end uses at time of peak demand by billing cycle 

Daily Monitoring Data 

NREL reduced the monitoring data presented in this section to daily values and focused on demand 

issues. Figure 5-13 presents the peak electrical demand for every working weekday for the period from 

November 7, 2001 to November 6, 2002. The highest of these in a given billing period constitute the 

basis of demand charges. Figure 5-14 shows similar daily peak demand but for only Saturdays, Sundays, 

and holidays. Holidays are assumed to be the usual U.S. legal holidays. Figure 5-15 shows the resulting 

time of day that the peaks occurred for the weekdays and Figure 5-16 shows the time of day for peaks on 

Saturdays, Sundays, and holidays. 
44 

0

25

50

75

100

125

150

11/7/2001 1/6/2002 3/7/2002 5/6/2002 7/5/2002 9/3/2002 11/2/2002

Peak Demand [kW]

Figure 5-13 Peak demand: working weekdays 

0

25

50

75

100

125

150

11/7/2001 1/6/2002 3/7/2002 5/6/2002 7/5/2002 9/3/2002 11/2/2002

Peak Demand [kW]

Figure 5-14 Peak demand: Saturdays, Sundays, and holidays 
45 

0

4

8

12

16

20

24

11/7/2001 1/6/2002 3/7/2002 5/6/2002 7/5/2002 9/3/2002 11/2/2002

Hour of Day at Peak

Figure 5-15 Time of day at peak demand: working weekdays 

0

4

8

12

16

20

24

11/7/2001 1/6/2002 3/7/2002 5/6/2002 7/5/2002 9/3/2002 11/2/2002

Hour of Day at Peak

Figure 5-16 Time of day at peak demand: Saturdays, Sundays, and holidays 

The highest peaks occur during working weekdays. Much of the wintertime peak is caused by morning 

warmup, as seen with demands clustered around hour 8 (Figure 5-15). Summertime peaks are spread out 

across the occupied hours between late morning and late afternoon. On weekends and holidays, 

wintertime peaks can happen any time of the day; summertime peaks tend to occur in the afternoon. 

For the year analyzed, we also compared the time of day that the HVAC electrical load peaked to the time 

of day that the entire facility peaked. Results showed that these two peaks exactly coincided 48% of the 

days (55% of weekdays). The two peaks differed by two or more hours for 28% of the days (21% of 

weekdays). 

Average daily profiles characterize how energy is used throughout the day. Average profiles were 

generated by averaging all the data from working weekdays at the corresponding time of day. 

Figure 5-17 shows the average daily profile for working weekdays in January 2002. Figure 5-18 shows 

the average daily profile for working weekdays in August 2002. 
46 

0

10

20

30

40

50

60

70

80

90

0:00 4:00 8:00 12:00 16:00 20:00

Time of Day

Electricity Demand [kW] 

HVAC

Receptacles

Lights

Figure 5-17 January 2002 average daily profile 

0

10

20

30

40

50

60

70

80

90

0:00 4:00 8:00 12:00 16:00 20:00

Time of Day

Electricity Demand [kW] 

HVAC

Receptacles

Lights

Figure 5-18 August 2002 average daily profile 
47 

Figure 5-17 and Figure 5-18 reveal energy demand characteristics and opportunities for energy savings at 

the Merrill Center. After an initial morning spike caused by the cleaning crew, lights come on at the same 

time the HVAC increases. Figure 5-17 and Figure 5-18 show that lights are on at a relatively constant 

level. Although daylighting might be used, it does not change the lighting power during the day to match 

the available solar resource (see Section 6.3). The results show that off-hour receptacle loads are about 

half of what they are during the day, which indicates that equipment is being left on when not in use. 

Presumably, this is because a large amount of information technology equipment operates in the offices 

and server room. The off-hour plug loads in the Merrill Center are probably typical of contemporary 

offices, but they could be considered an opportunity for improving the energy performance of the 

building. There are methods that might be used to reduce off-hour plug loads, including communicating 

with employees about the value of shutting down equipment, deploying new network-based software 

products that monitor and reduce computer power, and replacing equipment with energy-saving sleep 

modes. 

5.3 Development of Building Energy Models 

Building energy modeling is an important complement to detailed measurements when assessing the 

performance of a specific building. As with most commercial buildings, this building is unique. There is 

no actual side-by-side comparison, and as a result, the building must be compared with a simulation of a 

baseline building. Several parameters, including operating schedules, plug and miscellaneous loads, and 

weather, are the same between the buildings. 

This section presents baseline energy models used to provide a reference performance level to better 

understand the performance of the Merrill Center. The goal is to determine how the building in question 

would perform if it were built differently, which usually means a building that meets minimum energy 

code, but otherwise meets the same programmatic requirements of the building being evaluated. 

Although it would be fairer to use the energy code in place at the time the Merrill Center was designed 

and built issued by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers 

(ASHRAE) e.g., ASHRAE 90.1-1989, this report is based on the current version of the consensus 

Standard 90.1-2001 for nonresidential buildings (ASHRAE 2001b). This code was selected to be 

consistent with other baseline activities of NREL’s building research program. 

ASHRAE 90.1 was developed to provide a threshold for energy performance as opposed to a tool for 

assessing the energy performance of a building. To assist in using the code for comparative purposes, 

Appendix G of ASHRAE 90.1 was developed as a method of applying the Energy Cost Budget to rate 

buildings (ASHRAE 2002, ASHRAE 2003a). Appendix G will be included in the 2004 version of 

ASHRAE 90.1 (and currently published as Addendum e to ASHRAE 90.1-2001). NREL applied useful 

definitions from Appendix G in the analysis of the energy performance of the Merrill Center. 

The intended application for Appendix G is to compare results of two models: one for a proposed 

building and another for a comparable baseline building. The methodology that is evolving for 

comparing two models often deals with modeling difficulties by declaring that difficult-to-determine input 

be the same in both models. However, this study compares baseline modeling to measurements of a real 

building, so ascertaining the variability in the predictions of baseline models of energy performance is 

natural. Therefore, this study includes considerable additional analysis of baseline modeling to estimate 

the uncertainty in model predictions (see Appendix F). 

Section 5.3.2 summarizes the input data used to generate the baseline model results. Results of the 

baseline modeling are presented in Section 5.3.3. Many more details on the geometry and error analysis 

for baseline modeling are provided in Appendix F. 
48 

5.3.1 EnergyPlus Building Simulation Tool 

NREL selected EnergyPlus (Crawley et al. 2001) to model baseline energy performance. EnergyPlus is a 

relatively new building energy simulation program that was first released in April 2001. The program 

builds on the capabilities and features of BLAST and DOE-2. It collects many capabilities into a single 

program that models building performance. Some key capabilities include variable time steps, 

configurable modular HVAC systems integrated with heat balance-based zone simulation, multiple 

comfort models, daylighting and advanced fenestration, multizone airflow, displacement ventilation, 

flexible system modeling, and PV and solar thermal simulation. Since EnergyPlus was released in April 

2001, more than 24,000 copies have been downloaded. EnergyPlus can accept 15-minute weather files 

and complex schedules (with 8,760 values) for internal gains. This ability is very important for a 

measurement-to-simulation comparison, as weather and internal gains must be calibrated as accurately as 

possible. 

5.3.2 Modeling Inputs 

NREL processed some monitored data to provide inputs for modeling exercises. Appendix C presents 

plots of the weather data and Appendix D presents plots of schedules for internal gains that were 

developed from on-site measurements. The rest of this section documents details involved in developing 

modeling inputs from monitoring data. 

This section describes the envelope materials, floor areas, internal gains, HVAC, and energy pricing used 

in all the baseline models. Wherever possible, this study attempted to adhere to the forthcoming 

Appendix G of ASHRAE 90.1 by substituting proposed design with actual monitoring results from the 

Merrill Center, as appropriate. 

Modeling Analysis Year 

NREL selected a year’s worth of data for annual energy analysis. More were available from monitoring, 

but the energy simulations are typically for a single year. Therefore, we selected a subset of the complete 

measured data set to constitute a modeling analysis year. This year was selected based on the criteria that 

the measured data are as complete as possible and that times coincide with the electric utility’s billing 

cycles. The period selected was November 7, 2001 to November 6, 2002. 

The measured data were rearranged so that data from November 7, 2001 to December 31, 2001 follow 

data from November 6, 2002 (see Figure 5-19). This was done to have a calendar year for the data. All 

data used to generate modeling inputs (schedules for internal gains and weather) were rearranged in this 

manner. This rearrangement distinguishes the modeling analysis year from the actual monitoring period. 
49 

12/01 1/02 2/02 3/02 4/02 5/02 6/02 7/02 8/02 9/02 10/02 11/02

11/7/2001 11/6/2002

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

1/1 12/31

11/7/01 - 12/31/01

Modeling/Analysis Year

Actual Monitoring Period

11/7/01 - 12/31/01

Figure 5-19 Data rearrangement for modeling analysis year 

Weather Data 

Building energy models use weather data. Because measured data were obtained on-site for weatherrelated parameters, it was desirable to use these data for the energy models. Routines were developed to 

create annual weather files for use with EnergyPlus. 

Some measured data were fixed to avoid problems. Relative humidity sensors are notoriously poor at 

high humidity. Readings in excess of 100% were screened and set to 100% to avoid problems in 

psychrometric calculations. Radiation measurements less than 0.1 W/m2

 were set to 0.0. 

Dew-point temperatures were calculated with a direct relation from Chapter 6 of the ASHRAE Handbook 

of Fundamentals (ASHRAE 2001a), Equations 37 and 38. 

The on-site pyranometer measures global horizontal solar radiation. The Perez All-Weather Sky Model 

(Perez 1992) was used to calculate the direct component of this radiation. NREL adapted computer 

routines provided by Perez for use in the data analysis application HPBAnalyzer. In addition, solar 

position routines were adapted from EnergyPlus (Crawley et al. 2001) source code and from the TARP 

Manual (Walton 1983). 

Cloud cover is used to compute sky temperatures or horizontal infrared radiation intensity. EnergyPlus 

uses horizontal infrared radiation intensity to compute outside surface temperatures of surfaces exposed to 

the outside. Cloud cover was not observed directly and so was inferred from solar radiation 

measurements with a method developed by Auer (see Appendix E). 

The processed weather data were then written to a text file with the format for EnergyPlus weather files. 

Because EnergyPlus allows subhourly weather data, the weather file was formulated with 15-minute time 

steps to preserve the resolution of the measurements. The data included in the weather file are listed in 

Table 5-9. The estimated level of uncertainty in the weather data are also listed in Table 5-9 along with 

notes on the sources of error that contribute to the uncertainty. 
50 

Table 5-9 Weather Data for Energy Simulations Derived from Measurements 

Parameter Units Source Uncertainty Source of Error 

Month Integer Datalogger ±0 

Day Integer Datalogger ±0 

Hour Integer Datalogger ±0 

Minute Integer Datalogger ±1 Possible mismatch 

Dry-Bulb 

Temperature °C Directly measured ±0.5°C Transducer error 

Dew-Point 

Temperature °C ASHRAE 

equations ±1.5°C Estimated error 

propagation 

Relative Humidity % Directly 

measured ±6% Transducer error 

Atmospheric 

Pressure Pa 

Not 

measured, 

sea level 

±200 Pa Estimated error 

Horizontal IR W/m2

TRNSYS 

Type 69 

model 

±20% Model; latitude 

concerns 

Global horizontal 

Radiation W/m2 Directly 

measured ±3% Transducer error 

Direct Normal 

Radiation W/m2

 Perez model ±65 W/m2 Model validation 

RMS (Perez 1992) 

Diffuse Horizontal 

Radiation W/m2

 Perez model ±65 W/m2 Complement of 

direct radiation 

Wind Direction degrees Directly 

measured ±5° Transducer error 

Wind Speed m/s Directly 

measured ±0.5 m/s Transducer error 

Total Sky Cover tenths 

TRNSYS 

Type 69 

model 

±1.0 tenth Estimated error 

propagation 

Opaque Sky Cover tenths 

TRNSYS 

Type 69 

model 

±1.0 tenth Estimated error 

propagation 

NREL created two additional versions of the weather file to understand the errors associated with 

inaccurate weather data in EnergyPlus modeling results (see Section F.5). One file (CBF_minus.epw) 

was created by altering all the variables down (to a lower energy state) and reducing all the parameters by 

their respective uncertainty. Similarly, a second file (CBF_plus.epw) was created by shifting all the 

variables up. The solar radiation data were not shifted at night and relative humidity was not shifted 

outside physical limits. 

Schedules 

In this study, NREL developed two types of schedules from the monitoring data. The first, referred to as 

a smoothed schedule, is a simplified schedule that is meant to represent average conditions, which is usual 

practice for building energy modeling. The second is for detailed calibration of internal gains from 

receptacles and process loads. 

The smoothed schedules are used in all simulations to describe a variety of time-dependent input 

parameters for an energy model. Building operation parameters were determined by observing measured 

data. These include settings such as thermostat set points, setup and setback schedules, equipment 
51 

availability, lighting design levels and schedules, and occupancy schedules. Thermostat set points were 

developed from studying the measured zone air temperatures. Lighting schedules were determined from 

analyzing lighting patterns. A cleaning crew, which arrives at 6:00 a.m., is included in the lighting 

schedule. Some lights are turned off or down after cleaning and turned on again at 8:30 a.m. The 

cleaning crew switched from evening schedule to morning schedule during the analysis year, apparently 

at the beginning of January 2002. 

Detailed calibration schedules that have 8,760 values were generated. Bronson et al. (1992) developed 

methods of calibrating nonweather-dependent loads that produce more compact schedules. However, their 

method was more complicated to implement than the method of using an entire year of measured data. 

The approach taken here was to develop calibration schedules that directly followed from monitoring 

data. The 15-minute measured data were totaled or averaged to produce hourly data. Routines were 

implemented in HPBAnalyzer, which writes EnergyPlus input objects for the schedules. 

Table 5-10 lists the calibration schedules that were developed from monitored data. The data are plotted 

in Appendix D. Baseline modeling requires schedules for certain uses of electricity. One type of baseline 

building geometry combines the separate conference pavilion with the main office building. Special 

blended schedules were developed for the first and second floors of such baseline buildings. One blended 

schedule includes the measured first-floor receptacles and 18.4% of the conference pavilion plugs and 

lights. A second blended schedule includes the measured second-floor receptacles and 81.6% of the 

conference pavilion plugs and lights. The 18.4%/81.6% split was selected based on floor area 

distributions. 

Table 5-10 Calibration Schedules for Modeling from Measured Data 

Calibration Schedule Notes 

First-floor plugs Main building receptacle loads 

First-floor lights Main building lighting loads 

Second-floor plugs Main building receptacle loads 

Second-floor lights Main building lighting loads 

Conference plugs and lights Conference pavilion receptacle and lighting loads 

Miscellaneous ancillary loads Elevator, mechanical room, storage, exterior lights 

Blended first-floor plugs For baseline models, combines main and conference 

Blended second-floor plugs For baseline models, combines main and conference 

Internal Gains 

NREL calibrated receptacle and miscellaneous loads so that the baseline models would have the same 

level and timing of internal gains as the real building did for the model analysis year. The calibration 

process required two steps: 

1. Determine schedule (as described in Table 5-10) of factors (0.0–1.0) that are applied to the design 

levels. 

