NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Greenbuilt Retrofit Test House

Final Report

B. Sparn, K. Hudon, L. Earle, C. Booten, and 

P. C. Tabares-Velasco

National Renewable Energy Laboratory

G. Barker and C. E. Hancock

Mountain Energy Partnership 

Technical Report

NREL/TP-5500-54009

October 2012
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Greenbuilt Retrofit Test House

Final Report

B. Sparn, K. Hudon, L. Earle, C. Booten, and 

P. C. Tabares-Velasco

National Renewable Energy Laboratory

G. Barker and C. E. Hancock

Mountain Energy Partnership

Prepared under Task No(s). WR49.3000, ARRB.0900, and 

ARRB.2500

Technical Report

NREL/TP-5500-54009

October 2012
NOTICE

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iii

Acknowledgments

This work was funded by the Sacramento Municipal Utility District (SMUD) and National 

Renewable Energy Laboratory (NREL) CRADA (WR49.3000) as well as additional American

Recovery and Reinvestment Act funds (ARRB.0900, ARRB.2500).

The authors wish to acknowledge others who contributed to this project. Several NREL 

researchers helped plan the project and install the monitoring and test equipment at the house. 

Special thanks to: Lee Magnusson (formerly of NREL), Dave Roberts, Stacey Rothgeb, Cheryn 

Metzger, and Paul Norton (formerly of NREL). This work would never have been possible 

without the support of Jim Bayless (Greenbuilt Construction) and Mike Keesee (SMUD). Their 

flexibility and assistance throughout the year made the long-distance aspect of this project 

feasible. The authors also wish to thank Dan Roberts (Mountain Energy Partnership) for 

providing local assistance in a number of tests.
iv

Contents

Acknowledgments ..................................................................................................................................... iii

List of Figures ............................................................................................................................................. v

List of Tables ............................................................................................................................................. vii

Definitions................................................................................................................................................. viii

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

2 Evaluation of Retrofit Measures ...................................................................................................... 3

2.1 Summary of Major Retrofit Measures......................................................................................... 3

2.2 Floor Plan......................................................................................................................................... 4

2.3 Retrofit Validation Tests.................................................................................................................. 6

2.4 Rooftop Photovoltaics...................................................................................................................... 6

2.4.1 Efficiency Measurements.................................................................................................. 7

2.4.2 Measured Versus Modeled Energy Production................................................................. 8

2.5 Coefficient of Performance Test for Space Conditioning Heat Pump.......................................... 10

2.6 Performance Tests for Domestic Hot Water System ..................................................................... 12

2.7 Air Distribution System................................................................................................................. 13

3 The Greenbuilt House as a Laboratory House ................................................................................ 15

3.1 Instrumentation and Controls......................................................................................................... 15

3.2 Simulated Occupancy .................................................................................................................... 16

3.2.1 Sensible Heat Gains........................................................................................................ 16

3.2.2 Lighting Schedule ........................................................................................................... 17

3.2.3 Domestic Hot Water Draws............................................................................................ 18

3.3 Control4 ......................................................................................................................................... 19

3.3.1 Role of Control4 in the Greenbuilt Laboratory House.................................................... 21

3.4 Space Conditioning........................................................................................................................ 21

3.4.1 Introduction..................................................................................................................... 21

3.4.2 Research Questions for Cooling Experiments ................................................................ 23

3.4.3 Results............................................................................................................................. 24

3.4.4 Time-of-Use Pricing Comparison................................................................................... 30

3.5 Domestic Hot Water ...................................................................................................................... 32

3.5.1 Introduction..................................................................................................................... 32

3.5.2 Test Setup........................................................................................................................ 32

3.5.3 Research Questions......................................................................................................... 34

3.5.4 Results............................................................................................................................. 35

4 Whole-House Simulation Model........................................................................................................ 48

4.1 Annual Retrofit Analysis............................................................................................................... 48

4.2 Comparison of Cooling Strategies................................................................................................. 51

4.2.1 Validation of EnergyPlus Model..................................................................................... 52

4.2.2 Whole Summer Comparisons ......................................................................................... 58

4.3 Modeling Conclusions................................................................................................................... 60

5 Conclusions ........................................................................................................................................ 61

5.1 Lessons Learned and Future Work ................................................................................................ 61

5.1.1 Retrofit Project................................................................................................................ 61

5.1.2 Laboratory House Implementation ................................................................................. 61

5.1.3 Enhancing Results Through Modeling............................................................................ 61

5.2 Summary of Results....................................................................................................................... 62

5.3 Future Work................................................................................................................................... 63

6 References .......................................................................................................................................... 65

Appendix A Summary of Retrofit Measures.................................................................................... 66

Appendix B List of Instrumentation ................................................................................................. 68

Appendix C Air Temperature Measurement Location.................................................................... 71
v

List of Figures

Figure 1. The Greenbuilt house ................................................................................................................. 1

Figure 2. Insulation was added to the west wall and in the attic ........................................................... 3

Figure 3. Three pieces of equipment that were upgraded for energy efficiency.................................. 4

Figure 4. External shading and on-site renewable energy systems are shown................................... 4

Figure 5. Pre-retrofit layout of the Greenbuilt house .............................................................................. 5

Figure 6. Post-retrofit layout of the Greenbuilt house ............................................................................ 6

Figure 7. Current-voltage characteristics for a range of solar irradiance values ................................ 8

Figure 8. Temporal offset between collector-plane insolation and AC output, and discrepancy in 

relative magnitude of horizontal and collector-plane insolation................................................... 10

Figure 9. Flow hood test in progress ...................................................................................................... 13

Figure 10. Imposed sensible load schedule, by room, to account for occupants ............................. 16

Figure 11. Imposed sensible load schedule, by room, to account for appliances............................. 17

Figure 12. Aggregate lighting schedule for the whole-house, displayed as number of light bulbhours.................................................................................................................................................... 18

Figure 13. Hourly benchmark DHW draw profile ................................................................................... 19

Figure 14. Benchmark hot water draw profile broken into smaller discrete draws ........................... 19

Figure 15. Morning-dominated hot water draw profile......................................................................... 19

Figure 16. Evening-dominated hot water draw profile ......................................................................... 19

Figure 17. Control4 lighting control interface through the television ................................................. 20

Figure 18. Screen shot of lighting schedule from Composer program............................................... 20

Figure 19. Home, Away, and Green switch buttons in the entryway................................................... 20

Figure 20. Control4 thermostat can be controlled manually or remotely ........................................... 20

Figure 21. Energy use comparison for the baseline HVAC case during the summer and winter.... 22

Figure 22. HVAC energy use for the baseline case and the simple precooling schedule ................ 25

Figure 23. Weather comparison for baseline case and simple precooling days ............................... 25

Figure 24. HVAC energy use for baseline case and advanced precooling schedule ........................ 26

Figure 25. Weather comparison for baseline case and advanced precooling days .......................... 26

Figure 26. HVAC Energy use for the simple precooling and advanced precooling schedules........ 27

Figure 27. Weather comparison for simple precooling and advanced precooling days................... 27

Figure 28. HVAC energy use comparison between baseline case and advanced precooling (with 

shades) during a heat storm ............................................................................................................. 28

Figure 29. Weather comparison for baseline case and advanced precooling (with shades) during

a heat storm ........................................................................................................................................ 28

Figure 30. HVAC energy use for the baseline case and shade control tests ..................................... 29

Figure 31. Weather comparison for baseline case and shade control tests ...................................... 29

Figure 32. HVAC energy use for the baseline case and whole-house fan tests................................. 30

Figure 33. Weather comparison for baseline case and whole-house tan test.................................... 30

Figure 34. CopperHeart 40-gal ICS.......................................................................................................... 32

Figure 35. AirTap add-on HPWH.............................................................................................................. 32

Figure 36. Schematic showing the DHW system. This includes an ICS solar panel, an add-on 

HPWH, and a storage tank................................................................................................................. 33

Figure 37. Water draw configuration in laundry closet......................................................................... 34

Figure 38. Thermocouple to measure end-use temperature of hot water........................................... 34

Figure 39. Solar irradiance measurements for the months of February and August are compared

to measurements taken during the hourly benchmark test........................................................... 36

Figure 40. Thermal energy of the ICS and HPWH during the summer hourly benchmark test ....... 37

Figure 41. Thermal energy of the ICS and HPWH during the winter hourly benchmark test........... 37

Figure 42. Electric energy use during the summer benchmark test ................................................... 38

Figure 43. Electric energy use during the winter benchmark test....................................................... 38

Figure 44. Calculated COP values for all draw profiles ........................................................................ 43

Figure 45. Correlation between COP and volume of water drawn for HPWH operating alone ......... 44

Figure 46. Correlation between COP and average daily air temperature in the garage for HPWH 

operating alone................................................................................................................................... 44

Figure 47. Annual estimated COP values for all draw profiles............................................................. 45
vi

Figure 48. Air temperature variations when HPWH is operating ......................................................... 46

Figure 49. End-use water temperature—discrete benchmark profile.................................................. 47

Figure 50. Greenbuilt house model in BEopt......................................................................................... 48

Figure 51. BEopt results for annual site energy use, before and after the retrofit ............................ 50

Figure 53. Baseline HVAC energy and living room temperature comparison between data from the 

Greenbuilt house and the EnergyPlus model.................................................................................. 54

Figure 54. Simple Precooling HVAC energy and living room temperature comparison between data 

from the Greenbuilt house and the EnergyPlus model .................................................................. 55

Figure 55. Advanced precooling HVAC energy and living room temperature comparison between 

data from the Greenbuilt house and the EnergyPlus model.......................................................... 56

Figure 56. Advanced precooling + shade control HVAC energy and living room temperature 

comparison between data from the Greenbuilt house and the EnergyPlus model..................... 57

Figure 57. Shade control HVAC energy and living room temperature comparison between data 

from the Greenbuilt house and the EnergyPlus model .................................................................. 58

Figure 59. Thermocouples for interior temperature measurements ................................................... 71

Figure 60. Temperature differences between wall-mounted thermocouple and center of the room 

thermocouple during the advanced precooling schedule. The heat pump energy is shown to 

indicate when cooling is actively occurring.................................................................................... 72
vii

List of Tables

Table 1. Manufacturer’s Specifications for Suntech Model STP 175S-24/AD PV Panels .................... 7

Table 2. PV Installation Configuration at the Greenbuilt House ............................................................ 7

Table 3. Summary of PV Performance Prediction Versus Measurement.............................................. 9

Table 4. Measured COP and Manufacturer Ratings .............................................................................. 11

Table 5. ICS Thermal Performance Ratings Versus Measurements.................................................... 12

Table 6. HPWH Rated EF Versus Measured Performance.................................................................... 12

Table 7. Room-by-Room Register Air Flow Measurements.................................................................. 14

Table 8. Total Daily and Peak HVAC Energy Consumption for Baseline HVAC in Summer and 

Winter for Typical and Extreme Weather Days................................................................................ 23

Table 9. Total Daily HVAC Energy Consumption for Baseline and Basic Precooling ....................... 25

Table 10. Total Daily HVAC Energy Consumption for the Baseline and Advanced Precooling 

Cases ................................................................................................................................................... 26

Table 11. Total Daily HVAC Energy Consumption for Simple Precooling and Advanced 

Precooling ........................................................................................................................................... 27

Table 12. Total Daily Energy Consumption for Baseline Case and Heat Storm Precooling (With 

Shades)................................................................................................................................................ 28

Table 13. Total Daily HVAC Energy Consumption for Baseline Case and Shade Control Tests...... 29

Table 14. Total Daily HVAC Energy Consumption for Baseline Case and Whole-House Fan 

Tests .................................................................................................................................................... 30

Table 15. Time-of-Use Pricing Comparison for Various HVAC Schedules......................................... 31

Table 16. Summary of DHW Daily Thermal Energy Generation Versus Requirement for Hourly 

Benchmark .......................................................................................................................................... 37

Table 17. Daily Energy Use for Summer Hourly Benchmark................................................................ 39

Table 18. Daily Energy Use for Winter Hourly Benchmark................................................................... 39

Table 19. System COP for Summer Hourly Benchmark........................................................................ 40

Table 20. System COP for Winter Hourly Benchmark........................................................................... 40

Table 21. Effect of ICS on HPWH Performance During the Summer................................................... 40

Table 22. Effect of ICS on HPWH Performance During the Winter ...................................................... 41

Table 23. Annual Energy Savings Compared to an Electric Resistance Water Heater..................... 41

Table 24. Simple Payback Period Relative to an Electric Resistance Water Heater.......................... 42

Table 25. Summary of COP Values for All Draw Profiles...................................................................... 42

Table 26. Annual Estimated COP Values and Energy Savings Compared to an Electric Resistance 

Water Heater for All Draw Profiles.................................................................................................... 44

Table 27 Additional Model Inputs............................................................................................................ 49

Table 28. Single Day Comparison of Baseline HVAC Energy Consumption for Data From the 

Greenbuilt House and the EnergyPlus Simulation ......................................................................... 54

Table 29. Single Day Comparison of Simple Precooling HVAC Energy Consumption for Data From 

the Greenbuilt House and the EnergyPlus Simulation................................................................... 55

Table 30. Single Day Comparison of Advanced Precooling HVAC Energy Consumption for Data 

From the Greenbuilt House and the EnergyPlus Simulation......................................................... 56

Table 31. Single Day Comparison of Advanced Precooling + Shade Control HVAC Energy 

Consumption for Data From the Greenbuilt House and the EnergyPlus Simulation.................. 57

Table 32. Single Day Comparison of Shade Control HVAC Energy Consumption for Data From the 

Greenbuilt House and the EnergyPlus Simulation ......................................................................... 58

Table 33. June Through September Total Cooling Energy Comparison Using EnergyPlus Model 

for Five Cooling Strategies................................................................................................................ 59

Table 34. On Peak Cooling Energy Comparison During August Heat Storm Using EnergyPlus 

Model for Five Cooling Strategies .................................................................................................... 59

Table 35. List of Retrofit Measures at the Greenbuilt House and the Systems That Were Replaced66

Table 36. List of Data Logger Equipment and Accessories Used at the Greenbuilt House.............. 68

Table 37. List of Sensors and Controls Equipment Used at Greenbuilt ............................................. 69

Table 38. List of Control4 equipment at the Greenbuilt house ............................................................ 70
viii

Definitions

AC Alternating current

BEopt Building Energy Optimization program

Btu British thermal unit

CFL Compact fluorescent lamp

CFM Cubic feet per minute

COP Coefficient of performance

DAQ Data acquisition system

DHW Domestic hot water

DOE U.S. Department of Energy

EERD Energy efficient remodel demonstration

EF Energy factor

gpm Gallons per minute

HERS Home Energy Rating System

HPWH Heat pump water heater

HVAC Heating, ventilation, and air conditioning

ICS Integrated collector storage

kW Kilowatt

kWh Kilowatt-hour

MEL Miscellaneous electric load

NREL National Renewable Energy Laboratory

Pa Pascals

PV Photovoltaic

RH Relative humidity

SMUD Sacramento Municipal Utility District

STC Standard test conditions

SWH Solar water heater, solar water heating

TMY Typical Meteorological Year

TOU Time of use
1

1 Introduction

The Greenbuilt house is an all-electric, 1980s era home in the eastern Sacramento suburb of Fair 

Oaks (see Figure 1). It was retrofitted by Greenbuilt Construction as part of the Sacramento 

Municipal Utility District’s (SMUD) Energy Efficient Remodel Demonstration (EERD) 

program. The project was a joint effort between the design-build team at Greenbuilt 

Construction, led by Jim Bayless, Mike Keesee of SMUD, and the National Renewable Energy 

Laboratory (NREL). The EERD program works with local builders to renovate homes with costeffective energy-efficiency retrofit measures. The homes remodeled under this program are 

intended to showcase energy-efficient retrofit options for homeowners and other builders. The 

Greenbuilt house is one of five EERD projects that NREL has supported. NREL mainly provided

energy analysis and monitored post-retrofit performance to verify that the energy consumption is 

in line with the modeling predictions. NREL also performed detailed monitoring on the more 

innovative equipment included in these remodels, such as an add-on heat pump water heater

(HPWH).

Figure 1. The Greenbuilt house

NREL/PIX 19611, Credit: Lieko Earle

The Greenbuilt house, like all the other EERD projects, was subject to an extensive remodel to 

improve overall energy efficiency. The installed systems include common retrofit measures, such 

as increasing wall and attic insulation, and more advanced technologies, such as motorized 

exterior shading and a home control system. A photovoltaic (PV) array and a solar water heating 

(SWH) system were also installed to maximize energy savings. This home was intended to 

showcase the best in energy efficiency retrofit technologies so builders can use some of the 

features in future projects. SMUD rented the home from Greenbuilt Construction for one year to 

demonstrate the retrofit measures to builders and homeowners and to conduct extensive tests on 

the performance of the house as a whole and on some individual retrofit measures. NREL and 

SMUD also performed tests on the home’s heating, ventilation, and air-conditioning (HVAC)

and water heating systems to determine the best control strategies for the local climate.

NREL’s role at the Greenbuilt house began after construction was complete and consisted of two 

main goals:
2

• Characterize the advanced systems installed during the retrofit to ensure that all were 

performing as intended. This task also involved verifying that the expected energy 

savings were achieved.

• Use the advanced control systems to simulate occupancy and run experiments on the 

cooling and water heating systems.

As with other EERD projects, NREL ran a number of short-term tests on major systems, 

including the PV system, the add-on HPWH, the space conditioning heat pump, and the air 

distribution system. All these systems were either newly installed or improved during the retrofit 

and NREL’s tests were designed to verify their installed performance. Whole-house performance 

was determined by measuring whole-house power consumption for the year SMUD leased the 

home. A computer model of the house was also created in NREL’s building energy optimization 

software, BEoptTM, and was used to determine the expected energy savings. Based on this model, 

the annual energy consumption is expected to be 9400 kWh, almost 60% lower than the preretrofit annual consumption of approximately 22000 kWh.

