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

Evaluation of Model Results and 

Measured Performance of NetZero Energy Homes in Hawaii

Preprint

P. Norton, K. Kiatreungwattana, and K. J. Kelly

To be presented at the 2013 ASHRAE Annual Conference

Denver, Colorado

June 22-26, 2013

Conference Paper

NREL/CP-7A30-58000

March 2013
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
1 

Evaluation of Model Results and Measured 

Performance of Net-Zero Energy Homes in 

Hawaii 

Paul Norton Kosol Kiatreungwattana Kenneth J. Kelly

Member ASHRAE

ABSTRACT

The Kaupuni community consists of 19 affordable net-zero energy homes that were built within the Waianae Valley of Oahu, Hawaii in 2011. 

The project was developed for the native Hawaiian community led by the Department of Hawaiian Homelands. With high electricity prices in 

Hawaii, integrating energy efficiency and renewable energy to the design are critical to minimize energy cost. The energy features included in these 

all-electric homes were chosen using hourly energy modeling with building energy optimization (BEopt) software by the National Renewable 

Energy Laboratory (NREL). An optimization algorithm was applied to find the combinations of energy efficiency and renewable energy measures 

to achieve net-zero energy performance at least cost. 

The community includes a mix of one-story and two-story homes ranging from 1,306 to 1,616 square feet and is located in a relatively hot region 

of Oahu with low winds. Each home is equipped with high-efficiency windows, architectural shading, well-insulated walls and roof, light-colored 

roofing, SEER-16 air conditioning, solar water heating, high-efficiency appliances and lighting, and a 6.37 kW photovoltaic system. Wholehouse electricity consumption and indoor temperature has been monitored for all homes since their construction. 

This paper presents a comparison of the modeled and measured energy performance of the homes. Over the first year of occupancy, the community 

as a whole performed within 1% of the net-zero energy goals. The data show a range of performance from house to house with the majority of the 

homes consistently near or exceeding net-zero, while a few fall short of the predicted net-zero energy performance. The impact of building floor 

plan, weather, and cooling set point on this comparison is discussed. The project demonstrates the value of using building energy simulations as a 

tool to assist the project to achieve energy performance goals. Lessons learned from the energy performance monitoring have had immediate benefits 

in providing feedback to the homeowners, and will be used to influence future energy efficient designs in Hawaii and other tropical climates.

INTRODUCTION

Located in the Waianae Valley on the southwest side of the island of Oahu, the Kaupuni community consists of 19 

all-electric 1-story (1,306 ft2, 3 BR) and 2-story (1,616 ft2, 4 BR) affordable homes. With high electricity prices in Hawaii 

(Oahu residential electricity rates averaged $0.35/kWh over the period of this study), integrating energy efficiency and 

renewable energy to the design are critical to minimize energy cost. The goal of net-zero energy performance was chosen 

in early design charrettes for this project. During these charrettes, the decision was also made to include air conditioning for 

the homes. Although many homes on Oahu are naturally ventilated with no space conditioning system, the Waianae valley 

is on the leeward side of the Waianae mountain range and has little access to the trade winds available in many other parts 

of the island. For example, the annual average wind speed in the Typical Meteorological Year (TMY) weather data for 

Honolulu is 4.8 m/s, but the average wind speed measured in the Waianae valley during the test period was only 1.6 m/s. 
2 

Recognizing that air conditioned homes would require larger, more costly photovoltaic (PV) system, the team decided to 

minimize cooling loads and design around a modest cooling set-point of 76° F.

By combining shading, high-performance windows, well-insulated walls and ceiling, light-colored roofing, solar water 

heating, a high-efficiency air conditioning system, and a rooftop PV system, these “net-zero energy” homes were designed 

to generate as much energy as they consume each year. The homes were completed and occupied in the spring of 2011 

(HCEI 2010). Their energy performance was monitored from August 2011 to August 2012. This report summarizes the 

measured performance of the homes and compares these results to modeled performance. To protect the privacy of the 

homeowners, in this report each Kaupuni home is referred to by a randomly assigned house number. Each house number 

refers to the same home throughout the report, but it is not related in any way to the address or lot number of that home.