2. Iteratively determine design levels that, when used with the schedules, result in annual energy 

consumption that matches monitoring results. 

Different schedules were used depending on the geometry of the baseline model and how conference 

pavilion loads and floor areas were handled. The details of the schedules are shown in Appendix D and 

are documented in the EnergyPlus input files. Because the real building includes daylight harvesting, 

lighting schedules for baseline models used schedules typical for day shift operation. 
52 

Envelope 

ASHRAE 90.1-2001 has different requirements based on climatic differences. The envelope 

requirements are listed in Table B-13 in ASHRAE Standard 90.1-2001. Baltimore was selected as the 

climate area closest to Annapolis. Table 5-11 lists the building envelope minimum performance 

specifications and shows resulting performance levels computed by the EnergyPlus model because these 

are calculated from complete constructions (and cannot be modeled explicitly). The EnergyPlus results 

for assembly R- and U-factors do not include the added resistance of surface film coefficients. 

Table 5-11 Envelope Performance Specifications for Baseline Models 

Envelope Component 90.1 Minimum 

Requirements 

EnergyPlus 

As-Modeled 

Exterior Walls ft2·h·°F/Btu (m2·K/W) R = 13 (2.3) R = 15.5 (2.73)

Built-Up Roof ft2·h·°F/Btu (m2·K/W) R = 15 (2.6) R = 15.3 (2.70)

Windows Btu/ft2·h·°F (W/m2·K) U = 0.6 (3.24)

SHGC = 0.39 

U = 0.6 (3.14)

SHGC = 0.397 

Floor Area 

The total floor area of the Merrill Center is taken as 31,000 ft2

 (2,880 m2). Of this, 5,000 ft2 (466 m2) are 

mechanical and storage rooms on the ground floor; the rest of the building is elevated. These groundfloor spaces are not occupied and are not completely conditioned. To simplify the modeling, these spaces 

were not included in models. Instead, a smaller floor area of 26,000 ft2

 (2,414 m2) was modeled and the 

energy associated with the mechanical and storage rooms is taken as a given and counted as a 

miscellaneous load. However, the entire 31,000-ft2

 (2,880-m2)floor area was used to calculate EUIs 

(where overall energy performance values are normalized by the floor area of the building). The 

miscellaneous ground-floor loads also include the elevator and exterior lights and are input into the 

models with an 8760-value hourly schedule to include their contribution to demand charges. 

HVAC Systems 

For the climate and size of the Merrill Center, informational Appendix G specifies that the baseline 

HVAC system should be based on single-zone rooftop packaged unit with gas-fired heating and no 

economizer. The air-to-air package unit was modeled in EnergyPlus as diagrammed in Figure 5-20. 

There is one package for each thermal zone. The air-to-air cooling was governed by performance curves 

fit to catalog data for a nominal 4-ton unit; however, the capacity of the units was autosized by 

EnergyPlus. The cooling coefficient of performance is 3.1, which includes compressor and condenser 

fan. The heating efficiency is 0.8. The fan static pressure is assumed to be 2 in. (500 Pa) of water. Each 

unit is configured to automatically size using design day calculations that precede the annual simulations. 

Outside air ventilation rates for the baseline are determined during autosizing based on a requirement 20 

cfm (10 L/s) of air per person. Infiltration was set at 0.4 ACH and scheduled for off hours when the 

HVAC system is not operating. 
Figure 5-20 System diagram of EnergyPlus model for rooftop packaged unit 

Energy Prices 

The baseline building was assumed to use propane rather than natural gas. Although 90.1 Appendix G 

specifies natural gas heat, only electricity and propane are available at this building site. Therefore, the 

modeling proceeds assuming the building is using natural gas-fired coils for heating, but with pricing 

typical of propane. The price for propane was assumed to be $1.20/gal ($0.32/L) based on local utility 

bills. The heating value of propane is 0.91 therm/gal (25.4 GJ/m3

) or $1.317/therm ($12.48/GJ). This 

rate was used to calculate heating energy costs and is considerably higher than natural gas at roughly 

$0.75/therm ($7.11/GJ) (ASHRAE 2001a). 

Table 5-12 shows the utility pricing structure used to compute electricity costs. Post-processing routines 

were developed that compute energy costs from hourly results for energy use. The price per kilowatthour includes distribution and tax surcharges of $0.01932/kWh. Demand metering in the calculations is 

for the highest rate of use over a 15-minute period. Saturdays, Sundays, and national holidays are 

considered off-peak by the utility, but the routines only account for weekends and not the holidays. 
54 

Table 5-12 Electricity Pricing Structure 

Season Charge Category On-Peak 

10:00 a.m. – 

8:00 p.m. 

Intermediate 

7:00 a.m. – 10:00 a.m. 

8:00 p.m. – 11:00 p.m. 

Off-Peak 

11:00 p.m. – 

7:00 a.m. 

Summer 

(Jun–Sep) 

Energy Charge 

(¢/kWh) 6.194 5.003 3.552 

Demand Charge 

($/kW) 14.310 14.310 14.310 

Winter 

(Oct–May) 

Energy Charge 

(¢/kWh) 4.515 4.172 3.578 

Demand Charge 

($/kW) 8.690 8.690 8.690 

5.3.3 Summary of Baseline Results 

A thorough evaluation of baseline energy performance was performed with EnergyPlus. This study 

looked at a variety of baseline building descriptions to determine a best estimate for performance along 

with an estimate of the uncertainty in the results. The modeling used 15-minute weather data based on 

one year of actual on-site measurements. Receptacle and miscellaneous electrical loads were calibrated 

with hourly data based on actual measurements. Figure 5-21 shows the breakdown by end uses for site 

energy consumption. Figure 5-22 shows the breakdown by end use for source energy consumption. 

Figure 5-23 shows the breakdown by end use for energy costs. Demand costs include contributions from 

all end uses. 

Lights

27%

Receptacles

31%

Misc

6%

Fans

11%

Cooling

14%

Heating

11%

Figure 5-21 Mean baseline result for site energy consumption by end use 

Total = 1,650 MMBtu/yr 

 (1,741 GJ/yr) 
55 

Lights

29%

Receptacles

33%

Misc

7%

Fans

12%

Cooling

15%

Heating

4%

Figure 5-22 Mean baseline result for source energy consumption by end use 

Lights

16%

Receptacles

18%

Misc

4%

Fans

Heating 6%

6%

Demand

41%

Cooling

9%

Figure 5-23 Mean baseline result for energy cost by end use 

Table 5-13 summarizes the overall energy intensity performance of the baseline model and estimated 

uncertainty. Input perturbation methods (Lomas and Eppel 1992) were used to develop uncertainty 

estimates and are described in detail in Appendix F. 

Total = 4,945 MMBtu/yr 

 (5,217 GJ/yr)

Total = $37,019/yr 
56 

Table 5-13 Summary of Results for Overall Energy Intensity - Baseline Building 

Site EUI 

kBtu/ft2·yr 

(MJ/m2·yr)

Source EUI 

kBtu/ft2·yr 

(MJ/m2·yr)

Energy Cost 

Intensity 

$/ft2·yr 

($/m2·yr)

Baseline Model 53.3 ±7.3 

(604.8 ±82.8) 

159.6 ±20.0 

(1,812 ±227) 

1.20 ±0.17 

(12.87 ±1.8) 

Uncertainty ±14% ±13% ±14% 

5.4 Whole-Building Energy Performance Results 

5.4.1 Site versus Source Energy and Energy Cost Savings 

This section compares the results of measurements presented in Section 5.2 to the results of baseline 

building models presented in Section 5.3. When discussing low-energy buildings, it has become common 

to characterize performance with the term percent savings. Baseline modeling provides data to use as a 

reference when calculating the level of savings as a percentage. This section presents percent savings 

calculated from various performance metrics (such as site energy use, source energy use, and energy cost) 

that were calculated with 

100 ( ) percent savings ∗ − =

base

base meas

x

x x

 (1) 

Where 

base x = a performance metric from the baseline modeling and 

meas x = a performance metric from monitoring results. 

Table 5-14 summarizes the results and shows percent energy savings. The mean values represent a best 

estimate of the results. Error propagation assumed independent random errors and added fractional 

uncertainties in quadrature (see Appendix F). The estimated uncertainty levels represent a confidence 

interval of 98%. 

Table 5-14 Overall Energy Savings Levels 

Total Site EUI 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Site EUI 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Source EUI 

kBtu/ft2·yr 

(MJ/m2·yr)

Energy Cost 

Intensity 

$/ft2·yr 

($/m2·yr)

Baseline Model 53.3 ±7.3 

(604.8 ±82.8) 

53.3 ±7.3 

(604.8 ±82.8) 

159.6 ±20.0 

(1,812 ±227) 

1.20 ±0.17 

(12.87 ±1.8) 

Monitoring 40.2 ±0.3 

(456.8 ±2.9) 

39.9 ±0.3 

(453.2 ±2.9) 

124.3 ±0.7 

(1,412 ±8) 

1.05 ±0.003 

(11.31 ±0.03) 

Savings (24.5 ±14.1)% (25.0 ±14.1)% (22.1 ±12.8)% (12.1 ±14.1)% 
57 

On-site energy production from the PV system makes it important to distinguish between net and total 

energy use. Net energy use is the difference between the total energy use and the energy produced by PV. 

The small difference between net and total site energy use savings occurs because there is only a small 

amount of PV production. Cost saving levels are not as high as energy saving levels because the Merrill 

Center is predominantly electric and incurs substantial demand charges; propane is a secondary heat 

source. The baseline model uses propane-fired heating. 

Table 5-14 also includes results for uncertainty estimates for both the baseline energy model and 

monitoring. The accuracy of whole-building energy performance results for the baseline model results is 

about 13%; the accuracy of the monitoring results is better than 1%. When developing values for percent 

savings, the contribution of uncertainty in the baseline modeling is significant. 

Another method of characterizing energy savings levels is to consider only the energy used for HVAC&L 

by removing that energy used by the activities of the occupants inside the building. This method of 

comparing energy use is used in the Energy Cost Budget of ASHRAE Standard 90.1-2001. The USGBC 

has also used this method in its LEED rating system. However, some researchers and professionals in the 

building industry disapprove of this method because it tends to overstate the level of savings. Others 

approve because it focuses on the energy impacts directly attributable to building design. Results from 

the current study were reanalyzed to provide such data and provide context to such discussions and an 

assessment of earlier predictions. A significant difficulty arises in trying to remove plug loads from the 

energy costs because of their contribution to demand charges. Therefore, energy costs without plug loads 

were modeled by subtracting the contribution from plug loads from the overall costs using a virtual 

electricity rate of $0.091/kWh that was determined from a year of billing data. This represents a cost 

difference of $14,014/yr for the baseline model and $14,030/yr for the monitored results. Table 5-15 

shows the results of this analysis. 

Table 5-15 HVAC&L Energy Savings Levels 

Net Site EUI 

without Plug Loads 

kBtu/ft2·yr 

(MJ/m2·yr)

Energy Cost Intensity 

without Plug Loads 

$/ft2·yr 

($/m2·yr)

Baseline Model 36.3 

(412.2)

0.74 

(7.99) 

Monitoring 23.0 

(260.6)

0.59 

(6.34) 

Savings 36.8% 20.6% 

5.4.2 Measured Versus Predictions 

The design team predicted the energy performance of the Merrill Center to prepare application materials 

for LEED 1.0 documentation. The design team used a combination of simulations (TRACE 600) and offline analyses for natural ventilation and active solar systems to predicted performance values before the 

building was constructed. Table 5-16 summarizes the predicted results. This represents the original 

energy goals for the building. 

The building was monitored as documented in this report. Monitoring provides an opportunity to assess 

the accuracy of predictions made before the building was built. Table 5-16 also compares predicted to 

monitored energy performance. The comparison here does not take into account the differences between 

the actual weather and the typical weather year used in the modeling. (The actual weather was generally 
58 

warmer and sunnier than the weather file for Baltimore. See Table C-1.) Nor does it taken into account 

changes in the building’s architectural program that may have increased plug loads (such as adding 

computer servers). 

Table 5-16 Predicted versus Monitored EUIs 

Category Predicted 

kBtu/ft2·yr 

(MJ/m2·yr)

Monitored 

kBtu/ft2·yr 

(MJ/m2·yr)

Heating EUI 0.7 

(8.4)

3.9 

(46) 

Cooling EUI 7.2 

(82)

7.8 

(89)

Lighting EUI 6.8 

(77)

10.0 

(113)

Plug load EUI 8.8 

(100)

15.0 

(171)

Miscellaneous 0.0 

(0.0)

3.4 

(38)

PV Power Production 0.5 

(5.4)

0.3 

(3.3)

Net Site EUI 23.0 

(262)

39.9 

(453)

The measured data indicate that the whole-building energy performance realized was lower than 

predicted. Heating energy use was predicted to be very low, but the measurements showed about five 

times more energy use for heating than was predicted. Lighting energy use turned out to be 47% higher 

than predicted. Plug and miscellaneous loads were twice those assumed for the predictions. Predictions 

for cooling were within 10%, which is very good considering the additional lighting and plug loads. PV 

power production was 40% lower than predicted. Overall, the net site EUI was 73% higher than 

predicted. 

These results indicate that the performance predictions made during design development were optimistic. 

The largest deviation in terms of magnitude was from underestimating the amount of electricity drawn by 

plug loads in the occupied spaces and miscellaneous loads such as exterior lighting, mechanical room 

accessories, and the elevator, which were not accounted for in the original prediction. Efforts to improve 

the accuracy of predictions of whole-building energy performance during the design phase should focus 

on better methods of developing assumptions for receptacle and miscellaneous electrical loads. 
59 

6 Subsystem Evaluations 

Although NREL’s monitoring effort focused on an overall assessment of the energy performance of the 

Merrill Center, the data did allow more detailed evaluation of some specific systems. The Merrill Center 

has at least four systems that warrant further analysis. This section presents monitoring results related to 

the ground-source heat pumps, natural ventilation, PV power generation, and daylighting. 

6.1 HVAC Performance Evaluation 

Figure 6-1 compares the HVAC energy use with outdoor air temperatures for the analysis year. The 

results show that the building is cooling dominated. The relatively low energy use in the middle suggests 

that natural ventilation helps to reduce HVAC energy use. 

Figure 6-1 Daily HVAC electricity consumption versus average outdoor 

air temperature 

The HVAC systems installed in the Merrill Center include desiccant wheel dehumidification systems for 

both the main building and the conference pavilion. The systems remain installed, but they are used only 

to provide outside air via the integral supply fans. The desiccant wheels are not operated because the 

building operator has determined that (1) the heat pumps provide effective dehumidification during 

cooling and (2) it is not desirable to continuously run the boiler solely to regenerate the desiccant. 