In addition to the tests used to gauge performance, extensive monitoring and control equipment 

was installed to create a laboratory house. Some systems, including all the lights and the

thermostat, could be controlled remotely using the Control4 home control system that was 

installed as part of the retrofit. NREL also added controls that allowed hot water draws and small 

space heaters to be controlled remotely. These systems were used to simulate occupancy. Once a 

simulated occupancy schedule was in place, a variety of tests were performed. The cooling 

system tests focused on finding ways to reduce the daily cooling load and shift the peak cooling 

load. Tests were also performed on the add-on HPWH and integrated collector storage (ICS) 

SWH to measure efficiency, which varies according to use patterns and weather conditions. The 

ability to run these tests in an unoccupied home with an imposed simulated occupancy removes 

the uncertainty associated with occupants, making it easier to compare results from various tests. 

It also enabled researchers to investigate some cooling strategies that may have introduced 

uncomfortable indoor conditions without affecting occupant comfort.

The team learned a great deal about cooling strategies and HPWH efficiency during this year; the

conclusions and recommendations for future work are presented in this report. This opportunity 

also provided experience with simulated occupancy and other aspects of working in a laboratory

house. Some interesting research was done at the Greenbuilt house and valuable lessons were

learned, making this a very fruitful experience for the entire team.
3 

2 Evaluation of Retrofit Measures

2.1 Summary of Major Retrofit Measures

The Greenbuilt house underwent a total energy efficiency makeover as a part of its retrofit. The 

goal was to reduce energy consumption by 50%, which required all systems to be evaluated. 

Greenbuilt Construction and SMUD worked together to determine the most cost-effective means 

of achieving this goal. In addition to the aesthetic makeover of the interior and exterior, all 

appliances, the HVAC system, and the envelope were upgraded with an emphasis on energy 

efficiency.

Even though the house is relatively modern (built in 1983), it was very leaky and needed 

additional insulation throughout. The roof was replaced, a radiant barrier was added, and 

additional insulation was installed in the attic. All walls were air sealed, but the cost of complete 

insulation was prohibitive, so extra insulation was added to the west wall only (see Figure 2). 

 

Figure 2. Insulation was added to the west wall and in the attic

All the windows were replaced with double-pane, low-emittance, argon-filled windows. A new, 

higher efficiency heat pump replaced the earlier model and a whole-house fan was installed in 

the attic. All appliances were upgraded to ENERGY STAR® and an HPWH add-on unit was 

installed on top of an older standard electric water heater (see Figure 3). 

(a) Dense pack blown-in

cellulose being installed in

the west wall. 

Credit: Judy Lew-Loose, 

SMUD. 

(b) Blown-in cellulose in the attic covered fiberglass batts. 

NREL/PIX 19612, Credit: Lieko Earle
4

Figure 3. Three pieces of equipment that were upgraded for energy efficiency

All lights were replaced with compact fluorescent lamps (CFLs) or light-emitting diodes. 

Mechanical shades and an awning were installed to shade all west-facing windows. An ISC

SWH and 2.3 kW of PV were installed on the roof (see Figure 4).

Figure 4. External shading and on-site renewable energy systems are shown

Appendix A provides a detailed list of all the retrofit measures. These measures reduced the 

Home Energy Rating Index by 57% (a lower index indicates a lower overall energy use). Before 

the retrofit, the home was assessed by a certified Home Energy Rating System (HERS) rater and 

given an index of 181. This is well above the average index of 100 for a new home and 130 for a 

resale home. After the retrofit, the home’s HERS Index decreased to 78, which indicated a 

dramatic improvement in energy use.

2.2 Floor Plan

The Greenbuilt home has three bedrooms, two bathrooms, a den or office space, a two-car 

garage, and a large, open living room, dining room, and kitchen area. There is an attic but no 

basement or crawlspace; the house is built on a slab-on-grade foundation. The air handler and all 

the air ducts are located in the unconditioned attic. The floor plan remained mostly consistent 

(a) Whole-house fan in the attic 

NREL/PIX 19613, 

Credit: Lieko Earle

(b) High efficiency SEER 16, HSPF 9.75 

heat pump (condenser unit) 

Credit: Judy Lew-Loose, SMUD

(c) Add-on HPWH

NREL/PIX 19614, 

Credit: Lieko Earle

(a) The motorized exterior shading and awning 

on the west side of the house. 

Credit: Judy Lew-Loose, SMUD

(b) The 2.3 kW PV array and the ICS SWH. Also shown 

is the weather station installed by NREL. NREL/PIX 

19615, Credit: Lieko Earle
5

through the remodel (see Figures 5 and 6). Some walls were removed between the kitchen and 

the dining room/living room area and walls were added to the den to create a more defined space. 

Otherwise, most improvements were made to the enclosure, appliances, and systems.

Figure 5. Pre-retrofit layout of the Greenbuilt house
6

Figure 6. Post-retrofit layout of the Greenbuilt house

2.3 Retrofit Validation Tests

NREL researchers conducted validation tests to ensure the major retrofit measures were 

performing as expected. Installing insulation was a major part of the retrofit, but an infrared

camera was not available to verify that the wall insulation was installed correctly and 

completely. The attic insulation was visually inspected. A test was conducted during the winter 

on the space conditioning heat pump to calculate its approximate coefficient of performance 

(COP) in heating mode, which was compared to the manufacturer’s rating at the test conditions. 

The PV system was monitored to compare actual power output to the expected output based on 

the actual solar irradiance and the temperature of the panels. A test of airflow distribution was 

also conducted. The performance of the HPWH and the ICS SWH was tested over the course of 

several months. These tests and their results are described in more detail in the following 

sections.

2.4 Rooftop Photovoltaics

The Greenbuilt house is equipped with Suntech’s model STP 175S-24/AD-1 rooftop PV to 

provide onsite solar energy (see Figures 4a, 4b). The manufacturer’s technical specifications for 

this product and the installation configuration are summarized in Tables 1 and 2, respectively. 

The unit is grid tied and has no local storage capabilities.
7 

Table 1. Manufacturer’s Specifications for Suntech Model STP 175S-24/AD PV Panels

Maximum power at STC* (Pmax) 175 W

Open-circuit voltage (voc ) 44.7 V

Optimum operating voltage (vmp) 35.2 V

Short-circuit current (isc) 5.20 A

Optimum operating current (imp) 4.95 A

Solar cell Monocrystalline silicon 125 mm × 125 mm

Table 2. PV Installation Configuration at the Greenbuilt House

Number of modules 16

Number of modules per string 8 

Number of parallel strings 2 

Array azimuth 90° (due west)

Array tilt 27°

2.4.1 Efficiency Measurements

Short-term testing was conducted on February 17, 2010 using the procedure outlined in (Barker 

& Norton, 2003). The objectives were to ensure proper operation of the system components and 

to characterize installed performance so a reasonable prediction of annual electricity delivered to 

the home could be calculated.

The following measurements were required to compare the power output of the array with the 

theoretical value: 

• The current delivered by the array (measured with a precision shunt resistor)

• The incident global solar radiation (measured using a thermopile-type pyranometer) 

• The PV cell temperature.

The cell temperature was approximated to be equal to the back-of-module temperature, which 

was measured using a surface-mounted temperature sensor fixed to the back of one module. A 

total of 86 I-V traces were made, one every 5 minutes, between 9:50 a.m. and 5:00 p.m. The 

resulting I-V curves for are shown in Figure 7 for a range of solar irradiance values. Efficiency at

standard test conditions (STC) (Ic =1000 W/m2 , Tc = 77°F [25°C]) is expected to be 13.6% from 

manufacturer’s data, which closely matches the 13.7% predicted efficiency based on measured 

data extrapolated to STC.

* Standard test conditions (STC) are defined as solar irradiance 1000 W/m2, module temperature 77°F (25°C).
8

Figure 7. Current-voltage characteristics for a range of solar irradiance values

2.4.2 Measured Versus Modeled Energy Production

It is useful to compare the theoretical and measured power output of the array under test 

conditions. Hourly TRN-SYS simulations were driven using measured irradiance and module 

temperature for the period March 21 through September 19, 2010 and compared to the measured 

alternating current (AC) power output during the same period. The results are summarized in 

Table 3. The following assumptions were used in the modeling:

• The inverter tracks the maximum power point perfectly.

• The inverter efficiency curve is equal to that of a SunPower SPR 3300x-240 rated at 

4 kW.

• In simulating the array using manufacturer’s data, incidence angle modifier and air mass 

modifier are 1.0. (The manufacturer does not supply these data.)
9

Table 3. Summary of PV Performance Prediction Versus Measurement

Over the entire period, the calibrated model overpredicted the measured output by roughly 3%;

the actual measured output was 2293 kWh. The percent discrepancy between monthly measured 

and predicted output was –1% to +9%.

These results give us reasonable confidence that our model can be used to predict the array’s 

performance under a standard set of conditions using Typical Meteorological Year (TMY) data. 

It is possible to derive a correlation for the module temperature (which affects AC efficiency) as 

a function of outdoor conditions because we have measured data for solar radiation, ambient 

temperature, wind speed, and humidity, as well as module temperatures at several locations on 

the array. A good prediction of delivered energy under TMY conditions can then be made using 

this correlation.

The solar radiation measurements had two unexpected features (see Figure 8):

• The measured output from the inverter has a similar shape over time as the measured 

collector-plane insolation, but the AC power curve tends to peak about 40 minutes earlier 

than the measured insolation. When accepted solar geometry algorithms are used to 

calculate the expected global solar radiation in the collector plane, the shape and peak 

coincide with the measured AC power output from the inverter. This implies that the PV 

array was indeed oriented at tilt = 27°, azimuth = 90°, but the pyranometer’s output was 

somehow skewed by some combination of installation error and spectral sensitivity error. 

Figure 8 shows the time offset between the measured and modeled AC power output. The 

model follows the measured collector-plane insolation as expected (peaks at the same 

time).

• For several days (April 7 and April 18 through April 25) the magnitude of the measured 

collector-plane insolation was larger than expected relative to the insolation measured in 

the horizontal plane. For these days the model overestimated the inverter’s output. Figure 

8 illustrates this feature by comparing April 7 and April 8: The measured horizontal 

insolation profile is almost identical between the two days, but the measured collectorplane insolation for the first day is notably higher at its peak than that for the second day. 

Dates

(in 2010)

Solar Radiation

(kJ/m2

)

AC Delivery From Inverter

(kWh) Measurement/

Model

Measurement Model Model Based on

Manufacturer Data

March 21–31 177217 109.2 118.7 123.2 .9200

April 1–30 503878 317.5 336.1 347.2 .9447

May 1–31 627334 386.4 411.3 423.5 .9395

June 1–30 732690 436.4 465.3 472.6 .9379

July 1–31 765907 444.4 445.7 446.8 .9971

August 1–31 674974 397.9 393.0 396.4 1.012

September 1–19 332647 201.1 194.3 198.3 1.035

Total 3814647 2292.9 2364.4 2408.1 .96976
10

We expect the horizontal peak to be higher than the collector-plane peak, and we are 

unable to explain this discrepancy.

Figure 8. Temporal offset between collector-plane insolation and AC output, and discrepancy in 

relative magnitude of horizontal and collector-plane insolation

2.5 Coefficient of Performance Test for Space Conditioning 

Heat Pump

The COP for a heat pump in heating mode is defined to be the ratio of thermal energy (in the 

form of heat) supplied to the space divided by the electrical energy consumed by the heat pump. 

Although measuring the electrical energy consumed by the heat pump is straightforward (current 

transformers are clamped to the dedicated breaker for the heat pump), measuring the heat 

supplied by the heat pump requires airflow rate and temperature to be measured at the outlet of 

the heat pump, which are difficult measurements. A simplified method for calculating the COP 

of a space conditioning heat pump involves comparing the energy needed to heat the house with 

electric resistance heaters and the energy needed to heat the house using the heat pump. Electric 

resistance heaters have a COP of 1 (all the input energy is converted to heat), so comparing the 

two cases will give an approximate COP value for the heat pump. This will estimate COP in 

heating mode only, for the specific conditions of the test, but this is a valuable exercise, as it 

helps to verify the expected performance of the heat pump. The coheating test also provides a 

measure for how the performance of the heat pump is affected in a real installation.

This test included two phases: the coheating phase and the heat pump heating phase. Coheating 

refers to the period when electric resistance heaters were used to maintain an internal 
11

temperature of 68°F (20°C) at all times. Five small space heaters (used later to simulate

occupancy) were located around the house and provided the required resistance heating. After 

about a week of the coheating test, the heat pump and air handler were used to maintain the 

house at the same set point temperature. Both tests were performed in late January and early 

February.

Data from the two tests were examined to find periods with similar weather. Three periods with 

similar outdoor temperatures, all during the night, were chosen for comparison. The duration of 

each period was 10 hours, starting at 10:00 p.m. The energy consumed by each heating source 

was determined by looking at the energy required for the entire house and subtracting the 

submetered loads: the HPWH and the air handler. Some other small loads, such as the 

refrigerator and a handful of small electronics, contributed to the whole-house energy 

consumption. These were not submetered but also contributed to heating the house.

The heat pump is made by Goodman Manufacturing, model SSZ160361AC. The manufacturer 

provides performance data over a variety of heating and cooling conditions. For heating mode, 

the indoor temperature is 70°F (21°C) (rather than our set point of 68°F [20°C]). The COP is 

given across a wide range of outdoor temperatures of –10°F to 65°F (12°C to 18°C) in 5°F

(15°C) increments. The COP value was extrapolated from the manufacturer’s data, based on the 

actual outdoor temperature averaged over the night, and that value was compared to the 

calculated COP value from the coheating tests (see Table 4). The time periods used for 

comparison are referred to as Nights 1, 2, and 3; however, each includes data from two nights: 

one night when the heat pump was used to heat the home and another night with a similar 

overnight temperature when the space heaters maintained the indoor temperature.

Table 4. Measured COP and Manufacturer Ratings

Night 1 Night 2 Night 3

Outdoor temperature 48°F 8.9°C 41°F 5.0°C 45.7°F 7.6°C

Space heater energy (kWh) 20.5 25.4 21.9

Heat pump energy (kWh) 7.9 10.6 8.2

Calculated COP 2.59 2.41 2.66

Rated COP 4.02 3.72 3.92

% difference 35.6 35.1 32.2

The COP results vary between the three comparison periods, because the outdoor temperature

varies. The measured COP is about 35% lower than the manufacturer’s rating, which indicates 

some losses in the system, such as duct losses, that consistently affect the installed performance 

of the heat pump. The results for calculated COP are only approximate. The periods used for 

comparison were not exact matches and the energy consumed in both heating cases may include 

other heat sources. An installed performance below the rating provided by the manufacturer was

expected,so a 35% difference between rated and actual performance was reasonable. So, this test 

confirms that the heat pump operated approximately as expected and system inefficiencies (also 

expected) reduced the overall performance.
12

2.6 Performance Tests for Domestic Hot Water System

The domestic hot water (DHW) system consists of a CopperHeart ICS solar panel, model CP-40, 

and an AirTap HPWH add-on unit, model A7. A specific test was not performed to characterize 

the performance of the ICS; however, its thermal performance was calculated across several days 

of testing and compared to the thermal performance ratings defined by the manufacturer. The 

thermal performance calculations will be discussed in more detail in Section 3.4. These ratings 

are specified on clear, mildly cloudy, and cloudy days, when the incident solar irradiance is 2000 

Btu/ft2/day, 1500 Btu/ft2/day, and 1000 Btu/ft2/day, respectively. Data were selected from days 

with similar solar irradiance to make the comparison to the rated values; the measured values 

differed from the rated solar irradiance by up to 6%.

Table 5 shows the manufacturer’s quoted performance numbers compared to the measured 

performance. These numbers imply a performance below the manufacturer’s ratings, because

thermal energy was calculated based on the temperatures in the pipes entering and exiting the 

ICS, and not on the temperature of the ICS itself. Therefore, the thermal energy is calculated 

based only on the amount of hot water used during draws. A direct measurement of the ICS 

would have given more accurate results, which would likely have been closer to the 

manufacturer’s ratings. Regardless, this comparison shows that the ICS worked effectively as a 

preheater for the HPWH.

Table 5. ICS Thermal Performance Ratings Versus Measurements

Rating 

Condition*

Rated Thermal

Performance

(kWh/Day/Unit)

Measured Thermal

Performance

(kWh/Day/Unit)

Performance

Shortfall

(%)

Clear 10.50 6.07 42.1

Mildly cloudy 7.25 5.90 18.7

Cloudy 5.42 2.84 47.7 * The solar irradiance on the days used to calculate measured thermal performance is within 6% of the solar irradiance implied by the rated 

conditions.

An energy factor (EF) test was performed in February 2010 to validate the manufacturer’s rating 

for the HPWH, which is quoted as 2.11. The EF value is a standard rating for water heaters and 

is determined by following the U.S. Department of Energy’s (DOE) test specifications found in 

10 CFR Part 430, Subpart B, Appendix E (DOE, 1998). The results are shown in Table 6. The 

EF determined during testing was 2.10, which shows good agreement with the manufacturer’s 

rating. Table 6 also shows the average daily COP for summer and winter for the hourly 

benchmark test, which will be explained in more detail in Section 3.4. The EF test shows that the 

performance of this unit meets the manufacturer’s specifications. The similarity of the results 

between the EF test and the draw profile test indicates that performance seen in the official EF 

rating test translates to more typical operation.