DESIGN RECOMMENDATIONS BASED ON ENERGY MODELING

With the designated goal of net-zero energy with a 76° F cooling set-point and a preliminary architectural design 

completed, energy models of the homes were created using BEopt building energy optimization software, version 0.9. 

BEopt was developed by the National Renewable Energy Laboratory with support from the U.S. Department of Energy 

and is available free of charge (Christensen, et.al, 2006). BEopt used the DOE2 and TRNSYS simulation engines to model 

the energy performance of a home in optimization, parametric, or design mode. The least-cost efficiency and solar option 

package needed to achieve the project goals were found by using BEopt in optimization mode. In optimization mode the 

user chooses a variety of possible energy-efficiency options, and BEopt finds the least-cost combination of options for each 

energy savings level up to net-zero energy. TMY2 weather data for Honolulu was used for the simulations. Although it is 

recognized that the weather in the Waianae valley can differ from Honolulu, TMY2 data for the Waianae valley was not 

readily available. To simulate the effect of occupant behavior on energy consumption, BEopt implemented the occupant 

assumptions published in the 2008 Building America Research Benchmark (Hendron, 2008). This benchmark uses available 

national data on occupant behavior to provide reasonable assumptions for typical occupants. As we shall see, actual 

occupant behavior varied substantially. The results of the optimization modeling for the 1-story model are shown in 

Figure 1. 

Figure 1 BEopt optimization results for the 1-story home model. On the left, each point represents a full year of hourly energy simulation

for a unique package of efficiency and solar options. The points along the lower line are the least-cost option packages. The bar chart on the 

right shows the energy consumption comparison for the code-built (Hawaiianized IECC 2006) and net-zero energy homes.

Based on the BEopt results, a list of recommended energy features was provided to Group 70, the architectural firm 

for the project. For simplicity, a single set of recommendations for both the singe-story and 2-story homes was provided. 
3 

Most of these features were implemented in the actual homes. A few were left out or modified based on concerns with cost 

or ease of building integration. A list of the recommended and as-built features is presented in Table 1.

Table 1. Recommended and As-Built Home Energy Features

Energy Feature Recommended As-Built

Roofing Light-colored asphalt singles As recommended

Attic insulation R30 at the ceiling plane As recommended

Lighting 100% CFL As recommended

Air conditioning SEER 18 ductless mini-splits SEER 16 ducted central

Appliances Energy StarTM appliances As recommended

Wall insulation R11 batts with 1” exterior foam R11 open-cell spray foam

Water heating Solar water heater with electric backup As recommended

Window type Low-e, low SHGC throughout As recommended

PV system size 6.0 kW rated peak AC output 6.37 kW rated peak output

MEASURED HOME PERFORMANCE

The hourly total electricity consumption and PV production were measured by the Hawaii Electric Company (HECO) 

with support from the Electric Power Research Institute (EPRI). An internet-connected whole-house meter capable of 

measuring hourly average total electricity consumption and total PV production was installed in each home. Data was 

collected from at least August 18, 2011 through August 14, 2012. In addition, the temperature and relative humidity next to 

the thermostat was measured by NREL on an hourly basis using stand-alone battery-operated loggers. The ratio of the total 

PV production to total consumption for each home is shown in Figure 2. 

Figure 2 Annual net energy performance of each home (1.0 = exactly net zero energy).

Overall, the community came within 1% of the net-zero energy target. The total PV production for all 19 homes was 

equal to 99% of the total consumption for the year of study. However, only six of the homes in the community were within 
4 

5% of the net-zero target individually – most were either net-energy producers or consumers. Most of the homes fell within 

20% of the net-zero energy target. In the extreme, house 7 consumed so little that the PV production was more than 21⁄2 

times the consumption.

All homes were equipped with the same 6.37 kW-rated PV system – somewhat larger than the 6.0 kW system 

recommended based on modeling. The installed PV panels are slightly larger than those used for the roof design, forcing 

three homes to have several panels facing north. In addition, the layout of the development required the home design to be 

rotated 90 degrees on seven lots. The roof pitch is low (~18 degrees), so the reduction in PV output for these off-south PV 

panels is reasonably small. The average daily PV production over the year of study for each home is shown in Figure 3. 