Because the desiccant systems are not being used, they complicate the air system; therefore, they should 

not have been specified. The potential benefit of using the desiccant systems should be evaluated with an 

integrated analysis. If the wheels are never to be used, they should be removed because they only 

contribute to increased static pressure and fan energy. One suggestion is that the wheels could be used for 

energy recovery but not regenerated for dehumidification. Care must be used to control heat recovery 

such that the cost of operating the systems is less than the recovered heat (Pless and Torcellini 2004). 
60 

The Merrill Center’s HVAC systems do not include an economizer, which is a noticeable shortcoming for 

a low-energy building; it is probably related to the inclusion of natural ventilation, space conflicts, and 

overall airflow rates between the desiccant wheel systems and what would be needed for an economizer. 

Distributed heat pump packages also lead to an outside air system with smaller ducts that feed the 

individual air units with fresh air. 

Our results indicate that HVAC heat pumps are large contributors to demand charges because they often 

all come on at once. Therefore, energy costs could be reduced by reconfiguring the EMS to reduce the 

number of heat pumps that can run at the same time. We suggest that more time be allowed for warmup 

and that controls be organized so that half the pumps run for 15 minutes and then the other half for 15 

minutes. This change would not necessarily save energy, but it could reduce the peak electricity 

demands, which would reduce costs. 

6.1.1 Ground-Source Heat Pumps 

The Merrill Center’s main source of cooling and heating consists of small packaged water-to-air heat 

pumps that are connected in parallel to a vertical ground heat exchanger via a closed-loop. Heat pumps 

provide both cooling and heating in the same unit, but the electrical data do not indicate the heat pumps’ 

operating mode. In addition to the electrical measurements, water temperatures were measured for the 

ground loop’s supply and return. Here, supply indicates that water arrives from the ground heat 

exchanger and supplies the heat pumps, and return refers to the water that returns to the ground heat 

exchanger. The temperature sensor for supply is located after the main pump(s), so it also includes the 

heat added by pumping. This effect was neglected because it would be difficult to formulate an accurate 

correction and the high heat capacity of water suggests the temperature rise from pump work would be 

slight. 

The temperature difference between the ground-loop supply and return was used to formulate an estimate 

of the split between energy used for cooling and for heating. Although several heat pump air system 

temperature sensors were added on March 7, 2002, the data for air temperatures were not comprehensive 

enough to be used to separate heating and cooling. The sign of the temperature difference between supply 

and return in the water loop shows whether the heat pumps were, in aggregate, adding or removing heat 

from the ground loop. We assumed that the HVAC energy use could be split between heating and 

cooling end uses by examining the sign of the ground-loop temperature difference. When the return 

temperature is higher than the supply, we assign HVAC energy use to cooling; when the return 

temperature is lower than the supply, we assign HVAC energy use to heating. Table 6-1 shows the 

results of this analysis for billing cycles. The method of sorting HVAC electricity use between heating 

and cooling is aggregated, which introduces errors when different parts of the building are being heated 

and cooled at the same time. The results show that the building is cooling dominated in its energy use, 

but heating affects the peak electrical demand almost as much as cooling (although demand charges are 

lower in the winter). 
61 

Table 6-1 Heat Pump Cooling and Heating Characterization 

Billing Period Cooling Site 

Energy (kWh) 

Heating Site 

Energy (kWh) 

Cooling 

Demand (kW) 

Heating 

Demand (kW)

November 2001 1,950 769 0 27 

December 2001 1,635 4,056 0 80 

January 2002 966 3,628 0 84 

February 2002 1,853 3,272 0 61 

March 2002 2,468 2,730 0 67 

April 2002 2,460 541 66 0 

May 2002 6,906 174 80 0 

June 2002 13,136 0 86 0 

July 2002 14,332 0 85 0 

August 2002 12,483 0 86 0 

September 2002 10,691 15 78 0 

October 2002 2,573 1,745 0 35 

Total 71,454 16,931 

Because the building was built before the monitoring effort was conceived, flow meters could not be 

installed on the ground loop. The initial measurement plan called for ultrasonic flow meters to monitor 

the flow rates, but the pipe configuration was not suitable. One lesson learned is that future monitoring 

efforts on buildings with ground-source loops should attempt to change or otherwise influence 

mechanical plumbing designs so that relatively long, straight, horizontal sections of pipe are available to 

form suitable locations for ultrasonic flow meters. An alternative is to install flow meters as part of the 

system engineering. 

The flow rate through the loop is controlled in response to pressure through variable speed drives on the 

pumps. (The central EMS controls the pressure set point.) The resistance of the loop can vary based on 

the number of heat pumps requesting water. Each heat pump opens a valve when it is operating. 

Although the flow rates were not measured directly, loop pump energy use was measured and we used 

this to model water loop flow rates. The model assumed that: 

• There was a simplistic linear relationship between power and flow. 

• The maximum flow rate equals the rated capacity of the pumps. 

• Both pumps have the same performance. 

• The peak measured pumping power corresponds to the maximum flow rate (full operation occurred at 

least once). 

With a model for ground-loop flow and measured supply and return fluid temperatures, heat transfer can 

be analyzed to and from the Earth through the vertical ground heat exchanger. Because it is important to 

consider ground-source performance over a long period, all the available 15-minute data were included in 

an analysis that extended for 640 days from November 3, 2001 through August 4, 2003. The results of 

this estimate are provided in Figure 6-2 as daily totals for heat rejected to the ground. The data in 

Figure 6-2 form a best estimate, but they are very sensitive to the estimated flow rates. Figure 6-3 shows 

the daily average supply temperature over the same period. Results indicate an imbalance in that more 

heat is rejected into the ground for cooling than is extracted for heating. The relative finding is that 3.9 
62 

times more heat is being rejected than extracted over 640 days. However, the supply temperature data 

shown in Figure 6-3 do not show evidence of long-term drifting over the period, which indicates that the 

ground appears capable of transferring the heat away from the heat exchangers. 

-1000

0

1000

2000

3000

4000

5000

11/3/01 2/11/02 5/22/02 8/30/02 12/8/02 3/18/03 6/26/03

Estimated Daily Total Heat 

Rejection to Ground [kBtu]

Figure 6-2 Daily total estimate for heat rejection to ground 

10

20

30

40

50

60

70

80

90

11/3/01 2/11/02 5/22/02 8/30/02 12/8/02 3/18/03 6/26/03

Daily Average Ground Loop 

Supply [oF]

Figure 6-3 Daily average ground-loop supply temperature 

The monitored data also show interesting short-term responses in the ground loop. Figure 6-4 shows the 

supply and return temperatures for a selected winter day and Figure 6-5 shows them for a selected 

summer day. On the winter day, the system appears to change from heating to cooling during the 

morning. On these particular days, supply temperatures varied by 3.9°F (2.2°C) over the course of single 

winter day and by 14.8°F (8.2°C) over a summer day. These results show the importance of short-term 

modeling, although sizing of ground-source heat pump systems is usually based on models with a time 

resolution of one month. 
63 

30

35

40

45

50

55

60

65

70

0:00 4:00 8:00 12:00 16:00 20:00 0:00

 Time of Day

Ground Loop Water 

Temperature [oF]

Supply 

Return

Outdoor

Figure 6-4 Short-term response of ground loop: January 18, 2002 

65

70

75

80

85

90

95

100

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Time of Day

Ground Loop Water 

Temperature [oF]

Supply 

Return

Outdoor

Figure 6-5 Short-term response of ground loop: June 27, 2002 

The monitored data also provide a way to compare the ground-loop supply temperatures to the outside air 

temperature. The temperatures of heat sinks largely determine the thermodynamic efficiency of heat 

pump cooling. The purported advantage of a ground-loop arrangement that uses water-to-air heat pumps 

over rooftop packages with air-to-air direct expansion mechanical cooling is that ground-loop 

temperatures are expected to be lower than air temperatures and lead to more efficient cooling. NREL 

analyzed this assumption for the Merrill Center by comparing the ground loop and outdoor air 

temperature data for the model analysis year (November 7, 2001 to November 6, 2002). We first sorted 

the data every 15 minutes during this time to determine when cooling occurred—83% of the time, loop 

return fluid temperature was warmer than supply. We then analyzed the data to determine how outdoor 

air dry-bulb temperatures compared to the ground-loop temperature. The data show that for 61% of the 

time, cooling occurred and the outdoor air was actually cooler than the ground loop that supplies the heat 

pumps. Analysis of energy use shows that of the 71.4 MWh of electricity used for cooling, 42% was used 

when the outdoor temperature was cooler than the ground-loop supply. Figure 6-6 shows a time map of 

this and shows the magnitude of the temperature difference (calculated by subtracting the outdoor air dry 
64 

bulb temperature from the ground-loop supply) when the situation occurs. The problem is minimal 

during the summer but significant during fall, winter, and spring. 

Figure 6-6 Temperature difference between water loop supply and outdoor air 

when greater than 0.0 and not heating 

The results show that temperatures in the ground loop fluctuate widely and are sometimes higher than air 

temperatures. The fluctuating temperatures could result because the ground wells are undersized for this 

application, a plumbing problem artificially short-circuits the loop, or some other phenomenon restricts 

the performance of the system. An alternative method of solving the problem would be to add a 

conventional cooling tower to cool the water when ambient conditions are more favorable than ground 

conditions. 
65 

The data also allowed NREL to examine how much of the cooling energy is consumed when outdoor air 

conditions would be favorable for economizer operation (were the building to have this feature). For this 

examination, we sorted and summed HVAC cooling energy use for times when the outdoor air 

temperatures are below 58°F (14.3°C). The analysis revealed that of the 71.4 MWh of electricity used for 

cooling, 15% was used when the outdoor temperatures were favorable for so-called free cooling. 

6.1.2 Natural Ventilation Evaluation 

Although the monitoring effort did not specifically focus on evaluating natural or mixed-mode ventilation 

in the Merrill Center, some data that were collected are useful for evaluating the system. 

Earlier research by Chang noted that the designer’s intent was to take advantage of winds that flow from 

south to north, but that winds in the area tend to flow from the northwest when outdoor conditions are 

good for natural ventilation. The current study measured wind direction and velocity, and enabled 

researchers to further quantify this assessment. The monitored data were analyzed to determine the 

distribution of wind directions for times when the following conditions were met: 

• Outdoor dry-bulb temperatures were between 39.4°F (4°C) and 71.8°F (22°C). 

• Relative humidity was between 20% and 65%. 

• Daytime hours were between 8:00 a.m. and 6:00 p.m. 

• Wind velocity was stronger than 2.2 mph (1.0 m/s). 

Figure 6-7 shows the results of a histogram analysis of the wind direction when these conditions were all 

met between November 7, 2001 and November 6, 2002. The data confirm that winds often come from 

the northwest. They also come from the east as often as from the south. The monitoring data show that 

designers should not have assumed that the winds from the south (off the bay) would be the most 

important winds for natural ventilation cooling. 

0

50

100

150

200

250

300

350

400

450

500

0 90 180 270

Wind Direction (degrees clockwise from north)

Number of 15-minute 

observations of wind direction

Figure 6-7 Distribution of wind direction during periods amenable to natural 

ventilation over the model analysis year 

Although winds do not come from the north, designing for cross ventilation in the east to west and west to 

east directions would have been an improvement. The discrepancy between expected and measured wind 

directions also suggests that engineered natural ventilation systems should be designed to operate by stack 

forces rather than by wind. 

In practice, the so-called natural ventilation in the Merrill Center is often operated as a hybrid system with 

the aid of exhaust fans. The system essentially fits the role of an economizer, but without the controlled 
66 

air distribution provided by ductwork and registers or the control afforded by controlling to a mixed-air 

set point. This mode of hybrid operation could be more efficient because of lower static losses. 

The exhaust fans are used in the winter when the windows are closed and makeup air comes from 

infiltration. This type of system operation makes up for the lack of economizer operation. However, an 

economizer would be better because it is controlled to a mixed air set point and could be expected to 

provide more uniform comfort. Infiltration does little to ensure that cooling effects are evenly distributed; 

it also increases static pressure, which causes the exhaust fan to work harder. 

6.2 Photovoltaic System Evaluation 

6.2.1 Photovoltaic System Evaluation Methods 

The Merrill Center includes a 4.2-kW, thin-film PV power system that generates electricity. The 

electricity produced by the inverters is metered where it enters an electrical panel on the second floor. 

The solar resources are measured with two photometers, one situated horizontally and the other vertically 

facing south. The system was evaluated by examining the measured electricity generation and by 

comparison to simulations of the PV system (Mermond 1996). 

6.2.2 Photovoltaic System Evaluation Results 

Table 6-2 provides a summary of the monthly consumption, production, net purchased, maximum 

production, and percent load met by PV power. The net purchased electricity is the difference between 

the total electricity consumed in the building for a month and the total electricity produced for the month. 

The maximum recorded power over a 15-minute period was 3.4 kW compared to the nominal rated power 

capacity of 4.2 kW. Higher power was delivered during winter than during summer; Table 5-8 showed 

that the largest reduction to demand charges was 2.3 kW. The PV system contributes about 0.8% of the 

electricity used in the Merrill Center. 

Table 6-2 PV Power Performance Summary: 

November 7, 2001 – November 6, 2002 

Month/Year 

Total Electrical 

Consumption 

(kWh) 

PV 

Production 

(kWh) 

Net Electrical 

Purchased 

(kWh) 

Max PV 

Power 

(kW) 

% Electrical 

Load Met by 

PV 

November 2001 27,690 301 27,389 3.00 1.1% 

December 2001 28,497 230 28,267 2.92 0.8% 

January 2002 26,444 224 26,220 3.10 0.8% 

February 2002 29,821 334 29,487 3.38 1.1% 

March 2002 27,074 183 26,891 3.21 0.7% 

April 2002 23,935 190 23,745 1.88 0.8% 

May 2002 28,833 208 28,625 1.72 0.7% 

June 2002 32,427 189 32,238 1.45 0.6% 

July 2002 34,829 196 34,633 1.83 0.6% 

August 2002 31,830 211 31,619 1.96 0.7% 

September 2002 29,781 246 29,535 3.26 0.8% 

October 2002 25,380 164 25,216 3.31 0.6% 

Totals (Sum or 

Average) 346,541 2,676 343,865 3.31 0.8% 
67 

Figure 6-8 shows measured results for daily electricity production and compares them to the solar 

resources for the model analysis year. The solar electric production closely tracks the solar resource on a 

south-facing vertical surface and produces more energy in the winter months. This is atypical of PV 

systems as most produce more electricity in the summer. The PV panels are tilted at 30° from horizontal, 

and it is unusual that the performance tracks vertical insolation more closely than horizontal. 

Figure B-3 (in Appendix B) shows a time map of all the 15-minute measurements. Shading of the panels 

is evident in the unusual patterns of production compared to the solar radiation shown in Figure C-5. 