Table 6. HPWH Rated EF Versus Measured Performance

Manufacturer

Rated EF Measured EF Winter AverageDaily COP

Summer Average

Daily COP

2.11 2.10 1.95 2.13
13

This test was conducted in the garage, which was not conditioned per the DOE test 

specifications. The mains water temperature averaged 55.8°F (13.2°C) (56°F [13.3°C] was the 

minimum DOE test requirement), the average air temperature in the garage was 59.2°F (15.1°C)

(65°F [19.1°C] is the minimum DOE test requirement), the relative humidity (RH) averaged 84% 

(the DOE test requirement is 50% RH), and the set point temperature of the tank was 142.5°F

(61.4°C) (140°F [60°C] is the maximum DOE test requirement). In addition to these differences, 

the data were not collected in 5-second increments, as specified by the test procedure document.

The length of the standby part of the test was reduced from 18 hours to 14 hours, and the average 

tank temperatures were not recorded so outlet temperature was used instead. However, this 

approximation of the standard DOE 24-hour test allowed for a comparison to the rated 

performance, which proved to be in line with expectations.

2.7 Air Distribution System

A flow hood test was conducted on all the air registers as a part of the retrofit validation tests

(see Figure 9). This test captured the volumetric flow of air coming from the register and verified

that all the rooms were receiving a balanced amount of air. The test was done with an unassisted 

flow hood (no fan assistance to ensure zero pressure drop from the register), because the 

objective was to verify relative balance between the rooms. If the test had been aimed at 

measuring duct losses, for instance, a more accurate fan-assisted flow hood would have been 

used. (Volumetric airflow per room should roughly be proportional to the area of that room, but 

rooms with a higher thermal load should receive more airflow.) The rooms on the west side of 

the house—the kitchen, dining room, living room, and master bedroom—should have received a 

bit more air than their relative areas indicate. A flow hood test was initially performed in 

September 2009 and was repeated in January 2010 because some problems with the duct system 

were identified during the first round of tests (see Table 7). The room area is also included in the 

table for reference.

Figure 9. Flow hood test in progress 

NREL/PIX 19616, Credit: Lieko Earle
14

Table 7. Room-by-Room Register Air Flow Measurements

Duct Location Room Area

(ft2

)

Cooling Mode

Test 1 

(CFM)

Cooling Mode

Test 2 

(CFM)

Area-Based

Airflow 

(CFM)

Kitchen 242 85 80 86

Dining room 89 80 86

Living room (1) 487 195 185 173

Living room (2) 265 230 173

Den 195 88 82 138

Master bedroom (1) 240 30 105 85

Master bedroom (2) 0 126 85

Master bath 84 0 22 30

Master shower 18 34 30

Main bath 48 26 22 34

Bedroom 2 209 142 131 148

Bedroom 3 209 140 129 148

Total 1732 1078 1227 1227

Some results from the first flow hood test stand out immediately (and are highlighted in red in 

Table 7). Namely, the master bedroom was receiving almost no airflow, even though it contained 

two registers, and the master bath was receiving no air. Badly twisted ducts in the attic were 

unable to deliver any air to the intended rooms. After these ducts were straightened, the balance 

between the rooms improved considerably.

Based on the total airflow measured in the second flow hood test and the total area of the house, 

the airflow proportional to room area was determined and applied to the actual area of each 

room. The result of that calculation is found in the last column of Table 7. The room-by-room 

results from the second test do not differ significantly from the area-based calculation for airflow 

per room. The master bedroom and living room received more air than would be prescribed 

based on area alone, but those rooms are on the west side of the house and have a higher thermal 

load because of solar heat gain. The kitchen should probably also have received more air than the 

area-based calculation calls for to compensate for the west-facing windows and the additional 

loads introduced by cooking. Otherwise, the airflow appears to be reasonably distributed. The

total flow rate supplied by the heat pump (which has a 3-ton cooling capacity) was also within 

the appropriate range. (Each ton of cooling should equate roughly to 350–450 CFM of supplied 

air and the Greenbuilt heat pump supplied just over 400 CFM per ton.) Based on some 

reasonable guidelines, the air distribution system appeared to be working as intended.
15

3 The Greenbuilt House as a Laboratory House

In addition to being an example of a deep energy retrofit, the Greenbuilt house was also 

instrumented to be used as an unoccupied laboratory house. A laboratory house is intended to 

include all the same loads as a traditional occupied home but without occupants. It was thus

possible to install new and untested equipment and try novel techniques for space conditioning 

without the risk of making the occupants uncomfortable. Also, occupant behavior is 

unpredictable and can make test results ambiguous. If occupancy is simulated in a laboratory

house, the results of any experiment cannot be affected by human behavior. Also, a laboratory

house can be filled with sensors without aesthetic concerns. Laboratory houses allow researchers 

to study the performance of advanced energy efficiency technologies without the concerns that 

are inherent in an occupied home, making them the perfect setting for advanced whole-house 

research.

By their nature, laboratory houses require more instrumentation than a typical field test. When 

testing innovative technology, additional sensors are needed to ensure that it is working as 

intended. Especially in the case of testing new space conditioning equipment, conditions 

throughout the house must be measured to verify functionality. A laboratory house also must 

have simulated loads imposed on the space to mimic an occupied home. Simulated occupancy 

involves adding heat to account for the sensible load created by people and appliances, turning 

lights on and off throughout the day, and inducing hot water draws, as all these pieces affect the 

energy consumed by the HVAC and water heating systems. All the controls needed for simulated 

occupancy must be remotely controllable so a researcher does not need to visit the house 

regularly and so people will not have reason to enter the house, which can change the conditions. 

The level of monitoring and control capabilities needed to do these functions far exceeds the 

average field test setup.

3.1 Instrumentation and Controls

To implement a simulated occupancy schedule and run experiments on the space conditioning 

and water heating systems, remote access to sensors and controls is critical. A Control4 system 

was installed to control the thermostat, all the lights, and a few electronics. The Control4 

software allowed lighting and thermostat schedules to be created and edited remotely. The other 

pieces of the simulated occupancy schedule, the sensible heat loads and the hot water draws, 

were controlled by the data acquisition system (DAQ) installed by NREL. A Campbell Scientific 

DAQ was installed and could be accessed remotely. The DAQ collected temperature data for 

each room, power data for the house, PV system, and some large appliances, and local weather 

data from a weather station installed on the roof, among other data collection items. The DAQ 

also had on/off control that was used to turn the electric heaters for sensible loads on and off. 

The same relay control was used to open and close a solenoid valve on the hot water faucet in the 

laundry room. Several unique draw profiles were programmed in the Campbell data logger 

software and NREL researchers could choose the desired hot water draw schedule remotely. 

Appendix B provides a full list of sensors and control functions of the Campbell data system.
16

3.2 Simulated Occupancy

3.2.1 Sensible Heat Gains

Before any experiments could be run, all occupant-related loads needed to be imposed on the

house. The main sources of sensible heat in a home are occupants, major appliances (e.g. 

refrigerator, washer, dryer, oven, and dishwasher), DHW use, and miscellaneous electric loads 

(MELs). The only large appliance that was installed and operational in the Greenbuilt house was 

the refrigerator. The combined sensible load from the remaining appliances (including MELs), 

DHW draws, and occupants was simulated using small space heaters positioned in each room. 

The Building America Benchmark was used to determine the hourly sensible heat gain for each 

load (Hendron & Engebrecht, 2010). The benchmark specifies hourly sensible heat gains for 

three occupants (the average number of residents in a three-bedroom house), but the location is 

not prescribed. To make the occupancy simulation as realistic as possible, the required sensible 

heat loads were dispersed throughout the house based on time of day. For instance, during the 

night, the occupant loads were located in the bedrooms but in the evening, the occupant heat 

gains were introduced in the kitchen and living room. Each heater produced 930 W of heat and 

was turned on at the top of each hour and remained on until the sensible heat load for that room 

and hour was met. Figure 10 shows the sensible loads used to account for occupants by room 

over the course of a day.

Figure 10. Imposed sensible load schedule, by room, to account for occupants

The sensible heat generated by large appliances, hot water draws, and MELs was taken into 

account in the simulated occupancy schedule. The loads for large appliances (range, washer, 

dryer, dishwasher) are evenly distributed over the course of the day in the benchmark, but 

realistically, these appliances run at specific times of day. Therefore, the entire daily load for 

each large appliance was introduced at one time, in the most appropriate location. The sensible 

loads for the range and dishwasher were generated in the kitchen. The washer and dryer would 
17

have been located in the hall closet, so their sensible loads were introduced in the closest room: 

Bedroom 2. All large appliance loads were scheduled to occur in the evening. The daily sensible 

load generated by MELs was distributed throughout the day, as would happen in an occupied 

home, and introduced in the living room. The DHW sensible load was introduced in the master 

bedroom and distributed over the entire day, as hot water is typically used throughout the day.

Figure 11. Imposed sensible load schedule, by room, to account for appliances

3.2.2 Lighting Schedule

All the lights were operated on a daily schedule based on the Building America benchmark 

(Hendron & Engebrecht, 2010). However, the benchmark calls for lights during all hours of the 

day, including the middle of the night and the middle of the day. The benchmark was modified 

so lights would not operate between midnight and 5:00 a.m. and between 10:00 a.m. and 4:00

p.m. The remaining hourly lighting loads were used to determine the required fraction of daily 

lighting energy for each hour. The lighting schedules for each room were adjusted to match that 

profile, keeping the lighting locations consistent with the occupancy schedule. For instance, in 

the morning, lights in the bedrooms and bathrooms were turned on first, followed by the kitchen. 

In the evening, most of the lighting load was in the living room, dining room, and kitchen. Figure 

12 shows the whole-house hourly lighting schedule that was implemented as a piece of the 

occupancy simulation. Nearly all the light bulbs were CFLs with the same power rating, so the 

lighting profile is described in number of light bulbs multiplied by time in hours. For instance, if 

one room had two light bulbs that were on for an hour, that was counted as two light bulb-hours.
18

Figure 12. Aggregate lighting schedule for the whole-house, 

displayed as number of light bulb-hours

3.2.3 Domestic Hot Water Draws

The last piece of simulated occupancy is the DHW draw schedule. Unlike the sensible heat and 

lighting schedules that remained constant, the DHW schedule was varied approximately once a 

week to study the effects of draw patterns on system efficiency. Six draw profiles were tested, 

each with two system configurations. Each was tested with the HPWH alone supplying hot water 

and with the HPWH and ICS system working together to supply hot water.

The benchmark DHW draw profile prescribes a certain amount of hot water to be drawn every 

hour; however, their exact schedules and flow rates are not specified. In practice, the benchmark 

DHW profile is usually implemented by drawing each hour’s worth of hot water at the top of the 

hour. One draw profile followed this practice, with the flow rate set to 3 gpm for all draws. A 

more realistic draw profile was created based on the standard benchmark draw profile, but with 

shorter, discrete draws. The total daily volume drawn and total hourly volume drawn were

identical to the hourly benchmark draw profile. Two other draw profiles were created to 

determine whether the efficiency of the DHW system was affected by the time of day that most

draws took place. Keeping the daily hot water volume from the hourly benchmark draw profile 

consistent, a morning-dominated profile and an evening-dominated profile were created using a 

tool for generating realistic hot water events (Hendron, Burch, & Barker, Tool for Generating 

Realistic Residential Hot Water Event Schedules, 2010). Lastly, the hourly benchmark hot water 

profile was doubled and quartered to see the effects of larger and smaller daily hot water draw 

volumes on system efficiency. (The hourly benchmark, discrete benchmark, morning-dominated 

and evening-dominated profiles are shown in Figure 13 through Figure 16.) The resulting 

performance of the individual draw profiles will be discussed in Section 3.4. All draws were 

performed with a solenoid valve attached to the hot water line in the laundry room, with the flow 
19

rate set to 3 gpm for all draws. All other hot water end uses in the house were disabled to control

all hot water use.

Figure 13. Hourly benchmark DHW draw profile Figure 14. Benchmark hot water draw profile 

broken into smaller discrete draws

Figure 15. Morning-dominated hot water 

draw profile

Figure 16. Evening-dominated hot water 

draw profile

3.3 Control4

A Control4 home control system was installed as part of the extensive retrofit at the Greenbuilt 

house. Its main control features included control of all hardwired lights, the thermostat, and a 

few plug loads. The scale of the retrofit was such that wiring all the light switches to the 

Control4 system was not difficult, but in many remodeling projects, this level of integration 

would be unrealistic. All systems connected to the Control4 system could be controlled by an 

interface accessible through the television or through the Control4 Composer computer program, 

which could be changed from anywhere with Internet access. Appendix B provides a complete 

list of all the Control4 equipment installed at the Greenbuilt house.
20

Figure 17. Control4 lighting control 

interface through the television

Credit: Judy Lew-Loose, SMUD

Figure 18. Screen shot of lighting schedule from 

Composer program

All features controlled by Control4 could be set to follow a daily schedule or changed 

instantaneously by either control interface. All physical light switches and the thermostat could 

be used to override the schedule. A whole-house switch at the front door was programmed to 

turn on a prescribed number of lights and change the thermostat setting when the “Home” button 

was pressed and turn off lights and set back the thermostat when the “Away” button was pressed. 

A third button in the entryway, labeled “Green,” could also be programmed to turn lights on or 

off and set the thermostat set point.

Figure 19. Home, Away, and Green switch 

buttons in the entryway

Credit: Judy Lew-Loose, SMUD

Figure 20. Control4 thermostat can be 

controlled manually or remotely 

Credit: Judy Lew-Loose, SMUD

Several other functionalities are available with Control4 systems, including the ability to control 

motorized shades, integrate sensors from around the house, and gather whole-house energy use 

from a smart meter. The purpose of a home control system, from the perspective of energy 

efficiency, is to take some energy-related decisions out of the occupants’ hands to make smarter 

decisions. For instance, occupancy sensors could be installed in infrequently used rooms, 

allowing the lights to turn off automatically when the room is empty. Shades could automatically 

be drawn when the sun comes out during the summer. The thermostat set point could be changed 

based on the weather. So aside from creating a technologically advanced house, a home control 

system could also reduce energy costs by making smarter energy choices.
21

3.3.1 Role of Control4 in the Greenbuilt Laboratory House

The full functionality of the Control4 control system was used during this project, but with a 

different intent than in typical installations. For the laboratory house implementation, the 

Control4 system was used as an extension of the DAQ and enabled NREL researchers to control 

all lights and the thermostat on a schedule and remotely view and change settings. The lights 

were a part of the simulated occupancy schedule (see Section 3.2), and it would have been very 

difficult, if not impossible, to implement a lighting schedule without the Control4 system. The 

thermostat was changed regularly for various space conditioning strategies (see Section 3.4). All 

space conditioning schedules were created remotely and the Control4 thermostat had enough 

flexibility to implement the complicated schedules.

The “Home” and “Away” buttons were programmed similarly to a typical installation, with one 

additional feature. When the “Home” button was pressed, several lights in the main living area 

turned on and an email alert was sent to NREL researchers. When the “Away” button was 

pressed, lights were turned off and another email alert was sent. This functionality was added a 

few months into the project, because there were many visitors during the winter and the

researchers needed to know when people were inside. The ability to send emails based on certain 

action items is a useful feature in Control4, especially in the laboratory house application.

3.4 Space Conditioning

3.4.1 Introduction

Building America classified Sacramento as a “hot-dry” climate, so cooling loads are the primary 

concern (ORNL & PNNL, 2010). However, the performance of some systems, such as the ICS 

and HPWH, are affected by winter temperatures, so testing began at Greenbuilt in February 

2010. The baseline HVAC case for both summer and winter refers to when a constant set point is 

used throughout the day and simulated occupancy is enabled. During the baseline schedule in the 

winter, the house was kept at 68°F (20°C) and simulated occupancy (including lights, space 

heaters, and hot water draws) was enabled. In the summer, the baseline case consisted of a 

constant set point of 78°F (25.5°C) all day while the simulated occupancy schedule was running. 

Figure 21 shows the hourly HVAC loads throughout a typical winter day relative to a typical 

summer cooling day. The outdoor temperatures are also shown to illustrate the difference

between summer and winter weather.
22

Figure 21. Energy use comparison for the baseline HVAC case during the summer and winter

The days shown in Figure 21 represent average temperature days for the 2010 summer and 

winter seasons. Winters are mild in Sacramento, so a typical winter day has a high of 60°F

(16°C) and a low of 45°F (7.5°C). A typical summer day reaches a high of 95°F (35°C) and a 

low of 52°F (11°C). Summer and winter days have very different daily HVAC load profiles: the 

heating load is highest for winter nights and early mornings, whereas the late afternoon cooling 

load represents the peak load for summer days. There is no dramatic spike in winter heating 

loads, so they do not stress the electricity distribution in the same way that summer peak loads 

do. In Sacramento, the peak electricity demand in the summer occurs between 4:00 p.m. and 

7:00 p.m. (which coincides with the peak in the summer HVAC consumption), so reducing 

cooling demand during this time is a priority for SMUD.

Despite the lack of a winter peak load, the typical daily heating load is nearly identical to the 

typical daily cooling load. Table 8 shows the daily energy consumption by the space 

conditioning heat pump and air handler for the same typical days shown in Figure 21. For 

comparison, the daily space conditioning loads for an extremely hot summer day (high of 104°F

[40°C]) and an abnormally cold winter night (low of 35°F [2°C]) are also shown. The extreme 

heating and cooling loads are still similar, but the daily summer cooling load is slightly higher. 

Again, the cooling load is more important, especially during extended periods of hot weather, 

because it is concentrated over a short period in the afternoon. The daily heating load, even on 

especially cold days, is less a concern as it is spread out over the night and day, making the 

energy demand more manageable for SMUD. The electricity demand during the summer peak 

hours is also shown in Table 8 to further illustrate the interest in cooling loads, especially during 

the peak hours. Even though the summer peak only accounts for three hours of the day, the peak 

cooling load on a typical summer day is nearly half the daily total; on an extremely hot day, the 

peak load represents about 40% of the daily cooling load.
23

Table 8. Total Daily and Peak HVAC Energy Consumption for Baseline HVAC in 

Summer and Winter for Typical and Extreme Weather Days

Heat Pump

Energy

(kWh)

Air Handler

Energy

(kWh)

Total HVAC

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Summer—typical 8.3 1.4 9.7 4.6

Winter—typical 8.9 1.4 10.3 0.9

Summer—very hot 15.2 2.3 17.5 6.6

Winter—very cold 13.8 2.1 15.9 1.0

3.4.2 Research Questions for Cooling Experiments

A series of space conditioning experiments were run at the Greenbuilt laboratory house, designed 

to determine the most effective way to cut cooling costs and the best way to shift peak loads. The 

space conditioning experiments were designed to answer the following research questions:

• In the hot, dry climate of Sacramento, are there ways to reduce cooling loads throughout 

the day and during peak hours?