The average daily PV production varied from house-to-house by less than 10% – from a low of 26.9 kWh/day (capacity 

factor (CF)=17.6) to a high of 29.7 kWh/day (CF=19.4). Therefore, the large house-to-house variation in net energy is 

clearly mostly due to variations in consumption rather than variations in PV production.

Figure 3 Daily average PV production from each home.

The average daily electricity consumption over the year of study for each house is shown in Figure 4. The house-tohouse consumption varied by nearly a factor of four. The coefficient of variance (the standard deviation divided by the 

mean) for the community is 25%. This variation may seem higher than expected in a community of homes built at the same 

time with the same energy efficiency features, but similar variation has been seen is several other new home projects 

(Aldrich 2012, Osser and Kerrigan 2012, Norton, et.al. 2008, BIRA 2007, BSC 2000). It is interesting to note that any 

consumption differences between smaller one-story and larger two-story home are lost in the variation due to different 

numbers and behavior of occupants. The difference in net-energy performance seen in Figure 2 is nearly completely due to 

the difference in consumption patterns of different occupants. 
5 

Figure 4 Average daily total electricity consumption from each house. 

Lessons learned from the energy performance monitoring has had immediate benefits in providing feedback to the 

homeowners, and will be used to influence future energy-efficient designs in Hawaii and other tropical climates. The 

performance data from these homes will be presented in more detail in an upcoming NREL technical report, which will be 

available through the NREL publications website (http://www.nrel.gov/publications).

MODELING THE HOMES AS BUILT

To make a fair comparison between modeled and measured results for this paper, the home models created for design 

assistance were refined to more closely reflect the homes as they were actually built and operated. The geometry, window 

areas, cooling equipment, insulation, and PV sizing were modified to represent finished homes. The cooling set point was 

modified to reflect the actual measured average indoor temperature of 23.8° C (74.8° F) – somewhat lower than the 24.4° C 

(76° F) assumed for the design recommendations. Other than the cooling set point, standard occupant assumptions were 

used in all models. A summary of these additional modeling runs is given in Table 2.

Table 2. Additional modeling done to better represent the homes as they were actually built

Model # Weather Data Cooling Set Point BEopt Version Simulation Engine(s)

1 Honolulu TMY3 24.4 C (76 F) BEopt 0.9.04 Beta DOE2.2-47g, TRNSYS

2 Honolulu TMY3 23.8 C (74.8 F) BEopt 0.9.04 Beta DOE2.2-47g, TRNSYS

3 Waianae actual 23.8 C (74.8 F) BEopt 0.9.04 Beta DOE2.2-47g, TRNSYS

4 Waianae actual 23.8 C (74.8 F) BEopt 1.3 DOE2.2-47g, TRNSYS

The Honolulu weather file was modified with weather data for the actual test period in the Waianae Valley. In 

addition, the as-built models were recreated in an improved version of BEopt that incorporates an updated set of occupant 

assumptions – The Building America House Simulation Protocols (Hendron and Engebrecht 2010).
6 

The Waianae Intermediate School is about 1 km (0.6 mi) from the Kaupuni community and has a weather station with 

publically accessible data (HECO 2012). Waianae data for dry bulb temperature, wind speed, and solar radiation during the 

study period was used to replace those values in the Honolulu TMY3 data to create the “Waianae actual” weather file. The 

solar radiation data required for the TMY3 files was calculated from the radiation on the plane of the PV modules at the 

school. The cooling degree days for the Waianae weather file (2,566, base 18° C) were remarkably similar to the Honolulu

TMY3 weather file (2,524, base 18° C). The daily average horizontal global radiation was also similar between the two files 

(Waianae: 5,551 Wh/m2; Honolulu TMY3: 5,346 Wh/m2). The big difference between the weather files is the annual 

average wind speed (Waianae: 1.6 m/s; Honolulu TMY3: 4.8 m/s). The modification of the Honolulu TMY3 file was 

intended to include available microclimate data but does not represent the complete actual weather. The replacement of the 

Honolulu TMY3 weather with the measured Waianae data had only a small effect on the predicted annual consumption and 

PV production of the homes.