0

2

4

6

8

10

12

14

16

18

1/1/20032/1/20033/1/20034/1/20035/1/20036/1/20037/1/20038/1/20039/1/2003

10/1/200311/1/200312/1/2003

Day of Year

Produciton kWh/day

0

1

2

3

4

5

6

7

8

Resouce kWh/m2day

PV production South Vertical Solar Resource

Figure 6-8 Daily PV power production and solar resource 

Figure 6-9 shows the average hourly power profile for AC PV production for each billing period from 

November 7, 2001 through November 6, 2002. The monthly comparison of PV production profiles 

demonstrates the unexpected increase in PV production from summer to winter months. The maximum 

15-minute average AC PV power production varies from 1.8 kW in September to 0.4 kW in April 

(Eastern Standard Time). 
68 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Time of Day

Photovoltaic AC Power (kW)

Nov-01

Dec-01

Jan-02

1-Feb

Mar-02

Apr-02

May-02

Jun-02

Jul-02

Aug-02

Sep-02

Oct-02

Figure 6-9 Average hourly AC power profile by month for PV power production 

by billing period 

Figure 6-10 shows the results from simulation compared to the measurements. Simulations (Mermond 

1996) were run with weather data for the measurements and the typical meteorological year data for 

Baltimore. To determine the potential of the PV array, the simulations account for tilt, but not for shading 

by overhangs or the conference pavilion. 
69 

0

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Monthly Production (KWh)

Actual 

Simulated with measured weather

Simulated with Baltimore TMY

Figure 6-10 Monthly PV power production compared to simulated results 

This section presented results for performance of the PV system that clearly show the system is producing 

less energy than it should because of the shading from the overhead trellis and the conference pavilion. 

PV systems should be located where the panels will not be shaded. 

6.3 Ancillary Daylighting Evaluation 

6.3.1 Daylighting Evaluation Methods 

Additional measurements were made with photometers to evaluate how well the building is lit by daylight 

that enters the building through the windows. In contrast to continuous monitoring over a year, we 

obtained these ancillary measurements over short time periods and used a mixture of handheld and 

continuous measurements, which were closely supervised by researchers. The daylighting evaluation 

protocol was derived from the work of Atif et al. (1997). The intent of this analysis is to understand how 

well this resource is being used by determining the extent to which daylight might meet the lighting 

needs. Recommended lighting levels are 30–50 f.c. (300– 500 lux), and where daylight can provide the 

recommended lighting levels, the analysis shows where electric lighting can be reduced. 

Daylighting was measured over the course of three site visits at different times of the year. The first 

measurements were taken near the summer solstice from June 28 to July 1, 2001. The second period was 

near the autumn equinox from September 21 to September 22, 2001. The third visit was during winter 

from January 17 to January 22, 2002. During each visit, dataloggers were set up to continuously collect 

data from photometers at 5-minute intervals. Additional measurements taken with handheld photometers 

to collect supplementary data that cover a wider distribution of locations in the building. 

For daylighting to save energy, the use of electric lighting must be reduced in response to natural light. 

An aggressively daylit building should show significant reductions in lighting electricity during daytime 
70 

hours. Therefore, to quantify the amount that daylighting reduces electric lighting in the Merrill Center, 

we compared the daily average lighting profiles for working weekdays between the summer and the 

winter. The early darkness in January that overlaps with working hours shows the lighting levels without 

daylight harvesting. 

6.3.2 Daylighting and Lighting Evaluation Results 

The measured data, which indicate there is good daylight availability on the second floor and less on the 

first floor, support qualitative assessments of the Merrill Center. Although research protocols for 

gathering photometric data are adequate, and the data collected are considered robust, more research 

needs to be done to improve methods of processing the data to make them more useful. Therefore, this 

study does not present results in detail, but provides samples of the measurements. 

Figure 6-11 shows a sample of the 5-minute interval photometer results from a sunny day on the second 

floor. Figure 6-12 shows the locations for the results shown in Figure 6-11. A typical target for lighting 

is 30–50 f.c. (300–500 lux). Results show higher values everywhere except location 2, which indicates 

that enough natural light is available. Samples of the handheld measurements are provided in 

Figure 6-13, Figure 6-14, and Figure 6-15 for locations spread around the entire second floor at a height 

of 3 ft. (1 m). 

0

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Figure 6-11 Sample of automated photometric measurements on the second 

floor (September 22, 2001) 
71 

Figure 6-12 Locations for automated photometric measurements on the second 

floor (September 22, 2001) 

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Figure 6-13 Handheld photometric measurements on second floor: June 30, 

2001: 1:00 p.m. (bright diffuse sky with electric lights off) 
72 

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Figure 6-14 Handheld photometric measurements on second floor: 

 January 2, 2002: 9:00 a.m. (clear sky with electric lights off) 

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Figure 6-15 Handheld photometric measurements on second floor: 

 September 22, 2001: 12:00 p.m. (mostly clear sky with electric lights 

off) 
73 

Figure 6-16 Glare problems caused by daylighting 

The results indicate that daylight penetrates across the second floor, but that glare problems can be 

expected, as shown by the high readings (greater than 186 f.c. [2000 lux]) and observations of occupants 

setting up makeshift shading devices (shown in Figure 6-16). The problem suggests that a more robust 

solution for reducing glare should be explored. The broad expanse of windows along the south façade has 

more area than would be needed for daylighting purposes alone. (In addition, this wall probably creates 

additional cooling and heating loads.) There are several solutions to this problem, and a detailed design 

solution should be engineered. Placing diffusing films on the glass is the easiest solution; however, this 

solution would reduce the view to the bay. The diffusing films, as well as light-deflecting devices, would 

help light the ceiling. Hanging translucent shades would also minimize the glare. 

Figure 6-17 shows the average weekday lighting energy use for the lights (except task lighting) on the 

second floor of the main building for January and June 2002. The shorter days in January mean that in 

the evening, less light is available and more electric lights are in use. The data indicate that because of 

daylighting, the average reductions in electric lighting on the second floor of the main building are 

roughly 20%. The difference is seen from January through June between 5:00 p.m. and 6:00 p.m. The 

early morning peak is related to the cleaning crew. A lighting power reduction of 20% is considered low 

for an aggressively daylit building, which leads to the conclusion that daylight is not harvested well or the 

daylighting design is not adequate. 
74 

0.0

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0:00 4:00 8:00 12:00 16:00 20:00

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June

Daylight 

reduction

Figure 6-17 Average weekday profile of energy use for second-floor lighting for 

January and June 

Daylighting and indirect lighting fixtures benefit from lightly colored interior surfaces that reflect light. 

Figure 6-18 shows the relatively dark colors in the Merrill Center’s interior because of unfinished wood 

products, the rough surface of OSB and laminated beams, and exposed ductwork. Dark colors are counter 

to design for daylighting. Finishing the interior, especially the ceiling, would provide surfaces with 

higher reflectivity and brighten the space, which would allow for increased use of daylighting and less 

waste from the indirect fluorescents. Although results indicate that daylight is sufficient, this is because 

of the large amount of glazed area and despite the dark interior colors. The distribution of natural 

daylight and indirect electric lighting would be improved by using lighter colored interior finishes. 

Daylighting design analysis should take into account obstructions from structural beams and ductwork. 
75 

Figure 6-18 Second-floor ceiling, beams, and ducts 

The lighting controls in the open second-floor office are manual on/off (except for the first two rows, 

which are dimmable and connected to photocells). More of the lighting on the second floor could be 

controlled to harvest daylight. The operator currently switches banks of lights off (sometimes by 

removing bulbs), but automatic controls would represent a better long-term solution. Furthermore, the 

overhead fixtures are wired so that both the indirect and direct sections must be switched on or off 

together. The fixtures should be replaced or rewired so that the indirect up lights can be turned off 

separately from the down lights. Replacing the fixtures or rewiring would allow the indirect lights to be 

turned off when daylighting is present. User satisfaction is usually higher for dimming systems—the data 

indicate that nearly all areas of the second floor receive some daylighting and can therefore achieve some 

level of electric lighting saving. It may be possible to switch some fixtures off, especially up lights when 

daylighting is present. 

Experience with another building project (Torcellini et al. 2003) showed that a single photometer could be 

used to measure natural light and then used to control separate lighting zones (where each has its own 

setting relative to the single photometer). A photometer would work well on the second floor of the 

Merrill Center because light from the south windows dominates the daylighting. In addition, each 

lighting zone should have occupancy sensors and individual overrides so lights can be turned off when no 

one is present. Lights on the second floor used 37,980 kWh of electricity, which at a virtual rate of 

$0.091/kWh represents expenditures of $3,470/yr. Although detailed as-built analyses would produce a 

more refined estimate, improving the lighting controls and fixtures could probably reduce second-floor 

lighting energy use by 50%, which would save $1,700/yr. Some lighting fixtures in closets do not have 

on/off switches and do not need to run continuously; switches should be installed to allow lights to be 

turned off. 

 
76 

7 Recommendations 

This section discusses recommendations developed from lessons learned from our analysis of the Merrill 

Center. The term lessons learned is used in this paper to refer to either positive or negative aspects of a 

project that have a clear educational message that can help subsequent building projects or research 

efforts. Recommendations are intended to help those interested in low-energy design avoid repetition of 

mistakes and to improve the process of constructing low-energy buildings. The Merrill Center can be 

considered an excellent example of a green building. However, its energy performance is not what it 

could have been, so this section discusses aspects that could have been done better. This section is 

organized to correspond to the phases of the Merrill Center project: design process, operation, 

postoccupancy alterations, and performance analysis. 

7.1 Design Process 

Listed here are some general recommendations to improve the process of designing low-energy buildings. 

Although the design process was not explicitly studied, examining the energy performance of the Merrill 

Center illustrated several factors that could improve this process. 

Recommendation # 1–Design passive cross ventilation for multiple airflow paths and natural 

stack effect. 

Winds are important resources for daytime, natural cross ventilation. Section 6.1.2 showed that when 

designing for cross ventilation in the Merrill Center, seemingly safe assumptions about the direction of 

winds may be false. For this project, cooling breezes off the bay appeared to be ample for daytime 

cooling. The design of the building’s operable fenestration and intended natural ventilation airflow 

pathways were based on a single wind direction. Chang (2002) first observed, and monitoring data 

support the finding, that often the winds are not off the bay from the south. For the Merrill Center, it 

would have been better to design for more east-west cross ventilation. The Merrill Center should serve as 

an example of the importance of designing naturally ventilated buildings for a variety of airflow pathways 

and wind directions. Natural ventilation should be designed to rely primarily on stack effect unless winds 

are reliable. Designers should use appropriate computer models that predict airflow to help design such 

systems. Finally, although occupant operation of manual windows has not been a problem for the Merrill 

Center, designers should be aware that interest in operating them has waned somewhat and that automated 

operation of windows may be preferable. 

Recommendation # 2–Install PV panels where they will not be shaded. 

PV panels should always be located where they will not be shaded to maximize their energy production. 

In the case of CBF, the panels are shaded, especially in the summer, which reduces their efficiency. 

Figure 4-7 shows the panels shaded by structures above them and by the conference pavilion. One benefit 

to the panels in the current location is that they are highly visible from both inside and outside the 

building, which is useful for educating visitors. 

Recommendation # 3–Use models with short-term response when designing ground-source 

heat exchangers. 

Ground-source heat exchangers and heat pumps have been promoted for low-energy buildings. 

Experience with the Merrill Center indicates that although the system can deliver good space comfort, 

temperature fluctuations in the ground loop are greater than expected. Monitored data for fluid 

temperatures that return from the ground heat exchanger clearly show that loop temperatures can be high 

and show considerable seasonal (see Figure 6-3) and daily (see Figure 6-4 and Figure 6-5) fluctuations. 

When modeling is used to design such systems, the models typically use constant fluid temperatures for 

each month. Although such analysis attempts to ensure that the field can perform over a long period 
77 

(years), they do not capture the fluctuation of temperatures over the short term, as shown in Figure 6-4 

and Figure 6-5. The next-generation design tools for ground-source heat pumps, such as those now 

available in EnergyPlus, should be developed and applied when evaluating and designing such systems. 

7.2 Operation 

This section discusses issues related to how the building is operated. 

Recommendation # 4–Reprogram the EMS to run heat pumps based on current utility demand 

to better manage demand charges. 

As discussed in Section 6.1, the HVAC heat pumps often contribute significantly to demand charges. The 

EMS could be programmed to reduce the number of heat pumps that can run at the same time to reduce 

demand charges. During the winter, the heat pumps lead to demand peaks during morning warmup. 

More time for warmup should be allowed and the warmup should be staged to minimize the demand. 

This change would not necessarily save energy, but it could reduce the peak electricity demands and 

reduce costs. 

Recommendation # 5–Reduce receptacle loads during off hours. 

As discussed in Section 5.2.9, off-hour receptacle loads are about half what they are during the day, 

which indicates that equipment is left on when not in use. Presumably, this is because a large amount of 

information technology equipment operates in the offices and server room. CBF could deploy new 

network-based software products that monitor and reduce computer power. Because the receptacle loads 

are already submetered, the Merrill Center would be a good candidate for future efforts to research the 

effectiveness of such software-based solutions to control off-hours plug loads. 

7.3 Alterations 

This section suggests postoccupancy alterations. Predictive capabilities during the design phase are not 

perfect, and once the building is up and running, there should be an opportunity to revisit the design and 

make changes. The usual commissioning activities do little to improve the operation of a building beyond 

its designed capabilities. Experience with other low-energy buildings shows that after a building is 

constructed (and has been commissioned to verify its system components are as designed) there remains a 

significant need to adjust lighting and HVAC controls to further reduce energy use and improve occupant 

satisfaction. In addition, capital equipment changes can be made to improve performance. 

7.3.1 Lighting 

Recommendation # 6–Provide second-floor interior with high-reflectivity finish. 

Daylighting and indirect lighting fixtures benefit from lightly colored interior surfaces that reflect light. 

The Merrill Center’s unfinished engineered wood products are good from the point of view of resource 

use, but the relatively dark colors and rough surfaces of OSB and laminated beams reduce the 

effectiveness of the daylighting and the indirect lighting system. Finishing the interior, especially the 

ceiling, to provide surfaces with higher reflectivity would brighten the space and make the daylighting 

more effective. In addition, the high ceilings reduce the effectiveness of indirect lighting. Indirect 

lighting should be used only when the ceiling is close to the lighting source. 

Recommendation # 7–Install new lighting controls in open office. 

Automatic lighting controls should dim and energize lighting circuits according to available daylight. 

Manual controls limit the amount of daylighting that can be harvested. A single photocell could be 

installed on the second floor, and second-floor lighting zones could be calibrated against this sensor for 
78 

on/off control. Dimming fixtures should be controlled with local photocells to maintain a lighting level of 

40 f.c. (431 lux). 

Recommendation # 8–Monitor and design a system to minimize glare issues. 

The south wall has more glazing than necessary for daylighting and causes glare problems on the second 

floor. There are several potential solutions to this problem, and a detailed design solution should be 

engineered in follow-on efforts. Diffusing films on the glass are the easiest solution; however, this will 

reduce the view to the bay. These films, as well as light deflecting devices, would help light the ceiling of 

the building. Hanging translucent shades would also minimize the glare. Flat screen computer monitors 

would help with glare problems and reduce plug loads. 

Recommendation # 9–Install light switches in closets 

Some closets in the Merrill Center have linear fluorescent fixtures that cannot be turned off. Simple light 

switches or motion sensors should be installed to allow these to be turned on and off as needed. 

7.3.2 Ground-Source Heat Pumps 

Recommendation # 10–Evaluate and inspect the ground-loop system and consider increasing 

heat exchanger capacity and control improvements. 