• Can the afternoon peak load be shifted using precooling without making residents 

uncomfortable?

• How does precooling affect the total daily cooling load?

• Are there variations on the precooling schedule that shift the peak energy consumption 

without increasing overall energy use?

• Is precooling still effective at shifting peak and maintaining comfortable indoor 

conditions during a heat storm (three consecutive days that reach 100°F [38°C])? Does 

precooling during a heat storm increase the daily cooling load?

• Are external shades an effective means of cutting cooling costs?

• Is a whole-house fan an effective means of cutting cooling costs?

All the tests were run with simulated occupancy to mimic the additional load on the occupied

house. (The simulated occupancy schedule is discussed in more detail in Section 3.1.) Each test 

schedule, except the heat storm precooling test and the whole-house fan test, was run for 1–2 

weeks. A brief description of each test follows. The set points were suggested by SMUD to be 

consistent with Title 24 recommendations.

• Baseline. As with all the other tests, simulated occupancy was enabled. The thermostat 

was kept at a constant temperature of 78°F (25.5°C) at all times of day and night.

• Simple precooling. The house was precooled to 74°F (23.3°C) between 10:00 a.m. and 

4:00 p.m. The heat pump was turned off all other times of day. This test was conducted to 

see if a well-insulated home could remain comfortable with limited cooling during a 

summer day.
24

• Advanced precooling. The thermostat was set to 74°F (23.3°C) between 10:00 a.m. and 

4:00 p.m., 80°F (26.7°C) between 4:00 p.m. and 8:00 p.m., and 78°F (25.5°C) all other 

times. This is a more typical precooling schedule and will be compared to the “simple 

precooling” case for energy use and indoor comfort.

• Heat storm precooling. The same precooling schedule as “advanced precooling” was 

used during a heat storm, which is defined as three consecutive days that reach 100°F

(38°C). Heat storms place enormous stress on electricity supplies because the homes 

never have a chance to cool down, so air conditioners have to work harder and longer to 

counteract the extreme heat. Shifting peak cooling load during a heat storm would help 

the utility manage the electricity demand. Note that the shades were drawn during the 

heat storm precooling test (but not during the other precooling tests).

• Shade control. All the windows on the west side of the house had externally mounted 

semitransparent shades. The sliding glass door in the living room was shaded by a 

retractable awning. All the shades (and the awning) were drawn (covering the windows) 

during the shade control test for all hours of the day. The thermostat was set to maintain a 

constant temperature of 78°F (25.5°C) at all times.

• Whole-house fan. A short-term test of the whole-house fan was conducted over four

days. The shades remained down during the whole-house fan test. The fan was operated 

between 8:00 p.m. and 10:00 p.m. with a few windows in the house open. The windows 

were closed after the fan was turned off. The fan was run later in the evening to ensure 

that the outdoor temperature was cooler than the interior temperature.

3.4.3 Results

The tests spanned several months (July through September), so weather conditions varied. 

Average outdoor temperature and insolation were used to find days with similar weather 

conditions. These days were then used to compare results. In the following section, a graph 

comparing hourly combined energy consumption of the heat pump and air handler is shown for 

each test (see Figures 23 through 33). All tests are compared to the baseline schedule. The two 

precooling schedules are also compared to each other. The total HVAC energy consumption for 

each test day is summarized in a table following each graph (see Tables 8 through 14). The 

HVAC energy consumption during the summer peak hours (4:00 p.m. to 7:00 p.m.) is also 

shown in each table. All the data come from a single day of testing and are not averaged with any 

other days.

3.4.3.1 Simple Precooling

The first of two precooling strategies tested was the simple precooling schedule. This strategy 

allows the heat pump to cool the house only six hours of the day; nonetheless, the indoor air 

temperature did not exceed 79°F (26°C). This spartan cooling schedule would likely not produce 

the same results in homes that are not as well sealed or insulated as the Greenbuilt house. The 

strategy relies on the home’s ability to maintain a cool indoor air temperature without being 

affected by leaks and heat transfer from the warmer outdoor air. Even though the application for 

this scenario is limited, it provides a means of shifting summer peak loads and reducing overall 

HVAC energy use in high performance homes without sacrificing comfort.
25

The simple precooling strategy effectively shifts the peak to earlier in the day and reduces total 

daily HVAC energy use. The living room temperature is also displayed in Figure 22 to show that 

the simple precooling strategy results in an increase in indoor temperature during peak hours. It 

does not, however, deviate outside the bounds of the summer comfort zone as defined by 

ASHRAE, which has an upper temperature limit of 80°–83°F (27°–28°C), depending on the RH

(ASHRAE, 2005). Figure 23 shows the outdoor air temperature and insolation on the days 

compared in Figure 22. The outdoor air temperature matches well between the two days, but the 

total insolation on the simple precooling day was about 30% lower than the day of the baseline 

schedule. This is primarily due to the timing of the tests: the baseline schedule was run in midJuly and the simple precooling test occurred in mid-September.

Figure 22. HVAC energy use for the baseline 

case and the simple precooling schedule

Figure 23. Weather comparison for baseline case 

and simple precooling days

Table 9 shows the daily and peak HVAC energy use for the days compared in Figure 22. The 

simple precooling schedule virtually eliminates the peak HVAC energy use and reduces the 

overall daily HVAC use by nearly 30%. Despite the success of this strategy, it may not be 

suitable for the warmest summer days. The outdoor temperature reached a maximum of 93°F

(34°C) while this schedule was running. However, if a heat storm occurred, the home would hold 

more thermal energy through the warm nights and the time allowed for cooling may not be 

enough. The advanced precooling schedule may be a better option for the hottest days.

Table 9. Total Daily HVAC Energy Consumption for Baseline and Basic Precooling

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m. 

(kWh)

Baseline 6.68 1.34 8.02 3.56

Simple precooling 4.69 1.03 5.72 0.04

3.4.3.2 Advanced Precooling

The second precooling schedule, advanced precooling, is more typical. It has a cooler set point 

(74°F [23.3°C]) in the hours preceding the peak hours, a warmer set point (80°F [26.7°C]) during 

the peak time, and a standard set point (78°F [25.5°C]) for the rest of the day. Compared to the

baseline case, the advanced precooling strategy succeeds in shifting the peak load, but does not 
26

save energy. In fact, for the two days compared in Figure 24, the daily HVAC energy use 

increased by 28% under the advanced precooling schedule. Figures 24 and 25 show the hourly 

HVAC energy use profile for the baseline schedule and the advanced precooling case, along with 

a comparison of the weather on those days.

Figure 24. HVAC energy use for baseline case 

and advanced precooling schedule

Figure 25. Weather comparison for baseline 

case and advanced precooling days

Table 10. Total Daily HVAC Energy Consumption for the Baseline and Advanced Precooling Cases

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Baseline 6.68 1.34 8.02 3.56

Advanced precooling 8.60 1.64 10.24 0.04

3.4.3.3 Advanced Precooling Versus Simple Precooling

Figure 26 and Figure 27 compare the hourly cooling energy used during simple precooling and 

advanced precooling and show the indoor temperature as measured in the living room. The 

weather on the days of comparison is also shown. By turning off the heat pump between 4:00 

p.m. and 10:00 a.m., the simple precooling scenario reduces the total daily consumption by about 

35% relative to the advanced precooling schedule. During that interval, the energy use and 

indoor temperature look very similar. During the peak hours, the indoor temperature in both 

situations reaches a maximum of about 79°F (26°C). The advanced precooling schedule turns the 

heat pump back on for cooling at 8:00 p.m. and the interior temperature drops a couple of 

degrees. Even though the simple precooling maintains a “comfortable” temperature (as defined 

by ASHRAE), slightly cooler overnight temperatures may be preferred, making the advanced 

precooling schedule more attractive, despite the higher energy use. Both schedules effectively 

eliminate peak HVAC energy use.
27

Figure 26. HVAC Energy use for the simple 

precooling and advanced precooling schedules

Figure 27. Weather comparison for simple 

precooling and advanced precooling days

Table 11. Total Daily HVAC Energy Consumption for Simple Precooling and Advanced Precooling

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Simple precooling 5.83 1.17 7.01 0.04

Advanced precooling 9.09 1.69 10.78 0.07

3.4.3.4 Heat Storm Precooling

The baseline case on a hot day is compared to one day during a heat storm with the advanced 

precooling strategy enabled and the shades drawn. The day from the baseline case also occurred 

during a heat storm. The outdoor temperature and the insolation for the days of comparison are 

also shown. The precooling effectively shifts the peak HVAC energy use, but does not reduce 

overall energy consumption. Also, the shades were drawn during the heat storm advanced 

precooling test, which could explain why cooling energy did not increase more as a result, as was 

seen when the baseline case and the advanced precooling schedule were compared under more 

typical conditions (see Table 10). This combination of the advanced precooling schedule and 

closed shades during a heat storm may be an effective way for homeowners to deal with 

extremely hot summer weather. The added stress on the utility during heat storms is difficult to 

keep up with, especially during peak hours, and this strategy effectively shifts the peak load with

only a slight increase in overall HVAC energy use.
28

Figure 28. HVAC energy use comparison 

between baseline case and advanced 

precooling (with shades) during a heat storm

Figure 29. Weather comparison for baseline 

case and advanced precooling (with shades) 

during a heat storm

Table 12. Total Daily Energy Consumption for Baseline Case and 

Heat Storm Precooling (With Shades)

Heat Pump

Energy

(kWh)

Air Handler

Energy

(kWh)

Total HVAC 

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Baseline 15.47 2.34 17.81 6.57

Heat storm precooling 15.73 2.38 18.11 0.08

3.4.3.5 Shade Control Test

The shades for the windows on the west side of the house and the awning were drawn (see 

Figure 4a) during all hours of the day for the shade control test. The motorized shades were 

drawn all day because they were not remotely controllable. A large percentage of the west wall is 

composed of windows (see Figure 6), which introduced a large solar heat gain. Blocking that 

heat gain dramatically reduces the required cooling energy on a summer day. For the two days 

compared in Figure 30, the shades reduced the HVAC energy consumption by 34%. These 

shades were externally mounted; internally mounted shades would likely have a smaller effect on 

energy savings because they do not block radiation. Even though the external shades do not 

reduce the peak HVAC energy use as dramatically as the precooling schedules, the shades can

cut the peak load by nearly 40% and reduce the overall daily HVAC energy use. This is by far 

the simplest strategy tested, so it is likely the most attractive to homeowners.
29

Figure 30. HVAC energy use for the baseline 

case and shade control tests

Figure 31. Weather comparison for baseline 

case and shade control tests

Table 13. Total Daily HVAC Energy Consumption for Baseline Case and Shade Control Tests

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Baseline 9.20 1.63 10.82 4.81

Shade control 5.91 1.14 7.05 2.93

3.4.3.6 Whole-House Fan Test

The whole-house fan test was performed over the course of four days while the shades and 

awning were still drawn. Energy consumption was reduced slightly by using the whole-house fan 

with the shades drawn relative to the shade control test, as seen when comparing the total HVAC 

energy consumptions in Table 13 and Table 14. (Note: The days shown in Tables 13 and 14 were 

comparable in terms of outdoor air temperature and insolation.) The HVAC energy use shown in 

Figure 32 and Table 14, however, does not include the energy used by the whole-house fan. This 

will add about 300 W during operation. Because the whole-house fan was run for two hours each 

day, an additional 0.6 kWh should be added to the daily HVAC energy total, making the shade 

control test and whole-house fan test nearly identical. For this situation, running the whole-house

fan does not reduce energy consumption, but if the whole-house fan had been run without 

shades, a more noticeable benefit might have been be apparent. In terms of peak HVAC use 

reduction, the whole-house fan test with shades decreased peak use by 46%, which is similar to 

the reduction seen with the shades alone.
30

Figure 32. HVAC energy use for the baseline 

case and whole-house fan tests

Figure 33. Weather comparison for baseline 

case and whole-house tan test

Table 14. Total Daily HVAC Energy Consumption for Baseline Case and Whole-House Fan Tests

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

Summer Peak

4:00 p.m.–7:00 p.m.

(kWh)

Baseline 9.20 1.63 10.82 4.81

Whole-house fan 5.39 1.10 6.49 2.60

3.4.4 Time-of-Use Pricing Comparison

Peak energy use puts a large strain on utilities. One way they can encourage lower peak use is to 

institute a time-of-use (TOU) billing plan. These plans charge a higher rate for electricity during 

peak hours and a lower rate during off-peak hours. Smart meters enable utilities to implement 

TOU pricing schedules, although this is still at an early stage. Currently, most TOU billing plans 

are optional for residential customers, but will likely become more common and cease to be 

optional in the future. For illustration, the various space conditioning schedules are compared 

with a hypothetical TOU pricing scheme.

The peak price used for this rate plan is $0.24/kWh and the off-peak price is $0.0997/kWh. The 

peak hours are 4:00 p.m. to 7:00 p.m.; all other hours are off-peak (most TOU programs define a 

much longer peak period). This rate schedule is not taken from a real utility price plan but the 

rates are. This schedule is being imposed on the results for various HVAC tests only to illustrate

the motivation for shifting peak HVAC use. Table 15 shows the price for each HVAC schedule 

for a single day. The percent savings are also shown for each pair of tests.
31

Table 15. Time-of-Use Pricing Comparison for Various HVAC Schedules

Off-Peak

Energy Use 

(kWh)

Peak

Energy Use 

(kWh)

Total HVAC 

Cost 

($)

Savings 

Relative to 

Baseline

Baseline 4.45 3.56 1.31 –

Simple precooling 5.68 0.04 0.58 56%

Baseline 4.45 3.56 1.31 –

Advanced precooling 10.20 0.04 1.03 21%

Baseline—heat storm 11.23 6.57 2.71 –

Heat storm precooling 18.03 0.08 1.82 33%

Baseline 6.02 4.81 1.77

Shade control 4.12 2.93 1.12 36%

This TOU pricing plan is not expected to be perfectly realistic, but it does illustrate the monetary 

advantage to tailoring a precooling schedule to fit with a TOU billing schedule. The three 

precooling schedules, simple precooling, advanced precooling, and advanced precooling with 

shades (during a heat storm), eliminate HVAC energy use during the peak hours and shift all the 

cooling energy to the off-peak hours, which results in cost savings even when the overall energy 

use is higher than the baseline case. The shade control test does not involve any peak shifting,

but the reduction in total daily energy is enhanced by the TOU pricing. Most utility TOU 

programs have a much longer peak period, such as from 2:00 p.m. to 8:00 p.m., but our 

precooling schedules were designed around a 4:00 p.m. to 7:00 p.m. peak period. These cooling 

schedules would not produce such dramatic results when used with real TOU schedules, but a 

precooling schedule tailored specifically for the TOU pricing schedule could be as effective. As 

new pricing schedules become more common, creating heating and cooling schedules to take 

advantage of the off-peak prices can be a worthwhile effort.

3.4.4.1 Additional Tests

With more time, a few additional tests would have been performed. The highest priority tests 

would have been to repeat the whole-house fan test and the heat storm precooling test without 

the shades drawn. Having the shades drawn for both tests gives the best case scenario, especially 

for the heat storm precooling, but decoupling the effects is preferred.

Another test that would be interesting to implement would be the pairing of the simple 

precooling schedule with the whole-house fan test. This combination may bring the interior 

temperatures in the evening into a more comfortable range without requiring as much energy as 

running the heat pump. The power to the whole-house fan should also be measured to capture the 

total HVAC load.

The shade control test was done with external shades, which were very effective but are not as 

common as internal shades. It would be useful to repeat the shade control test with internal 

shades to see how effective they are relative to the external type. A variety of types of internal 

shades could also be tested.
32

A nighttime setup test was originally planned, where the thermostat set point is raised from the 

standard 78°F (25.5°C) set point overnight. This will likely have minimal impact on energy use,

as the heat pump usually turned off by 11:00 p.m. and did not turn on again until 9:00 a.m. This 

also may be uncomfortable to the residents, but may still be worth trying in the future.

3.5 Domestic Hot Water

3.5.1 Introduction

The DHW system in the Greenbuilt house consists of two parts, a CopperHeart ICS system and 

an AirTap HPWH. The ICS is a solar collector with a 40-gallon capacity that provides hot water 

to the system when the solar radiation is sufficient. It was mounted on the west-facing roof, as 

shown in Figure 34. The ICS preheats water before it enters a (formerly) conventional electric 

resistance water heater in the garage. The electric resistance elements in the tank were disabled 

and an add-on HPWH unit was attached to the top of the storage tank to provide supplemental 

heating when the ICS SWH could not meet the hot water demand. The AirTap A7 add-on 

HPWH used for this application is shown in Figure 35. Appendix A includes further details 

about these systems.

Figure 34. CopperHeart 40-gal ICS 

NREL/PIX 19617, Credit: Lieko Earle

Figure 35. AirTap add-on HPWH 

NREL/PIX 19618, Credit: Lieko Earle

3.5.2 Test Setup

The DHW system was designed to be operated in two ways:

• Connect the mains water to the ICS, where it is heated by solar radiation and then sent to 

the storage tank in the garage for additional heating by the heat pump, if needed. 

• Bypass the ICS system and send the mains water directly to the storage tank, where it is 

heated exclusively by the add-on HPWH. 