The predicted annual consumption for each of the refined models is shown along with the actual house-by-house 

consumption in Figure 5. 

Figure 5 Measured and modeled daily total electricity consumption

All modeled consumption results are within the home-to-home range of measured consumption. Although the change 

in cooling set point and weather data had some effect on predicted consumption, all BEopt 0.9 models predicted annual 

consumption below the measured average by about 19%. In contrast, Model 4 recreated in BEopt 1.3 predicted 

consumption above the measured average by about 14%. Most of the difference in predicted energy use between the earlier

and current versions of BEopt lies in the simulated cooling and air handler energy use.

The average daily PV production predicted was 24.4 kWh (CF=16.2) for Model 3 and 25.3 kWh (CF=16.8) for 

Model 4. These predictions are both shy of the measured average of 28.2 kWh/day (CF=18.7). Although the BEopt 

options library includes many specific PV modules, the modules used in Kaupuni were not available and a generic c-si type 
7 

module model was used. The models also include a derate factor of 15% when predicting PV output. This derate factor 

includes effects such as losses due to the inverter efficiency, wiring, diodes, connections, and soiling (PVWatts 2012). The 

actual derate factor may be different than the modeling assumption. The module type and default derate factors may 

account for the models’ under-prediction of PV output. The most refined and up-to-date simulations (Model 4) with the 

15% default derate predict that the community would fall short of the net-zero energy goal by over 20%.

DISCUSSION 

The use of modeling to provide design guidance for the Kaupuni project proved very effective. The community of 

homes built incorporating this guidance came within 1% of the net-zero energy goal. However, any single home may 

deviate significantly from the expectations based on simulations. The variation in behavior and choices of different 

occupants has a profound effect on energy use in residential buildings. Because of this, comparison of modeled results to a 

single occupied home may be a futile exercise. It is hard to imagine capturing all of the inputs needed to characterize the 

quirks of actual occupant behavior to an acceptable level of accuracy. For every simulation generated in this study there 

were homes in the community that matched the simulated consumption fairly well and others that differed dramatically. For 

example, if this project had been only one occupied home – house 2, then one might have lauded the accuracy of Model 4 

that predicted the consumption with less than 1% error. In contrast, if the only occupied home in the study had been 

house 7, one might have thought there was some egregious error in Model 4 causing it to miss the actual consumption by 

more than a factor of three. This level of mismatch between a model using standard occupant assumptions or even one 

with some assumptions (like cooling set point) tuned to the actual occupants should not be surprising considering the range 

of energy use variation seen in communities of identical homes. Comparing modeled to average measured energy 

performance in a large sample of occupied homes may provide a more meaningful assessment of modeling accuracy.

There are many possible explanations for the difference between the modeled and measured community average 

consumption at Kaupuni. The difference between the actual and TMY data does not seem to be a significant contributor to 

the difference between modeled and measured use in this project. The sample size for the Kaupuni study was fairly small – 

19 homes. This sample size may not be large enough to represent typical behavior, so the actual energy use is a function of 

the individuals who happened to purchase homes in the community. In addition, the occupant behavior in the unique 

climate and culture of Oahu may vary substantially from the national data upon which the modeling inputs were based. The 

BEopt program provides a pick list of energy-efficiency measures – in some cases, the exact measure implemented was not 

available and the closest option was chosen. For example, the configuration of solar water heating systems used in Hawaii 

does not exactly match any of the options available in BEopt. BEopt also provides a geometry tool to quickly create the 

home geometry. The exact roof geometry and window sizes could not be reproduced with the BEopt geometry tool. As 

mentioned earlier, some homes were built with differing orientations. The observed difference in modeled and measured 

average consumption is likely to be the net effect of many factors but is not large enough to reduce the usefulness of the 

model for guiding design decisions.