This recommendation stems from the findings, discussed in Section 6.1.1, that temperatures in the ground 

loop fluctuate widely and are often higher than expected. Additional detailed analysis of the ground loop, 

including a detailed review of the construction drawings, should be performed to understand how the 

system was designed. Based on the measured energy performance, the field may be undersized for the 

actual loads. The sizing calculations should be redone to include the unused desiccant dehumidification 

system. However, a simple mistake could be to blame: some of the 48 wells may be inactive or there may 

be a poor balance of flow between the four circuits. Follow-on efforts should verify that the field has 

been installed as designed and that all wells have the appropriate flow rates. Future projects should 

include field tests to verify that ground heat exchangers are installed as designed. 

As part of a follow-up evaluation, the addition of a cooling tower should be considered. Currently, 

temperatures are cooler than the ground-loop temperatures 61% of the time (mostly during the winter and 

shoulder seasons) and about four times more heat is added to the ground for cooling than is extracted for 

heating. With such a California-style heat pump arrangement, the ground-source heat pumps would be 

used only when ground temperatures are more favorable than ambient conditions. Further analyses 

should be done to study the rainwater collection system to see if a wet cooling tower would be feasible; 

otherwise, a dry cooling tower would need no water. 

As part of this recommendation, the control algorithms for variable-speed ground-loop pumps should be 

examined. It may be better to control loop pumps based on supply temperatures (or temperature 

differences) with limits set by pressure. Such research should seek to balance pump energy, ground heat 

exchanger effectiveness, and heat pump efficiencies. 

The server room contains a small water-to-air cooling system that requires continual operation of the 

main ground loop regardless of the heating or cooling needs of the rest of the building. The water flow 

needed by this small system should be compared to the minimum flow rates that the large variable speed 

pumps can deliver. If a mismatch is found, alternative methods of cooling the server room should be 

explored so that the main ground loop pumps do not need to run continuously. 
79 

7.3.3 Outside Air System 

Recommendation # 11–Refit the air system to allow economizer operation. 

The outside air system in the Merrill Center includes natural ventilation features as well as air handlers. 

One way to think about passive natural ventilation is that it essentially economizes without fans, and, if it 

is fan assisted, it is much like running an economizer. However, from a thermal comfort point of view, it 

is limited to moderate temperatures. An economizer-based system can control supply air temperature to 

provide thermal comfort for a wider range of outdoor temperatures. 

This study found that considerable energy is being expended for heat pump cooling during the winter (see 

Table 6-1), which indicates that natural ventilation systems do not always deliver cooling during the 

heating season. This recommendation is put forth with the caveat that additional analysis should be 

conducted to better understand the implications for fan energy use (exhaust fans versus air handler supply 

fans), and the costs and benefits of refitting the air handlers for economizer operation. Because the 

conference pavilion does not have the fan-assisted natural ventilation, it may be more important to install 

an economizer there. 

Recommendation # 12–Refit the Merrill Center’s air system to either remove or use the 

desiccant wheel system. 

The desiccant wheel dehumidification system in the Merrill Center is not used as designed and its 

potential benefits should be evaluated. If the wheels are not going to be used, they should be removed 

because they increase the pressure drop and fan energy used to provide fresh air. One option is to use the 

wheels in a heat recovery mode without regeneration. If the wheels are retained for use and an 

economizer system is added, the revised air handler should be configured to allow the wheels to be 

bypassed during economizer operation to minimize fan energy. 

7.3.4 Exterior Shading 

Recommendation # 13–Examine exterior shading options and add shading where beneficial. 

Cooling requirements may be further reduced by adding exterior shades. Some exterior shading on the 

conference pavilion was removed and should be reinstalled. The structure outside the south-facing glass 

on the main building might be refitted with additional shading. Before adding shading, a detailed analysis 

of the benefits that takes into account increasing heating loads, reducing PV output, decreasing 

daylighting, and mitigating glare problems should be made. 

7.4 Energy Performance Monitoring and Analysis 

Conducting this study of the Merrill Center led to the identification of additional topics that warrant 

research. 

7.4.1 Monitoring Equipment 

Future research and analysis of the Merrill Center should consider various changes and additions to the 

detailed monitoring. The dedicated monitoring system was very successful and provided robust data for 

the analysis. Many channels on the data acquisition system are still available. 

Recommendation # 14–Add temperature sensors to record the temperature of fluid in the four 

pipes that return from the ground wells. 

There are two advantages to installing sensors on the pipes that return from the ground wells. First, the 

data may show how well balanced fluid flow and heat transfer is between the four circuits. Individual 
80 

readings may show a plumbing or design problem. Second, it would be good to have readings before and 

after the loop pump to quantify any temperature rise from the pump. 

Recommendation # 15–Add flow sensors to record the flow rate of fluid in the ground-loop wells. 

With the current plumbing configuration, it was not possible to install a single flow transducer. Followup research activities that focus on ground-source heat pumps should attempt to directly measure the total 

flow in the loop. For the Merrill Center, this may require installing four meters on each circuit. For 

future projects, it is advantageous for maintenance and monitoring to install flow meters in the ground 

loops at time of construction. This should be considered an integral part of a ground-loop system. 

Recommendation # 16–Reinstall outdoor weather station. 

Contractors removed the weather station instrumentation in November 2002. These sensors should be 

reinstalled as part of any ongoing research efforts. 

Recommendation # 17–Record data from the energy management system. 

Several sensors in the building are connected to the EMS and are not recorded with the monitoring data. 

For example, the current monitored data do not indicate when the building is in natural ventilation mode, 

but the EMS has these data. The attentive building operator often uses the EMS to adjust the building, so 

modeling the ever-changing building controls is difficult. A preliminary list of data that should be 

recorded from the EMS includes thermostat set points, states of all systems related to natural ventilation 

(exhaust fans, window operators, and the Windows Open signage), CO2 sensor, air-handler stage, and 

propane flow. The robust recording of EMS data for long periods and successful merging of these data 

with the stand-alone dataloggers are challenges. One option is to add analog and relay outputs to the 

EMS and monitor them with the dataloggers. 

Recommendation # 18–Instrument domestic hot-water system. 

The Merrill Center meets all its DHW needs with solar thermal collectors. This study did not account for 

the energy performance and savings from this system. Thermal instrumentation should be checked for 

accuracy and any additional instrumentation needed added to allow proper analysis of the DHW system. 

Recommendation # 19–Instrument natural ventilation system. 

This study did not focus on natural ventilation. However, such systems are of interest because of their 

potential application in office buildings. Follow-up research on natural ventilation should consider 

adding instrumentation to submeter electricity used for assist fans, count opening and closing events of 

selected windows, and additional metrics related to occupant thermal comfort. Chang’s attempt to use 

low-cost cameras to monitor window operations merits further study. 

Recommendation # 20–Develop monitoring capability to directly measure horizontal infrared 

radiation weather data. 

This project generated weather data from monitoring results for use with EnergyPlus. However, we 

discovered a lack of appropriate methods (see Appendix E) for determining values for either cloud cover 

or horizontal infrared radiation from the weather data that were collected. It is not practical to have an 

observer manually record cloud cover. Furthermore, such data are being dropped from standard weather 

station observations. For future monitoring projects, techniques should be developed to directly measure 

horizontal infrared sky radiation. Silicon-based photometers can be used to apply thermopile-type 

detectors that directly measure infrared radiation (or effective sky temperature) in a manner analogous to 

solar measurements. 
81 

7.4.2 As-Built Modeling 

Recommendation # 21–Complete an as-built EnergyPlus model. 

The original plan for this study was to use the EnergyPlus whole-building energy analysis program to 

develop a detailed as-built model of the Merrill Center. Unfortunately, the model was not completed 

because the model for water-to-air heat pumps was not available in EnergyPlus until late in the project. 

Usually, it is difficult for most energy modeling programs to accurately simulate two separate types of 

HVAC systems that serve the same zone, such as the heat pumps and hot-water baseboards used at the 

Merrill Center. However, EnergyPlus can model various types of equipment that serve the same zone and 

a large number of measured data are available for comparison to model results; therefore, we recommend 

that the as-built model be revisited if future research is done on the building. 

Recommendation # 22–Model natural ventilation systems. 

Additional modeling of the Merrill Center is warranted because of the current level of interest in natural 

ventilation for commercial buildings. Although current monitoring data are insufficient to provide highquality validation of natural ventilation modeling, the availability of well-calibrated internal gains and 

measured weather data, along with the possibility of follow-up monitoring, make the Merrill Center an 

attractive test case for such efforts. 

7.4.3 Real-Time Modeling 

Recommendation # 23–Develop a near real-time energy savings meter. 

The original measurement and verification plan called for implementing a near real-time energy savings 

meter. The concept for such a meter is to continuously take measured data and automatically run baseline 

or as-built models to determine how well the building performs. Such a meter could not be implemented 

because the weather station was dismantled. The Merrill Center is a good candidate for this type of 

research because the building has already been instrumented and it has a motivated operator who is likely 

to use the information to improve the operation of the building. 
82 

8 Conclusions 

The Merrill Center is a 31,000-ft2 (2,880-m2) building that successfully demonstrates a variety of 

sustainable building practices. This report assessed the energy performance of the Merrill Center through 

a combination of physical monitoring and computer simulation. (Results are summarized in Table 8-1.) 

The assessment leads to the following conclusions about the Merrill Center: 

• The level of total site energy use savings, including receptacle and miscellaneous loads, is 24.5% 

compared to a minimally code-compliant building. 

• The level of source energy savings, including receptacle and miscellaneous loads, is 22.1% 

compared to a minimally code-compliant building. 

• The level of energy cost savings, including receptacle and miscellaneous loads, is 12.1% 

compared to a minimally code-compliant building. 

• The use of building monitoring and calibrated simulations can quantify differences between 

design expectations and actual building energy performance. 

• During the cooling season, the temperature of water that returns from the ground heat exchanger, 

which supplies the water-to-air heat pumps, is often warmer than might be expected. This 

indicates a need for additional research on the performance of the ground-source heat pumps. 

• Daylight is not harvested as well as it could be on the second floor. 

• The Merrill Center is a good candidate for follow-up research. 

This study led to the following general conclusions: 

• A long-term monitoring effort can collect detailed, annual data sets for energy use and weather 

that are suitable for evaluating energy performance. 

• The new Appendix G for standard ASHRAE/IESNA 90.1 2001 is helpful for defining baseline 

energy models. 

• An analysis of error in the baseline building energy models shows that such models have 

considerably more uncertainty than actual measurements that establish a true energy balance. 

• Significant differences were found between the actual energy performance and predictions made 

for rating purposes. This indicates a need to improve protocols for rating and to continue efforts 

to study actual buildings. 

Table 8-1 Energy Savings Summary: November 2001–November 2002 

Total Site Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Site Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Net Source Energy 

Use Intensity 

kBtu/ft2·yr 

(MJ/m2·yr)

Energy Cost 

Intensity 

$/ft2·yr 

($/m2·yr)

Baseline Model 53.3 

(604.8) 

53.3 

(604.8) 

159.6 

(1,812) 

1.20 

(12.87) 

Monitoring 40.2 

(456.8) 

39.9 

(453.2) 

124.3 

(1,412) 

1.05 

(11.31) 

Savings 24.5% 25.0% 22.1% 12.1% 
83 

9 References 

ASHRAE. (2001a). Handbook of Fundamentals – 2001. Atlanta, GA: American Society of Heating, 

Refrigerating and Air-Conditioning Engineers, Inc. 

ASHRAE. (2001b). ANSI/ASHRAE/IESNA Standard 90.1-2001–Energy Standard for Buildings Except 

Low-Rise Residential Buildings. Atlanta, GA: American Society of Heating, Refrigerating and 

Air-Conditioning Engineers, Inc. 

ASHRAE. (2002). BSR/ASHRAE/IESNA Addendum e to ANSI/ASHRAE/IESNA Standard 90.1-2001. First 

Public Review. July 2002. Atlanta, GA: American Society of Heating, Refrigerating and AirConditioning Engineers, Inc. 

ASHRAE. (2003a). BSR/ASHRAE/IESNA Addendum e to ANSI/ASHRAE/IESNA Standard 90.1-2001. 

Second Public Review. April 2003. Atlanta, GA: American Society of Heating, Refrigerating 

and Air-Conditioning Engineers, Inc. 

ASHRAE. (2003b). HVAC Applications Handbook. Atlanta GA: American Society of Heating 

Refrigerating and Air-Conditioning Engineers, Inc. 

Atif, M.; Love, J.; and Littlefair, P. (1997). Daylighting Monitoring Protocols & Procedures for 

Buildings. Report #T21/D2.1/97-01. IEA Solar and Heating Program Task 21 Subtask D. 

Canada: Institute for Research in Construction. 

Bronson, D.J.; Hinchey, S.B.; Haberl, J.S.; and O’Neal, D.L. (1992). A Procedure for Calibrating the 

Doe-2 Simulation Program to Nonweather-Dependent Measured Loads. ASHRAE Transactions. 

Vol. 98 (1): pp. 636-652. Atlanta GA: American Society of Heating, Refrigerating and AirConditioning Engineers, Inc. 

Chang, J.-C. (2002). Case Studies of Naturally Ventilated Commercial Buildings in the United States. 

MSc Thesis. Cambridge, MA: Department of Mechanical Engineering. Massachusetts Institute 

of Technology. 

Crawley, D.B.; Lawrie, L.K.; Winkelmann, F.C.; Buhl, W.F.; Huang, Y.J.; Pedersen, C.O.; Strand, R.K.; 

Liesen, R.J.; Fisher, D.E.; Witte, M.J.; and Glazer, J. (2001). EnergyPlus: Creating a NewGeneration Building Energy Simulation Program. Energy and Buildings. Vol. 33 [4], pp. 319-

331. Amsterdam: Elsevier Science. Available at www.elsevier.com or www.energyplus.gov. 

DOE. (2004). 2004 Buildings Energy Databook. Washington, D.C.: U.S. Department of Energy. 

http://buildingsdatabook.eren.doe.gov/. Accessed September 2004. EIA Annual Energy Outlook 

2003. Washington, D.C.: Energy Information Administration. U.S. Department of Energy. 

Klein, S.A.; Duffie, J.A.; et al. (1976). TRNSYS – A Transient Simulation Program. ASHRAE 

Transactions. Vol. 82, p. 653. Atlanta GA: American Society of Heating, Refrigerating and AirConditioning Engineers, Inc. See also http://sel.me.wisc.edu/trnsys/. 

Lomas, K.J. and Eppel, H. (1992). Sensitivity Analysis Techniques for Building Thermal Simulation 

Programs. Energy and Buildings. Vol. 19, pp 21-44. Amsterdam: Elsevier Science. Available at 

www.elsevier.com. 

MacDonald, I.A. (2002). Quantifying the Effects of Uncertainty in Building Simulation. Ph.D Thesis. 

Glasgow, UK: Univ. of Strathclyde. 

Maxwell, E.L.; Marion, W.F.; Myers, D.R.; Rymes, M.D.; and Wilcox, S.M. (1995). National Solar 

Radiation Data Base: Final Technical Report. NREL/TP-463-5784. Golden, CO: National 

Renewable Energy Laboratory. 
84 

Mermond, A. (1996). PVSYST Version 3.2. User’s Manual. Geneva: University of Geneva, University 

Center for the Study of Energy Problems. Available online at www.pvsyst.com/. Last accessed 

December 2002. 