A schematic of the DHW system is shown in Figure 36. This system was designed with the 

bypass option so that the pipes leading to and from the ICS can be drained. This is ideal for 

maintenance or when weather conditions are not optimal and the ICS needs to be disabled. This 
33 

schematic also shows a pressure relief valve, which is installed for safety reasons and is similar 

to pressure relief valves found on conventional hot water storage tanks. 

Figure 36. Schematic showing the DHW system. This includes an ICS solar panel, an add-on 

HPWH, and a storage tank. 

Figure 36 shows some of the sensors that were used to quantify the performance of the ICS and 

HPWH. The water temperature is measured as it exits the water heater, and at the inlet and outlet 

of the ICS and water storage tank, as well as at the fixture in the house. These temperatures were 

measured using immersion thermocouples installed in the pipes. A thermostatic mixing valve is 

located before the hot water enters the house, primarily to ensure that extremely hot water from 

the ICS is tempered before reaching the fixtures. A flow meter was also installed to measure the 

flow rate of the water being drawn from the tank. 

The delivered water temperature was measured at the hot water connection in the laundry closet. 

This faucet was used to simulate all water draws during this experiment (see Figure 37). The hot 

water supply valve for the laundry room was fully opened and attached to a solenoid valve that 

opened and closed to generate hot water draws. Downstream of the solenoid valve, a rotometer 

with a needle valve was installed (after this picture was taken) that allowed the flow rate to be 

measured and controlled. As long as the system water pressure remained relatively constant, the 

flow rate was held at 3 gpm for all draws. After the rotometer, a thermocouple was installed to 

measure the temperature of the water as it went down the drain, as shown in Figure 38. This 

measurement was intended to represent the water temperature delivered to the end user. 
34

Figure 37. Water draw configuration in laundry 

closet

NREL/PIX 19619, Credit: Lieko Earle

Figure 38. Thermocouple to measure end-use 

temperature of hot water

NREL/PIX 19620, Credit: Lieko Earle

In addition to the sensors shown in Figure 36, air temperature was measured at the exhaust of the 

heat pump and in a central location in the garage. These measurements, along with some data 

provided by the manufacturer, are essential for determining the cooling effect of the add-on 

HPWH on its surroundings. Other important measurements include the solar irradiance in the 

plane of the ICS and the electric energy use of the HPWH. The solar irradiance was measured 

using a pyranometer installed on the roof. The electric energy use of the HPWH was measured 

using a WattNode power transducer. Appendix B includes a detailed list of this instrumentation.

3.5.3 Research Questions

The DHW system was installed at the Greenbuilt house to optimize the energy savings of the hot 

water system. The ICS was chosen because Sacramento has a temperate climate and does not 

typically see air temperatures below freezing; therefore, a direct solar system can be used. 

Sacramento also has ample solar radiation throughout most of the year. The add-on HPWH unit 

was installed to supplement the water heating in the most efficient way possible for an allelectric home. The AirTap unit has a manufacturer’s quoted EF value of 2.11, meaning that it 

should be more than twice as efficient as electric resistance heating elements. Tests were 

performed that simulated hot water use of a typical household to determine the performance of 

this system. The data collected were intended to answer the following research questions:

• How much thermal energy was collected by the ICS system during winter and summer 

testing, and how does this compare to the amount of thermal energy needed to satisfy the 

hot water demand?

• What time of day is the ICS most effective at providing hot water? How effective is the 

use of the ICS and HPWH at reducing peak energy loads? How effective is the ICS and 

HPWH at reducing daily energy consumption?

• What is the COP of the system in the summer and how does that compare to the COP of 

the system in the winter?

• How do different draw profiles affect the system COP?
35

• How much is the temperature of the air in the garage affected by the operation of the heat 

pump?

• Was the outlet water temperature maintained for all profiles?

To answer these questions, six daily draw profiles were tested during the winter and summer 

seasons of 2010. Each was run across a span of about four to six days for both system 

configurations: the ICS and HPWH operating as a combined system and the HPWH operating 

independently. The temperature set point for the HPWH was 130°F (54°C) throughout testing. 

The flow rate was held constant at 3 gpm for all draw profiles. The data were averaged hourly 

across the multiple days of testing to reduce the impact of weather variations. Figures 13 through 

16 provide graphical representations of the draw profiles and each profile is described here.

• Hourly benchmark. Draws are intended to simulate the hot water use of an average 

American family. These draws occur at the top of the hour throughout the course of the 

day. The total daily volume of water drawn for the benchmark profile is 64.5 gallons (240 

liters). This is the baseline draw profile and will be used to answer most of the DHW 

research questions.

• Discrete benchmark. Similar to the hourly benchmark in terms of volume drawn each 

hour, but the profile comprises many smaller, more realistic draws.

• Double volume benchmark. Similar to the hourly benchmark in terms of profile shape, 

but differs because the volume drawn is doubled. Draws still occurred at the top of the 

hour.

• Quarter volume benchmark. Similar to the hourly benchmark in terms of profile shape, 

but the volume drawn at the beginning of each hour is reduced by 75%.

• Morning dominated. Draws simulate a real-life situation where most of the hot water is 

used in the mornings.

• Evening dominated. Draws simulate a real-life situation where most of the hot water is

used in the evenings.

3.5.4 Results

3.5.4.1 Integrated Collector Storage Thermal Energy Analysis

The thermal energy collected by the ICS is determined by the amount of solar radiation that 

reaches its surface. Figure 39 shows the average solar irradiance that was measured by the 

pyranometer during February and August, which represent the winter and summer seasons, 

respectively. The dashed lines show the averaged values across the month and the solid lines 

show the averaged values during the hourly benchmark test. This graph shows that the solar 

irradiances during the August and February tests of the hourly benchmark are representative of 

the monthly averages. This plot also shows that the solar irradiance in the summer is more than 

double that in winter. (the irradiance was measured in the plane of the ICS, which faced west 

with an angle of 27° from horizontal).
36

Figure 39. Solar irradiance measurements for the months of February and August are compared to 

measurements taken during the hourly benchmark test

To assess the performance of the ICS system, the thermal energy collected by the ICS solar panel 

was calculated for the hourly benchmark test and compared to the thermal energy needed to meet 

the hot water demand (see Eq. 3.1):

Qthermal = m ∙ Cp ∙ ∆T (3.1)

Where:

m = the mass of the water drawn from the ICS solar panel

Cp = the specific heat of water 

∆T = the difference between the temperature of the water exiting the solar panel 

and the temperature of the water entering the solar panel. 

A similar calculation was performed to determine the thermal energy associated with the pipes 

running to and from the ICS solar panel. This energy can be either positive or negative, because 

sometimes the pipes experience thermal gains and other times thermal losses. The thermal 

energy of the ICS as a system, which includes the solar panel and its piping, is discussed in the 

remainder of this section.

The amount of thermal energy collected by the ICS during the summer hourly benchmark test is 

shown in Figure 40. The results show that the ICS can provide all the hot water demand from 

4:00 p.m. through midnight. For morning hot water draws, some of the load was met by the ICS 

but the HPWH had to supplement it. The HPWH shows a negative thermal energy in the 

afternoon because the average inlet water temperatures were higher than the average temperature 

of the water in the storage tank. This results in a negative calculated thermal energy for the 

HPWH. Overall, for the summer hourly benchmark test, the ICS provided 81% of the energy for 

water heating.
37

Figure 40. Thermal energy of the ICS and 

HPWH during the summer hourly 

benchmark test

Figure 41. Thermal energy of the ICS and 

HPWH during the winter hourly 

benchmark test

For winter testing, the hourly benchmark draw profile produced the thermal energy profile 

shown in Figure 41. The thermal energy requirement is greater in the winter because the mains 

water temperature is colder and therefore more energy is needed to heat the incoming water to 

set point temperature. These data show that the heat pump was used almost exclusively because

much less solar energy was available during February. Also, the thermal energy for the ICS 

system went slightly negative during part of the day, meaning the thermal losses to the 

environment outweighed the gains. This implies that sometimes in the winter using the ICS is 

detrimental to the DHW system. For the winter portion of testing, the hot water provided by the 

ICS made up only 15% of the hot water demand, all of which was in the second half of the day.

Table 16 summarizes these results. These numbers imply a solar fraction of approximately 48% 

for this system, which is estimated as the average of the summer and winter hot water 

contributions made by the ICS. This is in line with expectations, because a 50% solar fraction 

was expected. The solar fraction represents the net energy savings associated with an SWH

system and is the measure of the amount of solar energy collected divided by the total amount of 

energy required to meet the system demand. The solar fraction of an SWH system depends on 

the quality of the solar resource, the technical characteristics of the individual system, and water

use patterns (Denholm, 2007). 

Table 16. Summary of DHW Daily Thermal Energy Generation Versus 

Requirement for Hourly Benchmark

Thermal Energy

Requirement 

(kWh)

Thermal Energy

From ICS 

(kWh)

ICS Contribution

(%)

Summer 7.6 6.2 81

Winter 11.0 1.6 15
38

3.5.4.2 Performance and Relationship to Time of Day

The ICS contributes most effectively to the hot water demand in the afternoon and evening 

hours. This makes an ICS system an attractive option for reducing demand during periods of 

peak energy use, which is between 4:00 p.m. and 7:00 p.m. during the summer in Sacramento. 

Figure 42 shows the energy used during the summer hourly benchmark test for the ICS and 

HPWH combined system and the HPWH alone. Also shown is the predicted energy use for a 

typical electric resistance water heater assuming the same draw profile. The peak energy hours 

are highlighted in light blue. When the ICS and HPWH were used together, the energy used 

during peak demand was reduced to zero. In fact, the ICS was able to provide all hot water from 

2:00 p.m. to midnight, a period that accounts for nearly half the daily hot water use. The HPWH 

operating alone reduced the energy use during peak demand by 56% on average.

Figure 42. Electric energy use during the 

summer benchmark test

The peak energy hours are highlighted in blue.

Figure 43. Electric energy use during the 

winter benchmark test

The summer peak energy hours (4:00 p.m.–

7:00 p.m.) are highlighted in blue.

Figure 43 shows the energy used during the winter hourly benchmark test. There is not a peak 

period during the winter time, but summer peak hours (4:00 p.m. to 7:00 p.m.) are shown on the 

graph for comparison. The ICS was not able to provide enough hot water to meet the demand in 

the evening, but the combined HPWH and ICS system was able to reduce the load between 4:00 

p.m. and 7:00 p.m. by 55% on average. The HPWH operating alone reduced the energy use from

4:00 p.m. to 7:00 p.m. by 45% on average.

In general, it is difficult to predict the energy reduction during hours of peak electricity demand 

because HPWH use is tied to hot water use and is not necessarily coincident with times of peak 

demand. For most homeowners, total electricity use is more important than peak energy use, so 

the results from Figures 42 and 43 are compared based on the daily energy savings. Tables 17 

and 18 show the daily average energy savings for the two configurations compared to an electric 

resistance water heater. These results clearly show that the combined ICS and HPWH system has 

an energy savings advantage over the HPWH operating alone and that the greatest benefit is seen 

in the summer. Both configurations achieved greater than 50% daily energy savings for the 

summer and winter hourly benchmark tests relative to a standard electric water heater.
39

Table 17. Daily Energy Use for Summer Hourly Benchmark

Daily Energy Use

(kWh)

Energy Reduction

(%)

Electric resistance 9.0 –

HPWH and ICS 1.2 87.1

HPWH alone 3.9 56.9

Table 18. Daily Energy Use for Winter Hourly Benchmark

Daily Energy Use

(kWh)

Energy Reduction

(%)

Electric resistance 11.8 –

HPWH and ICS 4.7 60.7

HPWH alone 5.2 56.3

3.4.4.3 Domestic Hot Water System Coefficient of Performance

Another way to compare the performance of the DHW systems is to calculate a COP for each 

configuration. COP is the measure of the useful energy transferred to the water divided by the 

input energy to the system. Equation 3.2 was used to calculate system COP for the DHW system:

COP = Qthermal

Whpwh

= m∙Cp∙∆T

Whpwh

(3.2)

Where:

m = the mass of the water drawn from the tank

Cp = the specific heat of water 

∆T = the difference between the temperature of the water exiting the tank and 

the temperature of the water entering the tank

Whpwh = the electric energy consumed by the heat pump. 

When evaluating the ICS and HPWH as a system, the inlet water temperature to the ICS was 

used instead of the inlet temperature to the tank. The mass of the water entering the tank is 

calculated by knowing the volume of water drawn (which is measured by a volumetric flow 

meter) and the density of water.

Daily averages of thermal and electric energy were used to calculate the COPs for the two 

configurations during summer and winter testing. The calculated COPs are listed in Tables 19 

and 20. The results show that the COP of the ICS and HPWH as a combined system is 

significantly better than the HPWH alone in the summer. The results during winter testing show 

a slight advantage of using the ICS and HPWH as a combined system, butdo not show a 

significant advantage over operating the HPWH alone. If these values are assumed to be 

representative of the best- and worst-case scenarios for the year, the average annual COP for the 
40

ICS and HPWH combined system can be estimated as the average of these values at 4.5. For the

HPWH alone, the average annual COP can be estimated at 2.0.

Table 19. System COP for Summer Hourly Benchmark

Daily Electric Energy

(kWh)

Daily Thermal Energy

(kWh)

Average Daily

System COP

ICS and HPWH 1.16 7.64 6.58

HPWH alone 3.88 8.27 2.13

Table 20. System COP for Winter Hourly Benchmark

Daily Electric Energy

(kWh)

Daily Thermal Energy

(kWh)

Average Daily

System COP

ICS and HPWH 4.66 11.02 2.37

HPWH alone 5.17 10.08 1.95

3.5.4.3 Effect of Integrated Collector Storage on Heat Pump Water Heater Performance

One concern with a DHW configuration consisting of an ICS and HPWH is that the ICS could 

reduce the performance of the HPWH to the point where the purchase of an HPWH cannot be 

justified. Tables 21 and 22 compare the results of the HPWH COP when the HPWH is operating 

alone and with the ICS in the summer and winter, respectively. The results in Table 21 show 

that, in the summer, the average reduction in HPWH performance for a typical daily hot water 

load is 32%. For the quarter benchmark profile, most of the hot water load can be met with the 

ICS, so HPWH performance drops significantly. This is because the HPWH operates for shorter 

periods and when it does run, the inlet water is preheated by the ICS. In contrast, the double 

benchmark profile shows that the ICS has a small impact on performance. Also, for eveningdominated profiles, the ICS will likely have a more significant impact on HPWH performance. 

As with the quarter benchmark, this is because most of the hot water demand for this profile can 

be met by the ICS. In general, these results show that HPWH performance in a HPWH and ICS 

combined system depends on how much hot water load is supplied by the ICS. When the ICS is 

used to meet a significant part of the demand, the HPWH performance will decrease.

Table 21. Effect of ICS on HPWH Performance During the Summer

Draw Profile

HPWH COP 

(HPWH Operating 

Alone)

HPWH COP 

(HPWH Operating 

With ICS)

Percent

Reduction 

(%)

Hourly benchmark 2.14 1.19 44

Discrete benchmark 2.27 1.52 33

Double benchmark 2.28 2.26 1

Quarter benchmark 1.53 0.34 78

Morning dominated 2.21 1.91 14

Evening dominated 2.36 1.50 36
41

The results in Table 22 show that the performance impact in the winter is less than in the 

summer, primarily because of the lower winter solar resource. The results similarly vary between 

–22% and 12%. In general, when the ICS can meet most of the demand, the HPWH performance

decreases. However, the HPWH will still save energy in this configuration and should be 

considered when a significant portion of the annual hot water load cannot be met with solar.

Table 22. Effect of ICS on HPWH Performance During the Winter

Draw Profile

HPWH COP

(HPWH Operating 

Alone)

HPWH COP 

(HPWH Operating 

With ICS)

Percent

Reduction 

(%)

Hourly benchmark 1.95 2.37 –22

Discrete benchmark 1.86 1.72 8

Double benchmark 2.11 2.17 –3

Quarter benchmark 1.29 1.21 6

Morning dominated 1.44 1.45 –1

Evening dominated 1.61 1.41 12

3.5.4.4 Annual Energy Use and Cost Effectiveness

Table 23 shows the annual energy savings that would be associated with these systems compared 

to a conventional electric resistance water heater. The combined ICS and HPWH system would 

save 78% and the HPWH alone would save 51% energy annually.

Table 23. Annual Energy Savings Compared to an Electric Resistance Water Heater

Annual

Estimated

COP

Annual Estimated

Energy Use 

(kWh)

Energy Savings

%

Electric resistance 1.0 3805 –

ICS and HPWH 4.5 851 78

HPWH alone 2.0 1865 51

Although using the ICS with the HPWH reduces the efficiency of the HPWH, the combined 

assembly still further reduces energy use relative to the HPWH alone. However, the combined 

system may not save enough energy to justify the additional cost. A simple payback period was 

calculated to investigate that question. The initial costs (provided by SMUD) for the HPWH and 

ICS were used, without taking into account rebates and incentives or installation costs. The 40-

gallon CopperHeart ICS cost $4000 and the add-on AirTap HPWH cost $700. The estimates for 

annual energy use given in Table 23 were used to calculate annual operating costs for each 

scenario. Sacramento has a winter electricity rate of $0.0967/kWh and a summer rate of 

$0.1045/kWh. A very crude estimate on annual operating cost was done by dividing the annual 

energy use by 2 (6 months each) and using the summer energy rate for half the year and the 

winter energy rate for the other half. The cost savings for the HPWH alone and ICS + HPWH 
42

system were used to find the simple payback period for each. The electric water heater cost is not 

considered, as it is necessary for all scenarios.