Understanding modeling accuracy requires a careful consideration of possible sources of errors and the development 

of methodologies to disaggregate these potential sources (Polly 2011, Polly, et. al. 2011, Roberts, et. al. 2012, Judkoff and 

Neymark 2006). It may be necessary divide and conquer – studying algorithm accuracy in laboratory studies and 

unoccupied instrumented homes and studying occupant input assumption accuracy statically in large samples of occupied 

homes. It is clear from this study that occupant behavior and choices can have as large an effect on the energy consumption 

as the energy efficiency measures incorporated in the home. As we build more efficient homes, the relative importance of 

occupant behavior is likely to increase. Although sensitivity of water heating and space conditioning to use and set points 

may be reduced with more efficient enclosures and equipment, the relative contribution of highly variable appliance and 

miscellaneous electric loads to total energy use increases substantially. This argues for a greater investment in understanding 

the variety of occupant behavior and developing more sophisticated ways to incorporate their effects on energy 
8 

consumption. Despite the differences between modeled results and measured performance, it is important to emphasize 

that the overall result has been that the community as a whole is performing near net zero during the first year of 

occupancy.

REFERENCES

Aldrich, R. (2012). Wisdom Way Solar Village: Design, Construction, and Analysis of a Low-Energy Community, 

Consortium for Advanced Residential Buildings, U.S. Department of Energy, DOE/GO-102011-3484. 

BIRA (2007). Final Report: Occupied Home Evaluation Results, Building Industry Research Alliance deliverable 16.D.2, 

Building America Program, U.S. Department of Energy.

BSC (2000). Large Scale Performance of Building America Home Designs: Updated Project Descriptions and Utility Bill 

Data, Building Science Corporation, National Renewable Energy Laboratory, Task Order No. 7.8.2 under TOA Kar-8-

18412-07. 

Christensen, C.; Anderson, R.; Horowitz, S.; Courtney, A.; Spencer, J. (2006). BEopt(TM) Software for Building Energy 

Optimization: Features and Capabilities. National Renewable Energy Laboratory, NREL Report No. TP-550-39929. 

HCEI (2010). Kaupuni Village: A Closer Look at the First Net-Zero Energy Affordable Housing Community in Hawai'i 

(Brochure), Hawai'i Powered, Hawai'i Clean Energy Initiative (HCEI), National Renewable Energy Laboratory, NREL 

Report No. BR-7A40-53401; DOE/GO-102012-3465. 

HECO (2012). Sun Power for Schools Demonstration, Hawaii Electric Corporation, data available at www.heco.com.

Hendron, R. (2008). Building America Research Benchmark Definition: Updated December 20, 2007. National Renewable 

Energy Laboratory, NREL Report No. TP-550-42662. 

Hendron, R.; Engebrecht, C. (2010). Building America House Simulation Protocols (Revised). National Renewable Energy 

Laboratory, NREL Report No. TP-550-49246, DOE/GO-102010-3141. 

Judkoff, R.; Neymark, J. (2006). “Model Validation and Testing: The Methodological Foundation of ASHRAE Standard 

140.” ASHRAE Transactions 112(2):367–376. Atlanta, GA: American Society of Heating, Refrigerating and AirConditioning Engineers.

Norton, P., Burch, J., and Hendron, B. (2008). Project Closeout: Guidance for Final Evaluation of Building America 

Communities, National Renewable Energy Laboratory, NREL/TP-550-42448. 

Osser, R. and Kerrigan, P., (2012). Performance Evaluation of a Hot-Humid Climate Community, Building Science 

Corporation, U.S. Department of Energy, DOE/GO-102012-3455. 

Polly, B. (2011). Improving the Accuracy of Software-Based Energy Analysis for Residential Buildings (Presentation). 

National Renewable Energy Laboratory, NREL Report No. PR-5500-52745. 

Polly, B.; Kruis, N.; Roberts, D. (2011). Assessing and Improving the Accuracy of Energy Analysis for Residential 

Buildings. National Renewable Energy Laboratory, NREL Report No. TP-5500-50865, DOE/GO-102011-3243. 

PVWatts (2012). PVWatts: A Performance Calculator for Grid-Connected PV Systems, National Renewable Energy 

Laboratory, http://rredc.nrel.gov/solar/calculators/pvwatts/version1/.

Roberts, D.; Merket, N.; Polly, B.; Heaney, M.; Casey, S.; Robertson, J. (2012). Assessment of the U.S. Department of 

Energy's Home Energy Scoring Tool. National Renewable Energy Laboratory, NREL Report No. TP-5500-54074.