Perez, R., Ineichen, P.; Maxwell, E.; Seals, R.; and Zelenka, A. (1992). Dynamic Global-to-Direct 

Irradiance Conversion Models. ASHRAE Transactions. Research Series, pp. 354-369. Atlanta, 

GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. 

Perez, R.; Ineichen, P.; Moore, K.; Kmiecik, M.; Chain, C.; George, R.; and Vignola, F. (2002). A New 

Operational Model for Satellite-Derived Irradiances: Description and Validation. Solar Energy. 

Vol. 73, (5), pp. 307-317. Amsterdam: Elsevier Science. 

Pless, S.; Torcellini, P. (2004). Energy Performance Evaluation of a Low Energy Academic Building

NREL/TP 550-33180. Golden, CO: National Renewable Energy Laboratory 

Taylor, J.R. (1982). An Introduction to Error Analysis. Mill Valley, CA: University Science Books. 

Torcellini, P.A.; Griffith, B.; Ketcham, M.; and Hayter, S. (2003). Thermal Test Facility: Evaluation of 

the Energy Performance and Design Process. NREL/TP-550-34832. Golden, CO: National 

Renewable Energy Laboratory. 

USGBC. (2003). U.S. Green Building Council. Web site last accessed July 15, 2003: 

www.usgbc.org/LEED/Project/project_list.asp. 

Walton, G.N. (1983). Thermal Analysis Research Program Reference Manual. Gaithersburg, MD: 

National Bureau of Standards. (now National Institute of Standards and Technology). NBSIR 83-

2655. 
85 

Appendix A Explanation of Time Map Figure Art 

This appendix describes the map figures in this report. Figure A-1 shows a sample time map and explains 

some of its features. Because color is used quantitatively, if color figure art is reproduced in black and 

white, the data become inaccurate. For this reason, many figures contain both color and gray scale maps 

so that if the report is printed in black and white, the figures are still accurate. 

Time maps are image-based plots where individual pixels represent quantities. They provide powerful 

data visualization without the geometric interpretation problems of three-dimensional plots. The color or 

gray-scale maps provide a third dimension. Time is represented in two dimensions with time-of-day 

across the width and day-of-year along the height. The upper left corner represents 0:00 on the first day. 

The upper right corner represents 23:45 (or 23:00 if hourly) on the first day. The lower edge is the last 

day of the data set. In the figures in this report, color scales are divided into 256 levels and gray-scales 

are divided into 16 levels. For 15-minute data, there is a one-to-one correspondence between pixels and 

measured data. For hourly data, four pixels in the width direction represent a single hourly data point. 

Each pixel in the vertical direction represents a single day. Most figures in this report represent 365 days 

so that images are derived from plots that have 96 x 365 pixels. (Pixel sizes in the original plotting 

routines are sized for a computer screen and are not the same as pixel sizes in the final document 

artwork.) 
86 

Color of point in map

shows quantity at

that particular time

according to color

scale

Units

Day # 365

Day # 1

0:00 23:45

Color scale

distributed

linearly from

“Max:” value to

“Min:” value

Figure A-1 Explanation of time map figure art 
87 

Appendix B Selected Results from Detailed Monitoring 

This appendix presents a selection of detailed data from the monitoring effort. All data sets in this 

appendix are from the period November 7, 2001 through November 6, 2002. In contrast to the data sets 

used in modeling, these have not been rearranged so the top of the time map contains data for November 

7, 2001. Appendix A describes the time map figure art. 

(a) (b)

Figure B-1 Time map of total site electricity use (kWh/15-min) in Eastern 

Standard Time in (a) color and (b) gray-scale 
88 

(a) (b)

Figure B-2 Time map of net site electricity use (kWh/15-min) in Eastern 

Standard Time in (a) color and (b) gray-scale 
89 

(a) (b)

Figure B-3 Time map of PV electricity generation (kWh/15-min) in Eastern 

Standard Time in (a) color and (b) gray-scale 
90 

(a) (b)

Figure B-4 Time map of HVAC energy use for main building (kWh/15-min) in 

Eastern Standard Time in (a) color and (b) gray-scale 
91 

(a) (b)

Figure B-5 Time map of ground-loop pump energy use (kWh/15-min) in Eastern 

Standard Time in (a) color and (b) gray-scale 
92 

(a) (b)

Figure B-6 Time map of first-floor air dry-bulb temperature (°C) in Eastern 

Standard Time in (a) color and (b) gray-scale 
93 

(a) (b)

Figure B-7 Time map of first-floor air relative humidity (%) in Eastern Standard 

Time in (a) color and (b) gray-scale 
94 

(a) (b)

Figure B-8 Time map of second-floor air dry-bulb temperature (°C) in Eastern 

Standard Time in (a) color and (b) gray-scale 
95 

(a) (b)

Figure B-9 Time map of second-floor air relative humidity (%) in Eastern 

Standard Time in (a) color and (b) gray-scale 
96 

Appendix C Weather Data for EnergyPlus Modeling

Table C-1 lists summary statistics for the weather data and compares them to the nearest typical 

meteorological year (TMY2) weather file for Baltimore, Maryland. The figures in this appendix depict 

the 15-minute data contained in the weather file created for use with EnergyPlus, called CBF.epw. These 

data were obtained from measurements from November 7, 2001 to November 6, 2002. Data were 

rearranged to start on January 1 and end on December 31. Appendix A describes the time map figure art. 

Table C-1 Weather Summary Statistic: Measurement versus Baltimore TMY2 

 Units Mean Minimum Maximum Standard 

Deviation 

Measurement °F 

(°C)

59.7 

(15.3)

20.2 

(-6.7)

98.2 

(36.6)

15.9 

Outdoor (8.8)

Air Dry-Bulb 

Temperature Baltimore 

TMY2 

°F 

(°C)

54.9 

(12.6)

2.1 

(-16.7)

96.3 

(35.6)

18.6 

(10.3)

Measurement °F 

(°C)

48.9 

(9.3)

2.4 

(-16.6)

79.0 

(26.0)

17.3 

Outdoor (9.6)

Dew-Point 

Temperature Baltimore 

TMY2 

°F 

(°C)

43.1 

(6.1)

-11.7 

(-24.4)

80.3 

(26.7)

19.2 

(10.7)

Outdoor Measurement % 68.7 14.0 100.0 18.6 

Relative 

Humidity 

Baltimore 

TMY2 % 67.1 18.0 100.0 19.3 

Measurement mph 

(m/s)

6.2 

(2.8)

0.0 

(0.0)

26.5 

(11.8)

3.7 

(1.7) Wind Speed Baltimore 

TMY2 

mph 

(m/s)

9.2 

(4.1)

0.0 

(0.0)

40.3 

(18.0)

4.8 

(2.2)

Measurement degree 203 1 352 80 Wind 

Direction Baltimore 

TMY2 degree 207 0 360 96 

Measurement Btu/h·ft2

(W/m2)

23.2 

(73.3)

0.0 

(0.0)

213.6 

(673.9)

32.8 

(103.4) Diffuse Solar 

Baltimore 

TMY2 

Btu/h·ft2

(W/m2)

23.2 

(73.3)

0.0 

(0.0)

152.1 

(480.0)

31.5 

(99.5)

Measurement Btu/h·ft2

(W/m2)

57.6 

(181.8)

0.0 

(0.0)

321.1 

(1013.0)

92.8 

(292.8)

Direct Solar 

Baltimore 

TMY2 

Btu/h·ft2

(W/m2)

51.4 

(162.2)

0.0 

(0.0)

310.0 

(978.0)

84.7 

(267.2)
97 

(a) (b)

Figure C-1 Time map of outdoor air dry-bulb temperature (°C) in (a) color and 

(b) gray-scale 
98 

(a) (b)

Figure C-2 Time map of outdoor air dew-point temperature (°C) in (a) color and 

(b) gray-scale 
99 

(a) (b)

Figure C-3 Time map of outdoor air relative humidity (%) in (a) color and (b) 

gray-scale 
100 

(a) (b)

Figure C-4 Time map of horizontal infrared radiation (W/m2) in (a) color and (b) 

gray-scale 
101 

(a) (b)

Figure C-5 Time map of global horizontal solar radiation (W/m2) in (a) color and 

(b) gray-scale 
102 

(a) (b)

Figure C-6 Time map of direct normal solar radiation (W/m2) in (a) color and (b) 

gray-scale 
103 

(a) (b)

Figure C-7 Time map of diffuse horizontal solar radiation (W/m2) in (a) color 

and (b) gray-scale 

The maximum direct normal solar radiation and the maximum diffuse horizontal solar radiation shown in 

Figure C-6 and Figure C-7, respectively, appear high relative to the design data in the ASHRAE 

Applications Handbook (ASHRAE 2003b), Chapter 33, Figure 4. These values, however, are more 

acceptable when compared to weather data in the TMY2 format from other locations in the region. Table 

C-2 shows that the maximum direct normal solar radiation as measured at the Merrill Center is similar to 

other locations. The table also shows that the ASHRAE design value for maximum diffuse horizontal 

solar radiation is much lower than the weather data for this region. 
104 

Table C-2 Comparison of Maximum Solar Radiation Values 

Source or Location 

Maximum Direct Normal 

Solar Radiation 

Btu/h·ft2

(W/m2) 

Maximum Diffuse 

Horizontal Solar Radiation

Btu/h·ft2

(W/m2) 

ASHRAE Applications (ASHRAE 2003b) 300 

(945) 

40 

(126) 

Merrill Center (EnergyPlus weather file) 322 

(1,013) 

214 

(674) 

Baltimore International Airport, Maryland 

(TMY2) 

311 

(978) 

152 

(480) 

Patuxent River Naval Air Test Center, 

Maryland (TMY2) 

264 

(833) 

128 

(403) 

Washington-Dulles, Virginia (TMY2) 312 

(983) 

178 

(562) 

Wilmington, Delaware (TMY2) 314 

(988) 

163 

(513) 

Providence, Rhode Island (TMY2) 308 

(971) 

163 

(514) 

(a) (b)

Figure C-8 Time map of wind direction (clockwise degrees from North) in (a) 

color and (b) gray-scale 
105 

(a) (b)

Figure C-9 Time map of wind speed (m/s) in (a) color and (b) gray-scale 
106 

(a) (b)

Figure C-10 Time map of total and opaque sky cover (tenths of coverage) in (a) 

color and (b) gray-scale 
107 

Appendix D Calibration Schedules for EnergyPlus Modeling 

This appendix provides time map figures that show the calibration schedules created for use with 

EnergyPlus. These were derived from 15-minute measurements and totaled to create hourly schedules for 

the entire modeling/analysis year. Data were rearranged to start on January 1 and end on December 31. 

Appendix A describes the time map figure art. 

(a) (b)

Figure D-1 Time map of calibration schedule for first-floor receptacles loads, 

normalized factors in (a) color and (b) gray-scale 
108 

(a) (b)

Figure D-2 Time map of calibration schedule for first-floor lighting loads, 

normalized factors in (a) color and (b) gray-scale 
109 

(a) (b)

Figure D-3 Time map of calibration schedule for second-floor receptacle loads, 

normalized factors in (a) color and (b) gray-scale 

 
110 

(a) (b)

Figure D-4 Time map of calibration schedule for second-floor lighting loads, 

normalized factors in (a) color and (b) gray-scale 
111 

(a) (b)

Figure D-5 Time map of calibration schedule for conference pavilion receptacle 

and lighting loads, normalized factors in (a) color and (b) gray-scale 
112 

(a) (b)

Figure D-6 Time map of calibration schedule for miscellaneous loads (elevator, 

exterior lighting, mechanical room receptacles), normalized factors 

in (a) color and (b) gray-scale 
113 

(a) (b)

Figure D-7 Time map of calibration schedule for blended (first floor plus 18.4% 

of conference pavilion) first receptacle loads, normalized factors in 

(a) color and (b) gray-scale 
114 

(a) (b)

Figure D-8 Time map of calibration schedule for blended (second floor plus 

81.6% of conference pavilion) second-floor receptacle loads, 

normalized factors in (a) color and (b) gray-scale 

The measured data can also be reduced to a form more useful for energy modeling. Table D-3 shows the 

magnitude of peak plug loads normalized by area for the first and second floors of the main building. The 

detailed schedules presented in Figure D-1 and Figure D-3 were combined to assemble average plug load 

profiles presented in Figure D-9. These were averaged. 

Table D-3 Peak plug loads from measurements 

CBF Location W/ft2 W/m2

first floor, main building 1.4 14.9 

second floor, main building 1.1 11.8 
115 

0

2

4

6

8

10

12

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:01

Time of Day

Average weekday plug loads [W/m2

]

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Figure D-9 Working weekday average plug load profiles by month for the first 

and second floors of the main building. 
116 

Appendix E Cloud Cover Modeling 

Cloud cover is used to compute sky temperatures or horizontal infrared radiation intensity. EnergyPlus 

uses horizontal infrared radiation intensity to compute outside surface temperatures of surfaces exposed to 

the outside. Values for cloud cover are in tenths: 0 is clear and 10 is cloudy. Cloud cover was not 

observed directly and so was inferred from solar radiation measurements. A literature search did not 

produce a body of research on methods for directly modeling cloud cover from global horizontal solar 

measurements. (However, the reverse problem of generating radiation data from cloud cover data has 

been addressed (Maxwell et al. 1995, Perez et al. 2002).) The computer program TRNSYS (Klein et al. 

1976) allows custom modules, and Auer has written a module called Type69 that models cloud cover and 

sky temperature. Unfortunately, a literature reference for Auer’s method has not yet been determined. 

Using Auer’s code for TRNSYS Type69, routines were developed for estimating horizontal infrared 

radiation intensity with the following five steps. 

1. Combine direct horizontal, Bh, and diffuse horizontal, Dh, solar radiation to obtain global horizontal 

radiation, Gh. This value is a measured quantity since Dh is determined by subtracting the Perez 

model result for Bh from Gh. 

Gh = Bh + Dh (2) 

2. Calculate cloud cover, CC, on a scale from 0 to 1 for Gh > 0.0 with: 

0.5

0.001, 1.4286 0.3 ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡ = −

h

h

G

D CC MAX (3) 

3. Fill low-sun angle and nighttime data based on neighboring acceptable data. The approach used here 

differs from Auer’s because our processing was performed on all the time steps at once, whereas the 

TRNSYS routine was for use during a simulation where at a given time step only time history is 

available (and not the results from future time steps). For each time step, where solar zenith angle is 

greater than 65°, the calculated values for cloud cover are assumed inaccurate. For these times, data 

were artificially filled using an average of two results before and two results after the results were cut 

off because they were not reliable. The 65° cutoff angle was selected arbitrarily after separate trials 

with cutoff angles of 80°, 75°, and 70° failed to produce clear skies at night. For the beginning of the 

year, when averaging before and after was not possible, we used the data for the following morning; 

for the end of year, we used only the data from the preceding evening. 