Table 24. Simple Payback Period Relative to an Electric Resistance Water Heater

Initial 

Cost

($)

Annual Estimated

Energy Use 

(kWh)

Annual Estimated

Operating Cost 

($)

Payback 

Period

(Years)

Electric resistance – 3805 383 –

ICS and HPWH 4700 851 84 15.7

HPWH alone 700 1865 188 3.6

From this simple analysis, the additional cost of the add-on HPWH would be recouped in less 

than 4 years; in contrast to nearly 16 years that would take to break even on the cost of the ICS + 

HPWH combined system. This is still within the expected lifetime of the ICS (estimated around 

20 years) but is a few years beyond the expected lifetime for HPWHs (approximately 13 years).

3.5.4.5 Effect of Specific Draw Profiles on System Coefficient of Performance

The average daily COP values in Tables 19 and 20 imply that the DHW system worked well for 

this application. These numbers, however, represent only the performance during the hourly 

benchmark draw profile. Additional draw profiles were tested as previously mentioned and the 

calculated COP values for each of these profiles are listed in Table 25. The COP values are also 

shown graphically in Figure 44.

Table 25. Summary of COP Values for All Draw Profiles

Draw Profile DHW

Configuration

Average Daily

Winter COP

Average Daily

Summer COP

Hourly benchmark HPWH alone 1.95 2.14

Discrete benchmark HPWH alone 1.86 2.27

Double benchmark HPWH alone 2.11 2.28

Quarter benchmark HPWH alone 1.29 1.53

Morning dominated HPWH alone 1.44 2.21

Evening dominated HPWH alone 1.61 2.36

Hourly benchmark ICS and HPWH 2.59 6.37

Discrete benchmark ICS and HPWH 3.02 4.92

Double benchmark ICS and HPWH 2.37 4.96

Quarter benchmark ICS and HPWH 1.58 2.78

Morning dominated ICS and HPWH 2.08 3.79

Evening dominated ICS and HPWH 2.35 3.89
43

Figure 44. Calculated COP values for all draw profiles 

Note: Draw volumes during the winter were inadvertently less than during the summer.

These results show that the COP of the ICS and HPWH combined system is higher for all draw 

profiles compared to the HPWH alone; the greatest benefit of the ICS is seen in the summer. The 

hourly and discrete benchmark tests performed on the HPWH alone are almost the same, 

showing that the method of drawing water from the top of the hour is a good estimate of actual 

hot water use. The double benchmark performed slightly better than the hourly benchmark for 

the HPWH alone, and the quarter benchmark performed noticeably worse than the other draw 

profiles. This verifies that the COP of the system depends on the volume of water drawn. This 

can be attributed to increased tank losses and reduced heat pump performance. When small 

volumes of water are drawn, the water temperature near the heat pump’s condenser remains high, 

which reduces the performance of the direct expansion system.

Figure 45 shows the correlation between the volume of water drawn and the COP. The data 

shown in this graph are for the HPWH operating alone. Clearly, air temperature also affects this 

correlation, as the correlation curves shift according to season. Despite the sparse dataset, the 

data fits are good, with R2

values of 0.9114 and 0.9834 for the summer and winter tests, 

respectively. The lower R2 value for the summer correlation is partly due to a low COP 

calculated for the double volume test. The air temperature in the garage was lower than average 

during this part of the test, which likely caused this low COP value.

The correlation of COP and average daily temperature in the garage is shown in Figure 46. As 

with the previous graph, the data shown here are for the HPWH operating alone. Only days with 

similar draw volumes were used to reduce the variation of results. The correlation to average 

daily air temperature is not strong, but clearly the COP increases with increased air temperature. 

The inlet water temperature is not the same for all data points and tank losses were not removed. 
44

Although this would change the correlation slightly, the data presented here show how an

HPWH is likely to perform in an installed situation.

Figure 45. Correlation between COP and 

volume of water drawn for HPWH operating 

alone

Figure 46. Correlation between COP and 

average daily air temperature in the garage for 

HPWH operating alone

Annual COP values were estimated for each draw profile by averaging the COPs for the summer 

and winter tests. Table 26 and Figure 47 show the annual estimated COPs. The results clearly 

show that the ICS and HPWH combined system is more efficient than operating the HPWH 

alone. On average, the ICS and HPWH together are more efficient than the HPWH alone by 

42%, but range from 35% to 54% depending on the draw profile. Compared to a typical electric 

resistance water heater, low water use households with only an HPWH save about 29% energy 

and households with average hot water use and a combined ICS and HPWH system save about 

78%.

Table 26. Annual Estimated COP Values and Energy Savings Compared to an 

Electric Resistance Water Heater for All Draw Profiles

Draw Profile DHW

Configuration

Annual Estimated

COP

Annual Energy Savings

Versus Electric 

Hourly benchmark HPWH alone 2.05 51%

Discrete benchmark HPWH alone 2.07 52%

Double benchmark HPWH alone 2.19 54%

Quarter benchmark HPWH alone 1.41 29%

Morning dominated HPWH alone 1.83 45%

Evening dominated HPWH alone 1.99 50%

Hourly benchmark ICS and HPWH 4.48 78%

Discrete benchmark ICS and HPWH 3.97 75%

Double benchmark ICS and HPWH 3.66 73%

Quarter benchmark ICS and HPWH 2.18 54%

Morning dominated ICS and HPWH 2.94 66%

Evening dominated ICS and HPWH 3.12 68%
45

Figure 47. Annual estimated COP values for all draw profiles

3.5.4.6 Cooling Effect on Garage

The add-on HPWH provides cooling to the garage when the heat pump is operating. Figure 48 

shows how the operation of the heat pump affected the air temperature immediately downstream 

of the heat pump and in the middle of the garage. The temperature at the exit of the heat pump 

dropped 39°–41°F (4°–5°C) and the average temperature in the garage dropped 33°–34°F (0.5°–

1°C) when the heat pump was on. Based on information provided by the manufacturer, the 

approximate airflow rate of the heat pump is 400 CFM. With this information, the sensible 

cooling capacity of the heat pump can be calculated using Eq. 3.3:

Qthermal = m ∙ Cp ∙ ∆T (3.3)

Where:

m = the mass flow rate of the air

Cp = the specific heat of air

∆T = the difference between the temperature of the air at the inlet and outlet of 

the heat pump. 

The sensible cooling capacity of this unit was calculated to be approximately 4000 Btu/h. 

However, this cooling effect can be observed only when the heat pump is operating, which varies

depending on hot water use.
46

Figure 48. Air temperature variations when HPWH is operating

3.5.4.7 Outlet Temperature

One concern about HPWH technology is its slower recovery rate compared to a conventional 

electric resistance water heater. Using the results from the EF test discussed in Section 2.6, the 

recovery rate was determined to be 41.4°F/h (5.2°C/h). This number is an approximation, as the 

average tank temperature is unknown and the outlet temperature was used for this calculation 

instead. This can be compared to an electric resistance water heater, which has a recovery rate of 

about 72°F/h (22°C/h), which was determined based on an annual simulation run with the 

benchmark draw profile.

A slower recovery rate results in higher energy efficiency but can have a negative impact on the 

temperature of the water that is delivered to the end user. Integrated HPWHs are available that 

have larger storage tanks to mitigate this problem. In the case of the Greenbuilt house, the add-on 

unit was installed on top of a 50-gallon storage tank, which may not be able to provide sufficient 

hot water, depending on household size and water needs. Figure 49 shows the water temperature 

at the outlet of the water heater for the summer and winter discrete benchmark tests. In this 

figure, the spikes in outlet temperature indicate when water draws occurred. These spikes drop 

from the set point temperature (at around 126°F [52°C]) to about 118°F (48°C) before the heat 

pump turns on. This is well above the temperature considered by most people to be “hot” water, 

which is marked on the graph as 104.9°F (40.5°C).
47

Figure 49. End-use water temperature—discrete benchmark profile

This figure also shows the water temperature measured at the faucet in the laundry room. The 

temperature is plotted for a day during both the summer and winter tests and shows that during 

the winter, the temperature at the faucet never reaches 104.9°F (40.5°C). During the summer, the 

temperature at the faucet exceeds 104.9°F (40.5°C) during most draws. The house had no other 

hot water uses, so this is quite a significant temperature drop between the water heater and the 

faucet. One explanation could be heat lost when the pipes travel through the unconditioned attic 

to the laundry room faucet on the other side of the house. Also, a mixing valve, which is used to 

protect the house against extremely hot water coming from the SWH, is located between the 

water heater outlet and the end uses in the house. Usually a temperature range is set with the 

mixing valve, but the colder mains temperature during the winter may have produced slightly 

colder delivered water. The colder water temperature may still be within the set point range, even 

though it causes the actual delivered temperature to be below the expected “hot water” 

temperature. End-use hot water is a potential concern, so the electric resistance elements should 

not be disabled for future installations of this add-on HPWH. Also, when the ICS is installed 

with the thermostatic mixing valve, final hot water temperature around the house should be 

measured to ensure that the set point on the mixing valve is high enough to deliver hot water in 

all seasons.
48

4 Whole-House Simulation Model

Whole-house building models can be used to determine energy use impacts or cost effectiveness 

of new building features or retrofit measures for a particular house and climate. This is the 

primary use for the BEopt software developed by NREL’s Residential Buildings Group, but it 

can also be used for a variety of other purposes. A whole-house model can be compared to actual 

energy bills to verify that systems are working properly or to help identify problems if the home 

is performing below expectations. A whole-house model of the Greenbuilt house was used to 

estimate the energy savings achieved through the deep energy retrofit, described in the next 

section. The rest of the modeling analysis was used to predict energy savings using various space 

conditioning strategies over extended periods. Short-term tests were performed on the space 

conditioning system to capture data points for cooling energy consumption and indoor 

temperature, as described in Section 3.4. Representative days are presented as validation for the 

EnergyPlus model, which was then used to compare energy consumption for various cooling 

strategies over a cooling season, from June through September.

4.1 Annual Retrofit Analysis

A project goal was to verify the success of the deep energy retrofit. Part of that process included 

evaluating the whole-house retrofit energy savings using whole-house BEopt models for the preand post-retrofit house. This analysis also looked at the cost effectiveness of the implemented

retrofit measures.

The geometry of the Greenbuilt house was modeled in BEopt, including wall construction and 

insulation, window area and orientation, and attic and roof construction. Figure 50 shows the 

model of the Greenbuilt house as it appears in BEopt. After the physical layout was defined, the 

characteristics of the house, HVAC equipment, and appliances were added as inputs to the 

model. Local weather data as collected by the weather station on the roof were also incorporated 

into the simulation. The specific input parameters are nearly identical to the list of pre- and postretrofit characteristics listed in Table 35 in Appendix A. However, some features required by the 

model, such as the orientation of the house, did not change during the retrofit (see Table 27).

Figure 50. Greenbuilt house model in BEopt
49

Table 27 Additional Model Inputs

Model Parameter Greenbuilt Input

Front of house orientation East

Proximity of neighbors 15 ft from house

Total floor area 1748 ft2

Garage characteristics Attached, 2 car capacity

Exterior finish Light-colored wood siding

Foundation type Uninsulated slab

Exposed floor 20% exposed flooring

Window area 290 ft2 total window area

Size of eaves 2 ft. width

The general building model was developed using BEopt because of its straightforward user 

interface and seamless generation of the EnergyPlus input file. The more complicated features, 

such as the heat pump model, varying wall insulation levels, and exterior shades on the west wall 

and finished garage were implemented directly in EnergyPlus v7.0 input files. Two separate 

models of the Greenbuilt house were created that incorporate its pre- and post-retrofit

characteristics. Figure 51 shows the annual energy use results from the pre- and post-retrofit 

models. The heating and cooling set points used in both models are consistent the Building 

America recommendations: 71°F (21.7°C) heating set point and 76°F (24.4°C) cooling set point

(Hendron & Engebrecht, 2010). The simulated annual net energy use (total use minus generation 

from PV) is displayed in Figure 51 in terms of site energy use, the same quantity that is shown 

on utility bills. The net annual electricity consumption is 9400 kWh, down from the pre-retrofit 

annual energy use of approximately 22000 kWh. Based on these results, the retrofit measures 

and PV generation reduced net annual energy consumption by 57%.

The properties of the original house were used to create the pre-retrofit model. A home energy 

audit was performed before the retrofit began, which yielded information on infiltration, 

insulation levels, and the efficiency of the installed appliances. This information was used to 

define many of the pre-retrofit model input parameters. Utility bills from before the retrofit were 

unavailable because the house was vacant for an extended period before Greenbuilt Construction 

bought it. The post-retrofit model was created based on post-retrofit audit information and data 

collected. The annual simulation used TMY3 weather data so the modeled annual energy use 

corresponds to a typical year in Sacramento.
50

Figure 51. BEopt results for annual site energy use, before and after the retrofit

Although other energy efficiency research and development projects have made cost

effectiveness a top priority, the Greenbuilt house was intended to showcase new efficiency 

measures. As a result, the project did not constitute the least-cost option for the net energy 

savings. Figure 52, generated by BEopt, shows many retrofit packages that vary in cost and net 

energy savings. The energy costs shown on the vertical axis include the cost of the retrofit 

measures and the cost of energy bills over the course of the year. The cost of the retrofit is 

assumed to be financed with a 5-year, 7% interest rate loan (the default loan option in BEopt) 

with an analysis period of 30 years (Polly & al, 2011). The variables include the amount of 

insulation in the walls, ceiling, and attic, the inclusion of a radiant barrier, the type of windows, 

infiltration level or sealing, amount of ventilation, duct sealing and insulation, appliance 

efficiency, percentage of fluorescent lighting, space conditioning heat pump efficiency, inclusion 

of the HPWH, inclusion of the ICS water heater, and the PV system. The cost and energy savings 

associated with the various combinations of these options are shown as red dots. The original 

house is represented by the pink diamond on the left side of the graph and the post-retrofit house 

is depicted by the green circle in the upper right portion of the graph. The blue curve shows 

minimum energy-related costs achieved by cost-optimal packages.
51

Figure 52. BEopt optimization curve based on energy saving and annual energy cost

The final package of energy efficiency measures at the Greenbuilt house produced good energy 

savings results, but the same energy savings may have been achieved at a reduced cost. Based on 

the optimization curve, the lowest annual cost required at 57% energy savings is about $3500, 

rather than $4130, the actual annual cost of the retrofit at the Greenbuilt house, meaning that the 

retrofit could have cost 15% less without reducing the energy savings. Relative to the actual 

retrofit measures, the least-cost option to achieve 57% energy savings included a reduction in 

insulation, removal of the HPWH, and use of a less efficient space conditioning heat pump. 

However, demonstrating technology such as the HPWH and a high-efficiency heat pump to the 

building community and public was more important than ensuring that the minimum cost point 

was achieved.

This curve shows that energy-related costs can be reduced and energy savings up to 40%

achieved. For the Greenbuilt house and the set of variables considered in this optimization, the 

break-even point occurs around 40% energy savings. To further increase the energy savings, the 

overall energy-related costs are higher than if the house was not improved at all. Much of the 

additional cost comes from the renewable energy generation: the ICS SWH and the PV panels. 

Generally, the most cost-effective way to improve energy efficiency is to reduce overall energy 

use as much as is reasonable before implementing renewable energy measures. That strategy 

allows the renewable energy to make a big impact on final energy use or allows for a smaller and 

less expensive renewable energy system, while keeping the energy savings goal unchanged.

4.2 Comparison of Cooling Strategies

One goal for creating a simulation model for the Greenbuilt house was to enable a comparison of 

energy consumption for the various cooling strategies over an entire cooling season. A field test 

in a single house cannot be used to compare multiple cooling schedules simultaneously. The data 

collected during the space conditioning tests at the Greenbuilt house were used to guide the 

development of a detailed EnergyPlus model. The model from BEopt was further developed in 
52

EnergyPlus v7.0 directly, which allowed more customization of the ventilation and occupancy 

schedules and individual characteristics for items such as specific walls, ceiling areas, and

equipment. Only the HVAC energy use—the sum of the heat pump and air handler energy 

consumption—was used to compare the modeling results to the field data. For each cooling 

strategy, the house was modeled for several days before the day used for data comparison to 

allow for thermal and other transient simulation effects to diminish, as recommended by (Ellis, 

Liesen, & Pedersen, 2003). Each cooling strategy was then modeled over an entire cooling 

season to produce comprehensive and more broadly useful results.

Weather data collected during the space conditioning tests were used to inform the model, which 

used weather data that were collected on the roof, including outdoor dry bulb temperature, RH, 

wind speed, and insolation. Other required weather data, including sky radiation, precipitation,

and wind speed, were taken from the TMY3 weather data for the Sacramento International 

Airport. This hybrid simulated weather file closely reproduced experimental conditions.

The thermostat set points used in the model deviated slightly from the schedules described in 

Section 3.4 because the measured indoor temperatures were always lower than the set points. 

The set points for the model were based on the actual indoor temperature to model the cooling 

energy. There was no temperature sensor next to the thermostat and the EnergyPlus model 

treated the living space as a single thermal zone, so the measured living room temperature was 

used to determine the modeled thermostat set point as it was the closest temperature sensor to the 

thermostat. The set point was chosen to be equal to the average living room temperature during 

the first two hours that the heat pump was actively cooling the space. If cooling occurred over a 

shorter period, such as with the advanced precooling schedule, the average living room 

temperature during that time was used as the set point. Figures 53 through 56 compare the 

temperature measured in the living room to the indoor temperature from the EnergyPlus 

simulation, which is assumed to the same throughout the entire living space. 

4.2.1 Validation of EnergyPlus Model

Five of the six space conditioning strategies tested were modeled: baseline, simple precooling, 

advanced precooling, advanced precooling + shades (the schedule used during the heat storm), 

and shade control. The shades on the west side of the house remained closed during the wholehouse fan test and the fan did not appear to improve the results from the shade control scenario, 

so the whole-house fan test was not modeled. The details for each schedule are given in 

Section 3.4. To verify the EnergyPlus model, each simulated cooling schedule was compared to 

the data from the Greenbuilt house for at least five days (more if possible). Each strategy was 

then modeled over the entire summer to compare afternoon HVAC energy consumption and 

peak demand. The modeling results and field test data presented here include the living room 

temperature and hourly HVAC energy (heat pump and air handler energy use). The same 

representative day used in the experimental results (Section 3.4) was also used when comparing 

data to the modeling results. All the other days of data in each schedule are similarly matched by 

the model. 