4. Multiply by a factor of 10.0 to convert to new scale with cloud cover in tenths. 

5. Use the cloud cover data and the method documented by Walton (1983) and used in EnergyPlus to 

calculate horizontal infrared radiation intensity: 

[ ] 2 3 1 0.0224 0.0035 0.00028

273.15

0.787 0.764ln CC CC CC TdewK

sky + − + ⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ ε = + (4) 

4 GIR,h skyσTdb = ε (5) 
117 

Appendix F Baseline Energy Model Error Analysis 

This appendix presents details of the methods used to develop estimates for uncertainty in the model 

predictions for baseline energy use. 

F.1 Geometry 

This section describes the geometry of the baseline building models. Figure F-1 shows the three baseline 

buildings that were modeled. In all cases, the models have the same number of floors and a floor area of 

26,000 ft2

 (2,414 m2). The three methods of specifying geometry for baseline models are: 

• A square solar neutral footprint, referred to as a box w aspect ratio 1.0. 

• A rectangular footprint with an aspect ratio similar to the Merrill Center referred to as a box w aspect 

ratio 4.4. 

• The actual geometry of the Merrill Center referred to as same as built. 

The aspect ratio 1.0 box represents a solar neutral building. The aspect ratio 4.4 box represents a building 

footprint similar to the Merrill Center if its conference pavilion were attached to one end of the main 

building. In addition, error analysis efforts modeled perturbations in aspect ratio because there is no 

consensus for how to combine the pavilion and main building. For baseline modeling, the building is 

modeled four times with orientation rotated through the four cardinal directions and results averaged. 

The box geometries use a default rectangular geometry. Aspect ratio is used here to indicate the east-west 

length of the building divided by the north-south length. This building geometry is a simple square or 

rectangular footprint with equal floor area on each floor and windows extending as a continuous band on 

each façade with a uniform height. Box buildings bear little resemblance to the Merrill Center, but they 

are meant to represent a typical low-cost market commercial office building. The box buildings are 

developed based on the following assumptions: 

• Rectangular floor plan 

• Uniform floor-to-floor heights 

• Uniform plenum heights 

• Ten zones per floor, including four perimeter zones, one core zone, and five return plenums (in a drop 

ceiling), no windows in plenum, and equal distribution of windows (based on glazing fraction or 

window-to-wall ratio) on all facades and floors. 

Computer routines were developed to automatically generate model inputs for EnergyPlus based on the 

following parameters: 

• Total building area 

• Number of floors 

• Aspect ratio 

• Floor-to-floor height 

• Return plenum height 

• Depth of perimeter zones 
118 

• Glazing sill height 

• Glazing area fraction 

• Glazing edge offset. 

The same-as-built geometry matches the first and second floors of the Merrill Center. Figure F-1 shows 

the thermal zones and floor plan for the same as-built baseline model. The envelope materials match the 

minimum requirements of ASHRAE 90.1-2001 outlined in Table 5-11. Shading surfaces are also not 

present. 

The current study modeled all three geometries with the intent of understanding the differences in results 

and the contribution of geometry to their uncertainty. Which method of determining baseline geometry is 

the most appropriate for rating purposes? The answer to this question is still being debated. Proponents 

of modeling the same geometry as the proposed (or in this case as-built) building argue that such an 

approach is better because: 

• Site requirements dictate footprint. 

• Problems would otherwise arise with differing ratios of exposed envelope area and interior volume. 

• Having just one geometrical representation reduces the effort required to do the modeling. 

Proponents of modeling box-type geometries argue that baseline performance should be independent from 

an actual design. This approach provides credits when orientation and aspect ratio are used to improve 

building performance and conversely, penalizes building designs that make poor judgments related to 

orientation. 

 
119 

( a )

( b )

( c )

Figure F-1 Baseline building model geometries, (a) box with aspect ratio 1.0, 

(b) box with aspect ratio 4.4, and (c) same as built 
120 

This section lists results for annual energy performance for the three baseline geometries. Various 

performance indices and end uses are included to meet the interests of different readers. Section 5.3.3 

provides a more concise presentation of the results. 

Table F-1 lists the results for site energy use for three baseline geometries, Table F-2 lists the results for 

source energy, and Table F-3 lists energy costs. The methods are all considered valid. Therefore, the 

mean was calculated by giving equal weight to each result. Linear deviations were recorded and were 

considered sources of error in the analysis. The results indicate that choosing between a square footprint 

(aspect ratio of 1), a rectangular footprint (with aspect ratio similar to the Merrill Center of 4.4), and the 

actual geometry of the Merrill Center has about a 3% effect on results. The large variation in fan energy 

results from the as-built geometry, which leads to higher solar loads, and therefore, higher design airflow 

rates in sizing calculations for the constant volume fans. 

Table F-1 Baseline Energy Model Results for Site Energy Use 

Geometry Lights 

MWh/yr

Plugs 

MWh/yr

Misc. 

MWh/yr

Fans 

MWh/yr

Cooling

MWh/yr

Heating

MWh/yr

Total 

Site 

MWh/yr

Intensity 

kBtu/ft2·yr 

(kWh/m2·yr)

Aspect 

Ratio 1.0 131.1 154.1 30.6 40.7 59.7 50.3 466.5 51.3 

(162.0) 

Aspect 

Ratio 4.4 131.1 154.1 30.6 47.7 68.0 56.8 488.3 53.7 

(169.6) 

Same as 

As-Built 122.6 153.9 30.7 67.9 73.1 48.3 496.6 54.7 

(172.4) 

Mean 128.3 154.0 30.6 52.1 66.9 51.8 483.8 53.2 

(168.0) 

± Geom. 

err. est. 3.3% 0.1% 0.2% 26.1% 10.0% 8.2% 3.1% 3.1% 

Table F-2 Baseline Energy Model Results for Source Energy Use 

Geometry Lights 

MWh/yr

Plugs 

MWh/yr

Misc.

MWh/yr

Fans 

MWh/yr

Cooling

MWh/yr

Heating

MWh/yr

Total 

Source 

MWh/yr

Intensity

kBtu/ft2·yr

(MJ/m2·yr)

Aspect Ratio 

1.0 422.8 497.2 98.9 131 193 54.2 1,397 154 

(1,746) 

Aspect Ratio 

4.4 422.8 497.2 98.9 154 219 61.1 1,453 160 

(1,816) 

Same as AsBuilt 395.6 496.4 99.2 219 236 51.9 1,498 165 

(1,873) 

Mean 413.6 496.9 98.9 168 216 55.8 1,449 159 

(1,811) 

± Geom. err. 

est. 3.3% 0.1% 0.1% 26.1% 10.0% 8.2% 3.5% 3.5% 
121 

Table F-3 Baseline Energy Model Results for Energy Costs 

Geometry Lights 

$/yr

Plugs 

$/yr

Misc. 

$/yr

Fans 

$/yr

Cooling

$/yr

Heating

$/yr

Demand 

Charges 

$/yr

Total 

Site 

$/yr

Intensity

$/ft2·yr 

($/m2·yr)

Aspect 

Ratio 1.0 5,946 6,635 1,296 1,839 3,034 2,258 13,600 34,608 1.12 

(12.0) 

Aspect 

Ratio 4.4 5,946 6,635 1,296 2,153 3,451 2,551 14,405 36,437 1.18 

(12.7) 

Same as 

As-Built 5,555 6,628 1,301 3,127 3,877 2,172 17,355 40,014 1.29 

(13.9) 

Mean 5,816 6,633 1,298 2,373 3,454 2,327 15,120 37,019 1.20 

(12.87) 

± Geom. 

err. est. 3.4% 0.1% 0.2% 27.1% 12.2% 8.1% 12.4% 7.3% 7.4% 

F.2 Input Perturbation Methods for Error Analysis 

This section provides a summary of efforts to estimate the accuracy of computer modeling results based 

on sensitivity analysis of uncertainty in input. This report uses two complementary methods of modeling 

uncertainty that were first presented by Lomas and Eppel (1992): 

• Differential sensitivity analysis (DSA), which provides information on the contribution of individual 

sources of error 

• Monte Carlo analysis (MCA), which provides a better method of capturing nonsuperimposable 

interactions between individual sources of input error. 

These sensitivity analyses are used to model uncertainty and are not rigorous treatments of error 

propagation. Both methods have the advantage that the core simulation program (EnergyPlus in this case) 

is treated as a black-box function. As a result, the error analysis is separable from the program, the 

program does not need to be modified, and it is assumed valid. Systematic errors may not be captured, 

but these methods do address problems that stem from bad input. Although both methods require 

considerable computing, DSA and MCA are more practical to implement than propagating error 

throughout the numerous computations in EnergyPlus and the post-processing routines. MacDonald 

(2002) used affine polynomials to propagate error through the ESP-r program. The baseline model with 

box geometry and an aspect ratio of 4.4 was selected for most of the analysis in this section. 

The first step in both DSA and MCA analyses is to identify the input parameters to be analyzed and their 

respective uncertainty characteristics. Estimating the uncertainty in energy modeling is not common 

practice and there are no standardized methods for characterizing input errors. Therefore, we had to make 

numerous assumptions for which input parameters should be included and which level of uncertainty 

should be assigned to them. Table F-4 lists the input parameters and their uncertainties used in this study. 

We selected parameters that are not clearly specified in ASHRAE 90.1-2001 or 90.1 Appendix G, with 

the exception of lighting levels, which are specified, but were varied anyway. The selection of input 

parameters was limited because each had to be implemented in preprocessor routines used to 

automatically prepare input files. Floor area is included as a variable because there is uncertainty in the 

floor area of the actual building. Aspect ratio is included as a variable because there is uncertainty in how 

well the selection of 4.4 would represent a building that combined the conference pavilion. Because of 

limitations on the amount of effort available for programming preprocessors, only a portion of the vast 

amount of input for an EnergyPlus model has been included in such routines. In this study, the 
122 

uncertainty in a particular value is modeled as 2.33 times the standard deviation (s), which leads to a 98% 

probability that the value lies within +2.33s and –2.33s of the mean. For MCA analysis, it is also 

necessary to characterize the probability distribution function associated with a particular input value. 

Following the research of MacDonald (2002), the current study implemented uniform, normal, lognormal, and triangle probability distributions for MCA analysis. 

Table F-4 Input Parameters Included in DSA and MCA and Uncertainties 

Parameter Units 

Mean 

and/or 

Mode 

Minimum Maximum Standard 

Deviation 

Probability 

Distribution 

Floor Area [m2] 2,414.4 2,293.7 2,535.1 51.8 normal 

Aspect Ratio [ ] 4.4 3.9 4.9 0.21 normal 

Floor-to-Floor 

Height [m] 4.572 4.072 5.072 0.22 normal 

Plenum Height [m] 0.914 0.614 1.214 0.13 normal 

Perimeter 

Zone Depth [m] 4.572 3.572 5.572 0.43 normal 

Glazing Sill 

Height [m] 1.0 0.8 1.2 0.086 normal 

Glazing 

Fraction [m] 0.4 0.35 0.45 0.021 normal 

Receptacle 

Intensity 

[W/m2] 1st 

 2nd

14.91 

15.49 

14.16 

14.72 

15.66 

16.26 

0.32 

0.33 

uniform 

Lighting 

Intensity [W/m2

] 11.0 9.0 13.0 0.86 triangle 

Occupant 

Density 

[People/ 

100 m2

] 

9.0 6.0 12.0 1.28 uniform 

Infiltration 

Rate [ACH] 0.4 0.0 0.8 0.17 normal 

The next step in error analysis was to implement a framework for performing DSA and MCA analysis 

using EnergyPlus as a black-box function. A computing framework (called EpInteface7

) was developed 

to automate the following operations: 

• Use either DSA or MCA to create a series of EnergyPlus input files. 

• Execute EnergyPlus. 

• Analyze EnergyPlus results. 

• Record results from individual simulations. 

• Compute error estimates. 

 

7

 Similar to HPBAnalyzer, this application was written by Brent Griffith in an interpreted language called IDL by 

Research Systems Incorporated, Boulder, Colorado. 
123 

F.3 Differential Sensitivity Analysis 

For DSA analysis, 23 simulations were run to assess 11 input parameters (one for base case and two each 

for the input parameters). The input for each DSA model is varied one at a time. Results from DSA are 

given in Table F-5 for site energy uses, Table F-6 for source energy uses, and Table F-7 for energy costs. 

An accepted method of combining a series of n independent sources of uncertainty, i δx , is to sum them in 

quadrature as shown in Equation 6 (Taylor 1982; Lomas and Eppel 1992). 

()()() () 2 2

3

2

2

2

1 n δq = δx + δx + δx + ⋅⋅⋅ + δx (6) 

This method computes individual contributions of error in isolation and then combines them in a 

straightforward manner using Equation 6. As discussed by Lomas and Eppel (1992), quadrature addition 

requires one to assume that the modeling behaves as a roughly linear and superposable system. 

Table F-5 DSA of Uncertainty in Baseline Model Predictions: Annual Site 

Energy Use 

Parameter 

Input 

Error 

± 

Lights 

± 

MWh/yr

Plugs

± 

MWh/yr

Fans

± 

MWh/yr

Cool

± 

MWh/yr

Heat 

± 

MWh/yr

Total 

Site

± 

MWh/yr

Intensity

± kWh/m2

Intensity

± 

kBtu/ft2

Floor Area 120 m2 5.0 7.7 1.3 2.4 2.5 18.9 6.6 2.1 

Aspect 

Ratio 0.5 0.0 0.0 0.2 0.8 0.9 2.0 0.7 0.2 

Floor-toFloor 

Height 

0.5 m 0.0 0.0 4.5 4.6 6.2 15.3 5.3 1.7 

Plenum 

Height 0.3 m 0.0 0.0 1.0 1.1 0.2 2.3 0.8 0.3 

Perimeter 

Zone Depth 1.0 m 0.0 0.0 0.8 0.5 1.4 2.7 0.9 0.3 

Glazing Sill 

Height 0.2 m 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 

Glazing 

Fraction 0.05 m 0.0 0.0 2.8 3.6 3.3 9.7 3.4 1.1 

Receptacle 

Intensity 0.75 m 0.0 7.7 0.0 1.0 1.7 10.4 3.6 1.1 

Lighting 

Intensity 

2.0 

W/m2 18.3 0.0 0.2 2.5 3.7 24.7 8.6 2.7 

Occupant 

Density 

3 

People/ 

100 m2

0.0 0.0 0.6 0.2 14.8 15.4 5.4 1.7 

Infiltration 

Rate 

0.4 

ACH 0.0 0.0 2.3 1.0 10.3 11.5 4.0 1.3 

Combination in 

Quadrature 18.9 10.9 6.1 7.1 20.0 50.3 17.5 5.5 
124 

Table F-6 DSA of Uncertainty in Baseline Model Predictions: Annual Source 

Energy Use 

Parameter 

Input 

Error 

± 

Lights 

± 

GJ/yr

Plugs

± 

GJ/yr

Fans

± 

GJ/yr

Cool

± 

GJ/yr

Heat 

± GJ/yr

Total 

Source

± 

GJ/yr

Intensity

± 

MJ/m2

Intensity

± 

kBtu/ft2

Floor Area 120 m2 58.3 89.5 15.5 27.3 9.7 200.3 69.6 6.1 

Aspect 

Ratio 0.5 0.0 0.0 2.6 9.6 3.6 15.8 5.5 0.5 

Floor-toFloor 

Height 

0.5 m 0.0 0.0 52.0 53.6 24.0 129.6 45.0 4.0 

Plenum 

Height 0.3 m 0.0 0.0 11.7 13.3 0.7 25.7 8.9 0.8 

Perimeter 

Zone Depth 1.0 m 0.0 0.0 9.6 5.8 5.3 20.7 7.2 0.6 

Glazing Sill 

Height 0.2 m 0.0 0.0 0.2 0.4 0.1 0.6 0.2 0.0 

Glazing 

Fraction 0.05 m 0.0 0.0 32.2 42.2 12.8 87.2 30.3 2.7 

Receptacle 

Intensity 0.75 m 0.0 89.5 0.2 11.5 6.6 107.7 37.4 3.3 

Lighting 

Intensity 

2.0 

W/m2 212.0 0.0 2.8 29.0 14.5 258.3 89.7 7.9 

Occupant 

Density 

3 

People/ 

100 m2

0.0 0.0 6.6 2.3 57.4 66.4 23.0 2.0 

Infiltration 

Rate 

0.4 

ACH 0.0 0.0 26.3 11.3 39.7 77.4 26.9 2.4 

Combination in 

Quadrature 219.9 126.6 70.4 82.5 77.5 577.0 200.3 17.6 
125 

Table F-7 DSA of Uncertainty in Baseline Model Predictions: Annual Energy 

Cost 

Parameter 

Input 

Error 

± 

Ligh

t 

± 

$/yr

Plug 

± 

$/yr

Fans

± 

$/yr

Cool

± 

$/yr

Heat

± $/yr

Demand

± 

$/yr

Total 

Site

± 

$/yr

Intensity

± 

$/m2

Intensity

± 

$/ft2

Floor Area 120 m2 233 332 61 120 112 543 1,401 0.49 0.05 

Aspect 

Ratio 0.5 0 0 10 41 41 57 149 0.05 0.00 

Floor-toFloor 

Height 

0.5 m 0 0 199 232 279 478 1,187 0.41 0.04 

Plenum 

Height 0.3 m 0 0 46 56 8 125 219 0.08 0.01 

Perimeter 

Zone Depth 1.0 m 0 0 35 22 62 51 170 0.06 0.01 

Glazing Sill 

Height 0.2 m 0 0 1 1 1 1 3 0.00 0.00 

Glazing 

Fraction 0.05 m 0 0 121 177 149 301 748 0.26 0.02 

Receptacle 

Intensity 

0.75 

W/m2 0 332 1 46 76 202 504 0.17 0.02 

Lighting 

Intensity 

2.0 

W/m2 845 0 11 122 168 731 1,542 0.54 0.05 

Occupant 

Density 

3 

People/ 

100 m2

0 0 26 19 667 123 835 0.29 0.03 

Infiltration 

Rate 

0.4 

ACH 0 0 99 21 461 194 733 0.25 0.02 

Combination in 

Quadrature 876 469 268 349 899 1,124 2,810 0.98 0.09 

F.4 Monte Carlo Analysis 

For MCA, each input file has all the parameterized input varied according to the probability distribution 

for each input parameter. The number of separate simulations in MCA is independent of the number of 

input parameters with Lomas and Eppel (1992); adequate results can be obtained after 60 simulations. 

The full set of simulations was processed to compute mean results and standard deviations, s, for various 

metrics. Our findings supported using 60 simulations for MCA error analysis, but the results do not 

appear to be very sensitive to the selection of this number. The contributions of individual input 

parameters are combined and cannot be separated as in DSA. (MCA also provides mean results that 

warrant some attention as a possible alternative method of computing baseline performance.) Table F-8 

lists results from MCA analysis for site energy use, Table F-9 lists results for source energy use, and 

Table F-10 lists results for energy cost. 
126 

Table F-8 MCA of Uncertainty in Baseline Model Predictions: Annual Site 

Energy Use 

Lights 

MWh/yr

Plugs 

MWh/yr

Fans

MWh/yr

Cool

MWh/yr

Heat 

MWh/yr

Total 

Site

MWh/yr

Intensity

kWh/m2

Intensity

kBtu/ft2

Average 129 153 37 62 58 471 163.4 51.8 

±2.3 s 26.9 9.2 8.1 7.2 26.8 63.0 21.9 6.9 

±% 20.8 6.0 21.7 11.6 46.4 13.4 13.4 13.4 

Table F-9 MCA of Uncertainty in Baseline Model Predictions: Annual Source 

Energy Use 

Lights 

GJ/yr

Plugs 

± 

GJ/yr

Fans

± 

GJ/yr

Cool

± 

GJ/yr

Heat 

± GJ/yr

Total Source

± 

GJ/yr

Intensity

± 

MJ/m2

Intensity

± 

kBtu/ft2

Average 1,503 1,779 435 723 224 5,019 1,743 154 

±2.3 s 312 107 95 84 104 524 182 16 

±% 20.8 6.0 21.7 11.6 46.4 10.4 10.4 10.4 

Table F-10 MCA of Uncertainty in Baseline Model Predictions: Annual Energy 

Cost 

Lights 

$/yr

Plugs 

$/yr

Fans

$/yr

Cool

$/yr

Heat

$/yr

Demand

$/yr

Total 

Site

$/yr

Intensity

$/m2

Intensity

$/ft2

Average 5,870 6,597 1,691 3,150 2,594 13,551 34,802 12.1 1.12 

±2.3 s 1,243 396 356 374 1,204 1,531 4,294 1.5 0.14 

±% 21.2 6.0 21.1 11.9 46.4 11.3 12.3 12.3 2.3 

F.5 Weather Data Sensitivity 

Weather data are handled separately from the other model input data because of fundamental differences 

in how the data are entered and used in the simulation program. Weather data were measured by 

instruments situated on top of the building, so many of the usual problems with weather data in energy 

modeling are avoided. (Weather stations are often located at airports far from building sites and have 

differing microclimates.) However, measurement inaccuracies and modeling/processing of the data, 

especially sky models and cloud cover estimates, lead to uncertainties in the data. The three baseline 

geometries were repeated with both the high and low versions of the weather file in a separate differential 

sensitivity exercise. The simpler method of using high and low weather files was selected because a more 

rigorous application of DSA and/or MCA to perturb individual elements of the time series data would be 

too involved. 

The results were processed to show the errors associated with weather for different end uses. Table F-11 

shows the uncertainty in site energy, Table F-12 shows the uncertainty in source energy, and Table F-13 
127 

shows the uncertainty in energy cost associated with the upper and lower bounds on weather data. Except 

for the solar neutral baseline with aspect ratio 1.0, the models were each run at four different cardinal 

rotations and three different weather files to determine the errors listed in these tables. The results from 

the three baseline geometries were combined by taking the largest to determine a final estimate of the 

error associated with inaccuracies in the weather data. 

Table F-11 Weather Data Error Analysis for Baseline Energy Model Results for 

Site Energy 

Baseline 

Geometry 

Fans 

± MWh/yr 

Cooling 

± MWh/yr 

Heating 

± MWh/yr 

Total Site 

± MWh/y 

Intensity 

± 

kWh/ 

m2·yr 

Intensity

± 

 kBtu/ 

ft2·yr 

Aspect Ratio 1.0 0.7 16.9 17.8 4.6 1.6 0.5 

Aspect Ratio 4.4 0.8 20.3 20.9 5.4 1.9 0.6 

Same as As-Built 0 14.9 16.6 2.4 0.9 0.2 

Max 0.8 20.3 20.9 5.4 1.9 0.6 

Table F-12 Weather Data Error Analysis for Baseline Energy Model Results for 

Source Energy 

Baseline 

Geometry 

Fans 

± 

GJ/yr 

Cooling 

± 

GJ/yr 

Heating 

± 

GJ/yr 

Total 

Source 

± 

GJ/y 

Intensity 

± 

MJ/ 

m2·yr 

Intensity

± 

kBtu/ 

ft2·yr 

Aspect Ratio 1.0 49.5 265.8 61.7 248.7 86.4 7.6 

Aspect Ratio 4.4 9.8 235.6 81.0 157.4 54.7 4.8 

Same as As-Built 0 172.5 64.0 108.5 37.5 3.5 

Max 49.5 265.8 61.7 248.7 86.4 7.6 

Table F-13 Weather Data Error Analysis for Baseline Energy Model Results for 

Energy Costs 

Baseline 

Geometry 

Fans 

± $/yr 

Cooling

± 

$/yr 

Heating

± 

$/yr 

Demand

Charges

± 

$/yr 

Total Site 

± 

$/yr 

Intensity 

± 

$/m2·yr 

Intensity

± 

$/ft2·yr 

Aspect Ratio 1.0 25.0 784.0 798.5 560.0 559.5 0.2 0.02 

Aspect Ratio 4.4 32.3 944.0 940.0 685.0 703.8 0.2 0.02 

Same as As-Built 0 751.5 742.5 1082.5 1091.5 0.35 0.04 

Max 32.3 944.0 940.0 1082.5 1091.5 0.35 0.04 

F.6 Overall Uncertainty Estimate 

An overall assessment of the accuracy of baseline predictions is made by combining the separate sources 

of error. DSA and MCA are separate methods of determining the contributions of the same sources of 

input error, so these errors are not added together. Because MCA captures the interactions between 

deviations in model input parameters, it is considered more rigorous. However, were DSA to predict a 

higher combined uncertainty, it would be appropriate to use that instead of the result from MCA. 

Therefore, the methodology adopted here is to use the higher of the results from MCA and DSA to 

account for uncertainty in input data. Although MCA errors are usually higher, some of the errors in 
128 

overall source energy are higher from DSA. Errors associated with weather were also determined in a 

separate analysis and are considered independent. Finally, there is the contribution of uncertainty 

associated with the selection of building geometry (as-built or box with different aspect ratios). The mean 

of results from the three baseline geometries was determined to arrive at error estimates associated with 

the selection of baseline geometry (see Table F-11, Table F-12, and Table F-13). 

Table F-14 lists the overall error estimates for model predictions of site energy use. Table F-15 lists the 

overall error estimates for source energy use. Table F-16 lists them for energy cost. The final estimates 

are determined by adding in quadrature the results for uncertainty from (1) DSA or MCA, (2) shape of the 

baseline building, and (3) weather data. 

Table F-14 Overall Error Analysis for Baseline Energy Model Results for Site 

Energy Use 

Contribution to 

Error 

Fans 

± MWh/yr 

Cooling 

± MWh/yr 

Heating 

± MWh/yr 

Total Site 

± MWh/y 

Intensity 

± 

kWh/ 

m2·yr 

Intensity

± 

 kBtu/ 

ft2·yr 

DSA 6.1 7.1 20.0 50.3 17.5 5.5 

MCA 8.1 7.2 26.8 63.0 21.9 6.9 

Max [DSA,MCA] 8.1 7.2 26.8 63.0 21.9 6.9 

Weather 0.8 20.3 20.9 5.4 1.9 0.6 

Geometry 4.3 0.1 0.0 13.6 6.7 4.3 

Combination in 

Quadrature 9.2 21.5 34.0 64.7 23.0 8.1 

Table F-15 Overall Error Analysis for Baseline Energy Model Results for 

Source Energy Use 

Contribution to 

Error 

Fans 

± 

GJ/yr 

Cooling 

± 

GJ/yr 

Heating 

± 

GJ/yr 

Total 

Source 

± 

GJ/y 

Intensity 

± 

MJ/ 

m2·yr 

Intensity

± 

kBtu/ 

ft2·yr 

DSA 70.4 82.5 77.5 577.0 200.3 17.6 

MCA 95.0 84.0 104.0 524.0 182.0 16.0 

Max (DSA,MCA) 95.0 84.0 104.0 577.0 200.3 17.6 

Weather 49.5 265.8 61.7 248.7 86.4 7.6 

Geometry 158.0 78.0 16.5 182.5 63.5 5.5 

Combination in 

Quadrature 190.9 289.5 122.0 654.3 227.2 19.9 
129 

Table F-16 Overall Error Analysis for Baseline Energy Model Results for 

Energy Costs 

Contribution to 

Error 

Fans 

± $/yr 

Cooling

± 

$/yr 

Heating

± 

$/yr 

Demand 

Charges

± 

$/yr 

Total Site 

± 

$/yr 

Intensity 

± 

$/m2·yr 

Intensity

± 

$/ft2·yr 

DSA 268.0 349.0 899.0 1,124.0 2,810.0 0.98 0.09 

MCA 356.0 374.0 1,204.0 1,531.0 4,294.0 1.5 0.14 

Max [DSA,MCA] 356.0 374.0 1,204.0 1,531.0 4,294.0 1.5 0.14 

Weather 32.3 944.0 940.0 1,082.5 1,091.5 0.35 0.04 

Geometry 644.0 421.5 189.5 1,877.5 2,703.0 1.0 0.1 

Combination in 

Quadrature 736.6 1,099.4 1,539.2 2,653.4 5,190.0 1.8 0.2 
130 

Appendix G List of People Involved in Project 

Contracting Party: 

Geoff Oxnam 

 The Chesapeake Bay Foundation 

 Annapolis, MD

General Contractor:

 Clark Construction Group 

 Bethesda, MD 

Mechanical Engineer: 

 SmithGroup, Inc. 

 Washington, DC 

Building Control Systems: 

Siemens 

Postoccupancy Evaluation Team: 

 National Renewable Energy Laboratory 

 Contact: Paul Torcellini 

 Golden, CO 

Building Operator: 

Roger Perry 

 The Chesapeake Bay Foundation 

 Annapolis, MD 

Structural Engineer: 

Shemro Engineering, Inc. 

 Bethesda, MD 

Architects/Engineers:

 Greg Mella 

 SmithGroup, Inc. 

 Washington, DC

Landscape Architects:

 Karen Motivans 

 Shepherdstown, WV 

Civil Engineer:

 Greenman-Pedersen, Inc. 

 Laurel, MD 

LEED Coordinator: 

 Janet Harrison 

 Annapolis, MD 
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1. REPORT DATE (DD-MM-YYYY)

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2. REPORT TYPE

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3. DATES COVERED (From - To)

 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Analysis of the Energy Performance of the Chesapeake Bay 

Foundation’s Philip Merrill Environmental Center 

5c. PROGRAM ELEMENT NUMBER

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NREL/TP-550-34830 

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BEC3.1001 

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The Chesapeake Bay Foundation designed their new headquarters building to minimize its environmental impact on 

the already highly polluted Chesapeake Bay by incorporating numerous high-performance energy saving features 

into the building design. CBF then contacted NREL to perform a nonbiased energy evaluation of the building. 

Because their building attracted much attention in the sustainable design community, an unbiased evaluation was 

necessary to help designers replicate successes and identify and correct problem areas. This report focuses on 

NREL’s monitoring and analysis of the overall energy performance of the building. 

15. SUBJECT TERMS

Chesapeake Bay; Chesapeake Bay Foundation; Philip Merrill Environmental Center; high-performance buildings; 

high performance; sustainable design; energy performance evaluation; energy analysis. 

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