An hourly comparison of cooling energy consumption data and modeled energy consumption 

was used to ensure that the model was reliable before comparing the modeled schedules over the 

cooling season. This process allowed the NREL researchers to identify and correct internal loads 

and ventilation schedules that deviated from the Building America Simulation Protocols and 
53

building envelope characterizes that are not allowed in BEopt. Examples of modifications made 

to the EnergyPlus model include:

• West wall insulation increased to R-15.

• 1 in. of expanded polystyrene and radiant barrier added to knee wall.

• Ceiling and drywalls added to garage.

• Exterior shades added to the windows on the west wall for the shade test.

• Exhaust fans, natural ventilation and vacation periods were eliminated.

• Latent loads were eliminated.

• Internal gain (lights and heaters) schedules were modified to represent the simulated 

occupancy schedule used at the Greenbuilt house.

4.2.1.1 Baseline Schedule

Figure 53 shows the hourly energy use and living room temperature for the baseline cooling 

strategy for a representative day in July. The predicted hourly profile captures the important 

characteristics for the cooling energy. The daily cooling energy uses were well matched, with the 

model coming within 1% of data for this day (see summary in Table 28). The simulated peak 

energy use was within 3.5% of the actual peak load (peak hours highlighted in light blue in 

Figure 53. Some physical properties, including internal thermal mass, ground coupling, and 

airflow patterns, may not be accurately represented in the model, leading to these discrepancies. 

Some assumptions for these are built into the EnergyPlus model and we did not have sufficient 

data to properly specify these properties. Any of these complex effects could impact the modeled 

indoor air temperature, which is the primary driver for heat pump operation. Overall, despite 

some small differences, the model for the baseline cooling schedule is a good representation of 

the data gathered at the Greenbuilt house.
54

Figure 53. Baseline HVAC energy and living room temperature comparison between data from the 

Greenbuilt house and the EnergyPlus model 

Peak hours are indicated by the blue band.

Table 28. Single Day Comparison of Baseline HVAC Energy Consumption for Data From the 

Greenbuilt House and the EnergyPlus Simulation

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

On Peak

4:00 p.m.–7:00 p.m. 

(kWh)

Field data 8.68 1.52 10.20 4.25

EnergyPlus results 8.50 1.60 10.10 4.40

% error in model –2.1 5.2 –1.0 3.5

4.2.1.2 Simple Precooling

Figure 54 and Table 29 compare the representative day of data from the simple precooling 

schedule to the same simulation day in terms of HVAC energy use and indoor temperature. The 

important features in the total HVAC energy use data are the large spike in energy use when the 

heat pump is initially turned on in the morning followed by a large decrease in energy use after 

the new set point temperature has been reached and the subsequent increase in cooling energy in 

early afternoon as the outdoor temperature and cooling load increase. EnergyPlus captures these 

features qualitatively but does not quite match peak power. When the measured temperature 

exceeds the predicted temperature, the measured energy use is lower than the predicted use and 

vice versa. The difference between the measured and simulated temperatures from 10:00 a.m. to 

11:00 a.m. is consistent with the difference in cooling energy use in Figure 54. Overall, the total 

daily HVAC energy predicted by the simulation matches the data within 3.5%, so even though 

the shape of the hourly energy consumption is slightly different, the model is a good surrogate 

for the data from the Greenbuilt house.
55

Figure 54. Simple Precooling HVAC energy and living room temperature comparison between data 

from the Greenbuilt house and the EnergyPlus model

Peak hours are indicated by the blue band.

Table 29. Single Day Comparison of Simple Precooling HVAC Energy Consumption for 

Data From the Greenbuilt House and the EnergyPlus Simulation

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

On Peak

4:00 p.m.–7:00 p.m.

(kWh)

Field data 5.84 1.17 7.00 0.04

EnergyPlus results 5.63 1.13 6.76 0.00

% error in model -3.6 –3.4 –3.4 –1

1 Peak energy use is nearly 0, so reporting model error would be misleading.

4.2.1.3 Advanced Precooling

Figure 55 and Table 30 compare a day of the EnergyPlus simulation for the advanced precooling 

schedule to a day of data collected at the Greenbuilt house. This cooling strategy is similar to the

simple precooling but with an additional cooling period in the evening. The heat pump is also

allowed to run overnight, if needed. The multiple rapid changes in the temperature set points and 

the associated thermal transients seen in living room temperature profile in Figure 55 make this a 

challenging schedule to model accurately. The important characteristics of the measured data are 

reflected in EnergyPlus simulation results, but the predicted energy use at the peaks (10:00 a.m.

and 3:00 p.m.) do not match the magnitude seen in the actual data. The total daily energy use (as 

predicted by the model) matches the data within 1%. The cooling load during peak hours is 

slightly higher than was seen in the data, but the peak load is still essentially eliminated.
56

Figure 55. Advanced precooling HVAC energy and living room temperature comparison between 

data from the Greenbuilt house and the EnergyPlus model

Peak hours are indicated by the blue band.

Table 30. Single Day Comparison of Advanced Precooling HVAC Energy Consumption for 

Data From the Greenbuilt House and the EnergyPlus Simulation

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

On Peak

4:00 p.m.–7:00 p.m. 

(kWh)

Field data 9.09 1.69 10.78 0.07

EnergyPlus results 8.89 1.83 10.72 0.23

% error in model –2.2 8.3 -0.6 –1

1 Peak energy use is nearly 0, so reporting model error would be misleading.

4.2.1.4 Advanced Precooling + Shade Control

During a heat storm, the advanced precooling schedule was implemented while all the westfacing windows were shaded by the external shades. The initial peak in cooling energy at 10:00 

a.m. is about 20% smaller in the model relative to the data, which is consistent with the previous 

cooling strategies tests. Overall, the daily cooling load is still well predicted by the model. A

small cooling load is predicted during the afternoon peak period, but the schedule still achieves 

an 85% reduction from the baseline peak cooling load.
57

Figure 56. Advanced precooling + shade control HVAC energy and living room temperature 

comparison between data from the Greenbuilt house and the EnergyPlus model

Peak hours are indicated by the blue band.

Table 31. Single Day Comparison of Advanced Precooling + Shade Control HVAC Energy 

Consumption for Data From the Greenbuilt House and the EnergyPlus Simulation

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

On Peak

4:00 p.m.–7:00 p.m. 

(kWh)

Field data 15.73 2.38 18.11 0.08

EnergyPlus results 15.34 2.82 18.16 0.72

% error in model -2.5 18.5 0.3 –1

1 Peak energy use is nearly 0, so reporting model error would be misleading.

4.2.1.5 Shade Control

The shade control test was performed with all the west-facing windows and glass doors shaded 

with externally mounted shades and an external awning. The shades in the EnergyPlus 

simulation were set at 24% solar transmittance, based on the specifications for the installed 

shades. The cooling set point was constant throughout the day and was the same as the baseline 

cooling schedule. Even though the maximum simulated and actual cooling loads at 6:00 p.m. are 

well matched (within 6%), the shape of the predicted hourly cooling load is much sharper than 

the data collected at the Greenbuilt house. The simulated energy use for the shade control test is 

similar to the baseline schedule simulation. The only difference between these two schedules is 

the external, west-facing shades in the shade control test, which should cut the magnitude of the 

cooling load. However, the actual cooling load during the shade control test had a different shape 

than the baseline schedule, with a lower, wider energy use profile. The model did not capture this 

shape, and the cause is not well understood. Thus, the simulation underpredicts the daily cooling 

load and the afternoon peak load by nearly 15%. 
58

EnergyPlus needs several shade properties besides solar transmittance, including reflectance, 

transmittance, and reflectance in the visible range, thermal emissivity, thermal transmittance, 

thickness, and conductivity. The manufacturer specified none of these, so reasonable values were 

used, but considerable uncertainty remains. The energy use predictions were highly sensitive to 

the shade properties and because they were not known, the model for this cooling schedule has 

the largest error relative to the data collected at the Greenbuilt house.

Figure 57. Shade control HVAC energy and living room temperature comparison between data 

from the Greenbuilt house and the EnergyPlus model

Peak hours are indicated by the blue band.

Table 32. Single Day Comparison of Shade Control HVAC Energy Consumption for 

Data From the Greenbuilt House and the EnergyPlus Simulation

Heat Pump 

Energy

(kWh)

Air Handler 

Energy

(kWh)

Total HVAC 

Energy

(kWh)

On Peak

4:00 p.m.–7:00 p.m.

(kWh)

Field data 5.95 1.16 7.11 2.70

EnergyPlus results 5.19 0.86 6.05 2.30

% error in model –12.8 –25.9 –14.9 –14.8

4.2.2 Whole Summer Comparisons

NREL researchers compared measured data to the EnergyPlus model for representative days to 

demonstrate the model’s ability to accurately predict cooling energy use. All the simulated 

cooling schedules matched the corresponding data within 3.5%, except for the shade control

simulation, which underpredicted the HVAC load by 15%.
59

The main goal for the validated EnergyPlus model was to extrapolate the cooling schedules over 

an entire cooling season (June through September) and determine how the modeled HVAC 

energy consumption and peak cooling load compare. This is an important step to ensure that the 

limited results from the Greenbuilt house can be more broadly applied. Each cooling schedule 

was modeled over an entire summer; all cases used the same weather file. The simulated 

summer-long HVAC energy uses are compared in Table 33. The baseline strategy is used as a 

reference to compare summer-long energy savings (or gains) for the other strategies. Based on 

the simulation results, the shade control and simple precooling strategies resulted in the lowest 

energy use. In contrast, advanced precooling increased the HVAC energy use by 22% relative to 

the baseline case and the advanced precooling + shades schedule resulted in about a 5% increase. 

However, there is more uncertainty in the energy savings predicted for the shade control 

simulation because the single day comparison between the model and the data was not as well

matched.

Table 33. June Through September Total Cooling Energy Comparison 

Using EnergyPlus Model for Five Cooling Strategies

Cooling Schedule

Total Summer 

Cooling Energy 

(kWh)

Difference From

Baseline 

(%)

Baseline 943.4 –

Simple precooling 886.4 –6.0

Advanced precooling 1152.5 22.2

Advanced precooling + shades 986.7 4.6

Shade control 880.3 –6.7

Finding ways to shift peak cooling energy is a priority for utilities, especially during the hottest 

days of the year. The three-day heat storm in late August was the hottest series of days during the 

summer of 2010; thus, these should represent the days with the highest peak cooling load. The 

peak cooling energy predicted by the EnergyPlus model for each schedule during those days is 

summarized in Table 34. The sum of the cooling energy between 4:00 p.m. and 7:00 p.m. for 

those three days is presented; the baseline schedule is used for comparison with the other 

strategies.

Table 34. On Peak Cooling Energy Comparison During August Heat Storm 

Using EnergyPlus Model for Five Cooling Strategies

Cooling Schedule

On Peak Energy

4:00 p.m.–7:00 p.m. 

(kWh)

Difference From

Baseline 

(%)

Baseline 16.1 –

Simple precooling 0.0 –100

Advanced precooling 2.5 –84.2

Advanced precooling + shades 1.1 –93.0

Shade control 13.8 –14.2
60

All the schedules that included precooling nearly eliminated any peak cooling load during the 

heat storm. Even though the advanced precooling schedule and the advanced precooling + shade 

control strategy both increased summer-long cooling energy use, a TOU pricing schedule could 

make these schedules cost effective for homeowners. Overall, the advanced precooling + shade 

control schedule increases the total cooling load by only about 5% and successfully shifts the 

peak cooling load to earlier in the day, making it potentially the best strategy for homeowners 

under TOU pricing. The simple precooling also eliminates the peak cooling load and reduces the 

overall cooling load, but the cooling schedule is so severe that it may not be comfortable for an 

entire summer.

4.3 Modeling Conclusions

An EnergyPlus model was developed to compare five cooling strategies over an entire summer;

each schedule was applied to the same summer-long weather file. The same cooling schedules 

were first tested in the Greenbuilt house and the data from those tests were used to validate the 

EnergyPlus model. Several days of data were compared to the same days of the EnergyPlus 

model for all the cooling schedules to ensure good agreement. The daily cooling energy 

predicted by the models matched the actual data within 4% for all schedules except for the shade 

control model, which underpredicted the cooling load by 15%.

The summer-long modeling results for total cooling load and peak cooling load were compared 

to the same results for the baseline schedule. The shade control schedule resulted in the largest 

overall HVAC energy reduction, nearly 7%, and reduced the peak cooling load during the heat 

storm by 14%. The simple precooling schedule reduced the total summer cooling load by about 

6% and completely eliminated the peak cooling load. The advanced precooling + shade control 

strategy increased the overall cooling load by 5% and shifted nearly the entire peak load out of 

the peak time during the heat storm. The advanced precooling schedule resulted in the largest 

increase (22%) in overall HVAC energy use, but like the other precooling schedules, also 

significantly reduced the peak cooling load.

The ability to model these complex space conditioning schedules is important for maximizing

the benefit of TOU pricing for the utility and the customers. As TOU pricing becomes more 

common, this type of modeling could be used to find the optimal precooling schedule for a 

specific house. A specific TOU pricing schedule saves money, and the utility keeps its peak 

loads manageable. However, further work is needed to determine the data and modeling needs

before that type of optimization can easily be implemented. Rapid changes in cooling load, such 

as large changes in thermostat set point, are difficult to accurately reproduce in a model even 

though the steady-state periods can consistently be simulated. Determining all the physical 

properties for internal or external shades is nearly impossible without additional information 

from the manufacturer. It is unclear which properties are the most important to know fully. 

Further work is needed to understand the sensitivity of the model to difficult-to-measure 

parameters such as thermal mass, ground coupling, interior temperature variations, and room-toroom airflow patterns.
61

5 Conclusions

5.1 Lessons Learned and Future Work

This project was successful in many ways, but there is room to improve similar future projects. 

Recommendations follow, grouped by topic.

5.1.1 Retrofit Project

One critical piece of information missing from this project was the pre-retrofit utility bills. 

Modeling was done based on the characteristics of the house, the initial energy audit, and the 

blower door test done before the retrofit, but it would have been better to have actual utility bills 

(and thus, actual electricity use). Pre-retrofit data should be collected for any retrofit project, 

regardless of whether it will be used as a laboratory house. If pre-retrofit utility bills cannot be 

collected, pre-retrofit monitoring should be performed to ensure that the impact of the retrofit 

can be well characterized. Because the Greenbuilt house was empty for some time before the 

retrofit, pre-retrofit monitoring would have required simulated occupancy during the monitoring 

period.

NREL’s involvement with the Greenbuilt house began after the retrofit construction was 

complete. Modeling the home in BEopt to determine the most cost-effective retrofit measures 

before starting the project may have produced even better construction cost and whole-house

energy saving results.

5.1.2 Laboratory House Implementation

The Greenbuilt house is about 1000 miles from NREL, so researchers had difficulty making

changes to equipment settings or sensor locations. Everything that was installed had to operate 

autonomously for an indefinite period, which was limiting. Having a closer laboratory house 

would be optimal, but having a dedicated local assistant would have been useful. Ideally, the 

assistant would be familiar with the house and all the monitoring and control systems installed 

by NREL and would make regularly scheduled and impromptu trips to the house.

One consequence of the distance between NREL and the Greenbuilt house was that latent loads 

were not included in the simulated occupancy schedule. People, appliances, and hot water draws 

contribute to latent loads, and have an impact on the cooling load requirements. However, 

despite several attempts, NREL researchers were not able to install a reliable and safe means of 

introducing latent heat to the house. With additional testing time, it should be possible to reliably 

deliver latent loads remotely, but it was not feasible in the project time frame. Future laboratory

house projects should include latent loads in their occupancy simulation schedule as a priority if 

space conditioning is a focus of the project.

5.1.3 Enhancing Results Through Modeling

Creating a robust simulation model for a home used in a field test, especially when the home 

contains extensive monitoring equipment, is critical to ensuring that the results are more broadly 

applicable. If development of the model had started at the beginning of the project, the initial 

results could have been compared to energy consumption data while the project was ongoing, 

allowing the model to be perfected before the tests were complete. This would have highlighted 

some additional sensor needs, such a temperature sensor next to the thermostat and more heat 
62

flux sensors around the house, possibly before any space conditioning experiments had been 

conducted. Even simple information, such as the height of the cathedral ceilings in the living 

room, would have been helpful when building the EnergyPlus model and could not be obtained 

months after the house was sold.

The difference in temperature measurements taken in five rooms around the house (living room, 

den, master bedroom, bedroom 2, and bedroom 3) revealed that treating the house as a single 

zone with a constant temperature is not accurate. The temperature variation between rooms was 

as high as 37.4°F (3°C) and the predicted HVAC cooling loads were sensitive to even smaller 

temperature differences. The Greenbuilt house had many large west-facing windows that 

probably caused the large temperature variation between the rooms on the west side and those on 

the east and north sides. Especially when the variation between rooms appears to have an 

obvious cause, the ability to include multiple zones in an EnergyPlus model should improve the 

agreement between data and modeling results for future projects.

5.2 Summary of Results

The Greenbuilt house underwent extensive renovations as a part of SMUD’s EERD project and 

was used to show builders and homeowners what is possible with an energy efficiency 

renovation. Greenbuilt Construction was the contractor for the design and completion of all the 

retrofit measures. After the renovation was complete, NREL performed several short-term tests 

to ensure that the major systems installed during the retrofit were performing as expected. The 

systems evaluated included the space conditioning heat pump, the air handler and ducts, the 

HPWH, the ICS SWH, and the PV array. The duct system required attention after the test 

revealed that the master bedroom and bathroom were receiving insufficient airflow. Some small 

modifications to the ducts in the attic corrected the problem. Otherwise, all major systems 

performed within an acceptable range.

NREL was also given the opportunity to use the Greenbuilt house as a laboratory house for a 

year. The two main systems of interest were the space conditioning system (in cooling mode, 

specifically) and the DHW system. SMUD and NREL worked together to develop a mutually 

beneficial test plan. Experiments done on the HVAC system during the summer were aimed at 

finding ways to shift the peak cooling load and reduce the overall cooling load during a summer 

day. Of the scenarios tested, the use of external shades was the most effective means for cutting 

the daily cooling load. The simple precooling schedule was the most effective way of shifting the 

peak load and reduced the total cooling load relative to the baseline HVAC schedule. However, 

the advanced precooling + shades schedule may be a better way to shift peak cooling load and 

maintain indoor comfort during very hot days.

A similar trend was observed when the EnergyPlus model was used to compare the cooling 

strategies over the entire summer. The shade control test produced the largest total HVAC 

energy savings relative to the baseline schedule, followed closely by the simple precooling case. 

All schedules that included precooling nearly eliminated the peak cooling load, but the advanced 

precooling + shades and the advanced precooling schedules increased the summer-long cooling 

energy by 5% and 22%, respectively.

In the end, the “best” cooling strategy will likely depend heavily on a utility’s pricing schedule. 

If a TOU price schedule is implemented that dramatically increases the cost of peak electricity, 
63

the precooling schedules will likely produce the best results. The advanced precooling + shades 

scenario slightly increases the total HVAC use, but eliminates the peak load. Coupled with 

pricing that makes midday electricity use cheaper, this schedule should reduce energy bills for 

consumers and decrease the stress on the utility during peak hours. The advanced precooling + 

shades schedule may be a better option than the simple precooling schedule because the home 

receives additional cooling in the evening, which should make the occupants more comfortable, 

especially on very hot days. If TOU pricing is not in place, simply drawing shades during the day 

appears to be the most effective way of reducing cooling energy use, though it is unclear how 

these results would translate to homes with internal shades. Only the west-facing windows were 

shaded at the Greenbuilt house because it has no south-facing windows. In most houses, east, 

south, and west-facing windows should be shaded during the day to reduce the cooling load. 

Anyone with a programmable thermostat can implement a precooling schedule, but switching 

between cooling schedules is often cumbersome. However, most homes have some form of 

window shades (though most are internal), so it would be feasible for a homeowner to close the 

shades during summer days.

The hot water system was tested in two configurations: the HPWH alone and the ICS SWH

paired with the HPWH. Six hot water draw profiles were imposed on the hot water system to test 

their effects on performance. In both configurations, the standard Building America benchmark 

draw profile, the benchmark profile segregated into smaller draws, double benchmark profile, 

quarter benchmark profile, and morning- and evening-dominated profiles were tested. With the 

HPWH operating alone in the summer, all profiles produced a COP around 2.2, except the

quarter benchmark that had a COP of 1.6. During the summer, the combination of ICS and 

HPWH resulted in a greater COP variability in the draw profiles. The standard benchmark 

profile had the highest COP (6.4), followed by the discrete benchmark and the double 

benchmark, which both had COPs of 4.9. The morning- and evening-dominated profiles were 

actually very similar for the ICS + HPWH system, with COPs of 3.8 and 3.9, respectively. Just 

as with the HPWH operating alone, the quarter benchmark profile produced the lowest COP 

(2.8) for the combined system. In terms of impact on summer peak load, the ICS + HPWH 

combined system reduced the peak load to zero and the HPWH alone reduced the peak load by 

56% relative to a standard electric water heater running the hourly benchmark profile. Other 

interesting observations from the DHW tests include the ICS system performance during summer 

and winter. In the summer, as previously stated, adding the ICS to the HPWH had a large impact 

on system efficiency. However, during the winter, the increase in COP due to the addition of the 

ICS was much less significant. In fact, the ICS had a negative impact on the hot water system at 

some times during the winter. However, over the course of the year, either configuration of the 

DHW system would save homeowners at least 50% of their water heating electricity, compared 

to a standard electric resistance water heater.

5.3 Future Work

The results of these experiments performed on the space conditioning and DHW systems should 

be incorporated into future field tests in similar climates. The most effective cooling schedules 

should be used in other homes and compared to other cooling strategies. The schedules tested 

here cannot be considered comprehensive, so other innovative cooling schedules should also be 

considered. It will be important to verify that the conclusions drawn from this research are not 

unique. This same process should be repeated in other climate regions to determine viable 
64

cooling strategies. The EnergyPlus model effectively represented the results seen in the 

Greenbuilt house, but a great deal of time was needed to create a model with the required detail. 

The model was built with EnergyPlus because BEopt does not allow some of the complex 

geometries seen in the Greenbuilt house, such as one wall having more insulation than the others. 

BEopt also has some built-in assumptions that control schedules for internal loads, occupancy, 

lighting, and ventilation, all of which had to be adjusted to fit the simulated occupancy schedule. 

Modeling could play a large role in optimizing cooling schedules that can reduce overall cooling 

loads and shift the peak to earlier in the day, but more complex space conditioning schedules 

require detailed home models to ensure accurate results.

On the DHW side, the Greenbuilt house results could be used for future research in a number of 

ways. HPWH technology has moved away from the add-on type of system used here toward 

integrated HPWHs with backup electric resistance elements that are built as a single unit. The 

performance of HPWHs in various climates and in conditioned and unconditioned spaces is 

currently being investigated by a number of field tests. Additionally, ducting the outlet air to 

either put it to better use when cooling is required or rejecting the cold air to the outside during 

the heating season could maximize the benefits of HPWHs, but the cost and feasibility of that 

configuration are not well understood. A field test to investigate that question would be a 

valuable addition to HPWH research. The developmental focus for solar thermal systems is 

reducing costs for colder climate systems. As new products enter the marketplace to address this 

need, field tests should be conducted in cold climates. These systems will likely be paired with 

gas water heaters for backup water heating. The work done at the Greenbuilt house will 

hopefully be used to advance the performance and acceptance of deep energy retrofits and the 

advanced energy efficiency technologies installed in the process.
65

6 References

ASHRAE. (2005). 2005 ASHRAE Fundamentals Handbook. Atlanta, GA: American Society of 

Heating, Refrigeration and Air-Conditioning Engineers, Inc.

Barker, G., & Norton, P. (2003, May). Building America System Performance Test Practices: 

Part 1 - Photovoltaic Systems , NREL/TP-550-30301. Golden, CO: National Renewable Energy 

Laboratory.

Cameron, C., Boyson, W., & Riley, D. (2008). Comparison of PV System Performance-Model 

Predictions with Measured PV System Performance. 33rd IEEE Photovoltaic Specialists 

Conference. Sandia National Laboratories.

CEC. (2008). 2008 Building Energy Efficiency Standards for Residential and Commercial 

Buildings. California Code of Regulations, Title 24, Part 1 , CEC-400-2008-001-CMF.

Denholm, P. (2007). The Technical Potential of Solar Water Heating to Reduce Fossil Fuel Use 

and Greenhouse Gas Emissions in the United States , NREL/TP-640-41157. Golden, CO: 

National Renewable Energy Laboratory.

DOE. (1998). 10 CFR Part 430 Energy Conservation Program for Consumer Products: Test 

Procedure for Water Heaters . Federal Register Volume 63, Number 90.

Ellis, P., Liesen, R., & Pedersen, C. (2003). EnergyPlus Experimental Data Validation Work: 

Development and Validation of the Unvented Trombe Wall Model and Other Heat Balance 

Components. CERL , Final Report DACA42-01-D-0004.

Hendron, R., & Engebrecht, C. (2010). Building America Research Benchmark Definition , 

NREL/TP-550-47246. Golden, CO: National Renewable Energy Laboratory.

Hendron, R., Burch, J., & Barker, G. (2010, August). Tool for Generating Realistic Residential 

Hot Water Event Schedules , NREL/TP-550-47685. Golden, CO: National Renewable Energy 

Laboratory.

ORNL, & PNNL. (2010). Guide to Determining Climate Regions by County. Volume 7.1 

Building America Best Practices Guide.

Polly, B., & al, e. (2011). Method for Determining Optimal Residential Energy Efficiency 

Measures Packages , NREL/TP-5500-50572. Golden, CO: National Renewable Energy Laboratory.
66

Appendix A Summary of Retrofit Measures

Table 35. List of Retrofit Measures at the Greenbuilt House and the Systems That Were Replaced

System Pre-Retrofit Post-Retrofit

Thermal Envelope

Ceilings R-19 insulation R-42 blown-in cellulose

Roofing Asphalt shingles Asphalt shingles with radiant barrier

Band joist areas and attic 

perimeter No insulation Rake and “pack” insulation to perimeter

Knee walls R-11 insulation 1-in. metallic-reflective face

rigid foam (R-6) over R-15 batts

West wall R-11 insulation R-15 blown-in cellulose insulation

Infiltration 3280 CFM @ 50 Pa

15.5 ACH @ 50 Pa

946 CFM @ 50 Pa

3.6 ACH @ 50 Pa

Windows

Windows and sliding glass 

doors

Al. frame, single pane 

U-value ≈ 1.07, 

SHGC ≈ 0.7

Vinyl frame, dual pane

Argon filled, low-emittance coating

U-value = 0.28 - 0.29, SHGC = 0.19 - 0.22

Air sealing None Air Sealing to comply with

ENERGY STAR thermal bypass checklist

Mechanical Systems

Space conditioning heat 

pump

HSPF 7.75

SEER 13/EER 10

HSPF 9.75

SEER 16/EER 13

Ducts R-2 insulation R-6 insulation, “tight” - 4.5% leakage 

@ 25 Pa

Thermostat Manual Programmable, communicating

Whole-house fan None 2-speed AirScape 2.5 whole-house fan

Water heating Electric Storage Tank

EF ∼ 0.97

Storage tank with add-on HPWH, 

COP of 2.11

Ventilation

Spot ventilation None ENERGY STAR bathroom low sone fans 

with timer controls

Lighting and Appliances

Lighting Incandescent bulbs 100% hardwired ENERGY STAR CFL 

bulbs, LED fixtures in master bath

Ceiling fans Incandescent bulbs ENERGY STAR rated with CFL bulbs

Solar tubes None ENERGY STAR rated, in bathrooms

Refrigerator Existing Tier 2 ENERGY STAR

Dishwasher Existing Tier 2 ENERGY STAR (EF = .69)

West-facing shades None Motorized awning and shades
67

System Pre-Retrofit Post-Retrofit

Whole-house controls None Control4 home area network

Renewable Energy

SWH None 40-gal ICS

50% solar fraction

PV None 2.3 kW AC PV

Home Energy Rating

HERS Index 181.5 77.9 (57% improvement)

Green Point Rating Unknown 134
68

Appendix B List of Instrumentation

Table 36. List of Data Logger Equipment and Accessories Used at the Greenbuilt House

Data Logger and Accessories

Equipment Purpose Make and Model Notes

Measurement and controls 

data logger Campbell Scientific CR1000 All other peripherals connect to 

CR1000

Solid-state multiplexer Campbell Scientific AM25T Used to read thermocouples

Voltage pulse count module Campbell Scientific SDM-SW8A Used to collect all power 

measurements

Relay controller Campbell Scientific

SDM-CD16AC On/off control for relays

Remote data collection Campbell Scientific CR206

Two of these modules were used

to collect data from rooftop 

sensors

Backup power supply for 

CR1000 Campbell Scientific PS12 12-volt battery

Radio receiver for CR206 

modules Campbell Scientific RF 401 Radio

Wireless modem Sierra Wireless Raven XT Used for remote access to data

logger

Antenna for wireless modem Campbell Scientific

Cellular Antenna 800 MZ
69

Table 37. List of Sensors and Controls Equipment Used at Greenbuilt

Sensors and Controls Equipment

Measured Quantity Make and Model Notes

Air temperature sensors 

(center of room)

Omega TT-T-24S-TWSH-SLE

T type thermocouple wire

Twisted, shielded wire

and rated for special limits of error

Air temperature sensors

(wall-mounted)

Omega EWS-TC-T

Environmental Wall Mount 

Sensor

T-type thermocouple in housing

Surface-mounted 

thermistors Omega SA1-TH-44006-120-T

Surface-mounted thermistors for

PV surface temperature 

measurements

Water temperature sensors Omega TMQSS-062U-6 T type thermocouples,

rated for special limits of error.

Humidity sensors Vaisala Intercap HMP50

Three were installed inside and

one outside for humidity 

measurement

Pulse output flow meter Omega FTB4605 Used to measure flow rate of hot

water drawn from water heater tank

Ultra sensitive heat flux 

plates Hukseflux HFP03 Four were installed in house

to measure heat flux through the slab

Pulse output power 

transducers

Continental Control Systems

WNB-3Y-208-P3

Used to measure power consumption

of appliances, PV and whole-house

Current transformers Continental Control Systems

CTS-075-030, CTS-075-100

100 A CT used for whole-house

power

30 A CTs used for everything else

Pyranometers for

irradiance measurements Campbell Scientific CS300-L50

Two were installed on roof:

Horizontal irradiance and irradiance

in plane of PV and ICS systems

Pyrgeometer for

infrared measurement Eppley model PIR One installed on roof

in plane of PV and ICS systems

Barometric pressure sensor Vaisala PTB110 Outdoor pressure measurement

Anemometer R M Young 03002-L

Wind Sentry Anemometer Rooftop Wind speed measurement

Solenoid valves Asco Redhat II 8210G002

1/2” pipe, normally closed

Controlled water flow out of water 

heater

Space heaters 930 W each Used to introduce sensible load to 

building

Solid-state relays Crydom CSD2425 Relays for space heaters and 

solenoid valves
70

Table 38. List of Control4 equipment at the Greenbuilt house

Control4 Equipment

Equipment Description Control4 

Product ID Notes

Home controller C4-HC200-E-B Main controller for all other components

Composer software C4-CHE-XXX Used to program control system remotely

System remote control C4-SR150-Z-B Remote used with TV to program controller on site

Wireless thermostat CCZ-RCR1-B Programmable thermostat

Wireless 3 button keypad KPZ-3B1-W Installed at the front door: Home, Green, and Away 

buttons

Wireless outlet switch LOZ-5S1-W Able to control power to plug loads

Wireless switch LSZ-102-W Allows for remote control of light switches

Wireless dimmer switch LDZ-102-W Allows for remote control of dimmer switches
71

Appendix C Air Temperature Measurement Location

Wall-Mounted Versus Center of Room Thermocouples

Historically, interior air temperature measurements have been made with thermocouples that are 

housed in an aspirated radiant barrier enclosure that is suspended in the middle of each room. 

(See Figure 58 for an example of this type of installation.) This has been established as the best 

practice for interior temperature measurements, but the installation is labor intensive and too 

awkward for field tests involving occupied homes. When instrumenting an occupied house, 

thermocouples are mounted on the walls instead of hung in the center of the room (also shown in 

Figure 58). However, the two sensor locations have never been compared to see if there are 

significant benefits to the center of the room location or if the wall-mounted thermocouples are 

adequate. 

Greenbuilt provided an opportunity to investigate this question. Another motivation for this 

comparison comes from the structure of our modeling software. The internal temperature used by 

BEopt is air temperature, rather than wall temperature. The center of the room measurement is 

the ideal data source for the model, but occupied field tests are more common and so most often, 

the wall temperatures are used to inform the model. Another more practical consideration is that 

thermostats for space conditioning control are wall mounted, but occupants feel the air 

temperature rather than wall temperature. Are there large enough differences to justify the 

thermostat manufacturers making specific changes to their product to address the difference?

Figure 58. Thermocouples for interior temperature measurements 

NREL/PIX 19621, Credit: Lieko Earle

Each of the five rooms where air temperature was measured had thermocouples installed in the 

center of the room and on a wall. The thermocouples were simple welded T-type thermocouple 

wire, taken from the same spool of wire, meaning that there should be ≤32°F (0.1°C) difference 
72

between the temperature signals if they were next to each other. In reality, a larger difference in 

temperature was measured between the wall-mounted and center of the room thermocouples of 

32.4°–32.7°F (0.2°–0.4°C on average, depending on the room and whether heating or cooling 

was occurring. The thermal mass of the walls appears to be the main source of the discrepancy. 

The center of the room thermocouple is more sensitive to temperature changes, especially when 

cooling or heating are actively occurring. As expected, the wall-mounted thermocouples are 

slower to respond to temperature changes because the thermal mass of the wall cannot change as 

fast as the air temperature. An example of this is shown in Figure 59, which shows the wallmounted thermocouple and the thermocouple suspended in the center of the room for the living 

room during one day of the advanced precooling schedule. 

Because this example features a thermostat set point that changes several times throughout the 

day, the differences between the center and wall temperature measurements are amplified. When 

the precooling period begins, the difference between the two thermocouples jumps to 32.3°F 

(0.7°C), but over the course of the day, the average temperature difference is only 32.5°F 

(0.25°C). For tests that maintained a steady set point through the day, both the maximum and 

daily average temperature differences were smaller.

Figure 59. Temperature differences between wall-mounted thermocouple and center of the room 

thermocouple during the advanced precooling schedule. The heat pump energy is shown to 

indicate when cooling is actively occurring.

For some perspective on these temperature differences, over the same day shown in Figure 59, 

the average temperature difference between rooms during the day was 34°F (1.1°C), with a 

maximum temperature difference of 36°F (2.2°C). BEopt assumes a constant temperature 

throughout the house and it is not currently possible to specify multiple thermal zones. 

Therefore, wall-mounted temperature sensors introduce a smaller error to the simulation than 

assuming a constant temperature throughout the house.