NIST Special Publication 1204

An Assessment of Typical Weather 

Year Data Impacts vs. Multi-year 

Weather Data on Net-Zero Energy 

Building Simulations 

Joshua Kneifel

Eric O’Rear

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.SP.1204

 

NIST Special Publication 1204

An Assessment of Typical Weather 

Year Data Impacts vs. Multi-year 

Weather Data on Net-Zero Energy 

Building Simulations 

Joshua Kneifel

Applied Economics Office

Engineering Laboratory

Eric O’Rear

Applied Economics Office

Engineering Laboratory

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.SP.1204

January 2016

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Director
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Special Publication 1204

Natl. Inst. Stand. Technol. Spec. Pub. 1204, 45 pages (January 2016)

CODEN: NTNOEF

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.SP.1204
ii

Abstract

This paper looks to contribute to the existing building simulation literature by examining the 

impacts of weather variability on annual household energy use and solar photovoltaic (PV) 

production for low-energy homes integrating renewable energy generation system(s) in their 

design. Using the U.S. Department of Energy’s EnergyPlus v8.3 whole-building energy 

simulation program, we observe variability in the energy performance of a net-zero home across 

34 different Actual Meteorological Year (AMY) weather files collected between 1980 and 2013 

(U.S. Department of Energy (DOE) 2015). The simulated building design is based on the Net 

Zero Energy Residential Test Facility (NZERTF) developed by the National Institute of 

Standards and Technology. In addition to this, the simulation will be run with a local TMY3 

weather file to evaluate how representative it is of “typical” weather conditions for the area, and 

if its use will result in accurate predictions of NZERTF energy performance. 

Keywords

Energy efficiency; solar photovoltaics; net-zero; weather data; historical weather
iii

Preface

This study was conducted by the Applied Economics Office (AEO) in the Engineering 

Laboratory (EL) at the National Institute of Standards and Technology (NIST). The study is 

designed to observe the variability in the annual energy performance of the NIST Net-Zero 

Energy Residential Test Facility (NZERTF) over a period of 34 years. Additionally, the study 

seeks to evaluate how well the statistically-derived TMY3 weather file reflect average or 

“typical” weather conditions for the Gaithersburg, MD area.

Disclaimers

The policy of the National Institute of Standards and Technology is to use metric units in all of 

its published materials. Because this report is intended for the U.S. construction industry that 

uses U.S. customary units, it is more practical and less confusing to include U.S. customary units 

as well as metric units. Measurement values in this report are therefore stated in metric units 

first, followed by the corresponding values in U.S. customary units within parentheses.
iv
v

Acknowledgements

The authors wish to thank everyone for their advice and recommendations for the writing of this 

report, including Douglas Thomas and Dr. Robert Chapman of EL’s Applied Economics Office, 

Matthew Boyd of EL’s Energy and Environment Division, and Dr. Nicos S. Martys of EL’s 

Materials and Structural Systems Division.

Author Information

Joshua D. Kneifel

Economist

National Institute of Standards and Technology

100 Bureau Drive, Mailstop 8603

Gaithersburg, MD 20899-8603

Tel.: 301-975-6857

Email: joshua.kneifel@nist.gov

Eric G. O’Rear

Economist

National Institute of Standards and Technology

100 Bureau Drive, Mailstop 8603

Gaithersburg, MD 20899-8603

Tel.: 301-975-4570

Email: eric.orear@nist.gov
vi
vii

Contents

Abstract.............................................................................................................................. ii

Preface...............................................................................................................................iii

Acknowledgements ........................................................................................................... v

Author Information .......................................................................................................... v

List of Acronyms.............................................................................................................. xi

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

2 Background ............................................................................................................... 3

3 Methodology .............................................................................................................. 7

3.1 The NIST Net-Zero Residential Test Facility...................................................... 7

3.2 Simulation model specifications and general assumptions.................................. 7

4 Results and Discussion............................................................................................ 11

4.1 Impacts of alternative weather profiles on NZERTF electricity consumption .. 11

4.2 Impacts of alternative weather profiles on the on-site solar photovoltaic electricity 

production.................................................................................................................. 15

4.3 Impacts of alternative weather profiles on annual net electricity production .... 17

4.4 Impacts of alternative weather profiles on building HVAC system peak electricity 

demands 19

5 Conclusion ............................................................................................................... 20

6 References................................................................................................................ 22

A Appendix.................................................................................................................. 25
viii
ix

List of Figures

Figure 3-1 The NIST Net-Zero Energy Residential Test FacilityError! Bookmark not defined.

Figure 4-1 Annual Household Electricity Use for AMY and TMY3 Files 

(1980 through 2013) ......................................................................................................... 11

Figure 4-2 Annual Electricity Usage for Household Heating and Cooling DemandsError! 

Bookmark not defined.

Figure 4-3 Proportional Electricity use for Household Heating and Cooling Demands for 

TMY3 and AMY Files (1980 through 2013).................................................................... 13

Figure 4-4 Total Number of HDD and CDD for AMY Files (1980 through 2013)..Error! 

Bookmark not defined.

Figure 4-5 Histogram of Annual Household Electricity use for the TMY3 and AMY Files 

(1983 through 2013) ......................................................................................................... 14

Figure 4-6 Annual NZERTF Solar PV Production and Global Horizontal Irradiance for 

TMY3 and AMY Files (1980 through 2013).....................Error! Bookmark not defined.

Figure 4-7 Average Global Horizontal Irradiance Measures for the TMY3 and Select AMY 

Weather Files.................................................................................................................... 16

Figure 4-8 Histogram of Annual Solar PV Electricity Production for the TMY3 and AMY 

Files (1983 through 2013)..................................................Error! Bookmark not defined.

Figure 4-9 Annual Net Electricity Production for TMY3 and AMY Files 

(1980 through 2013) ......................................................................................................... 18

Figure 4-10 Histogram of Annual Net Electricity Production for the TMY3 and AMY 

Files (1983 through 2013)..................................................Error! Bookmark not defined.

Figure 4-11 Annual Peak Electricity Demands for Building HVAC SystemError! Bookmark 

not defined.
x

List of Tables

Table 3-1 NZERTF Building Specifications ...................................................................... 9

Table A-1 Annual Household Electricity Demands, Heating-based Electricity Demands, 

and Cooling-based Electricity Demands under each Weather File .................................. 25

Table A-2 Annual Household Electricity Demands, Solar PV Production, and Net Solar 

PV Production under each Weather File........................................................................... 27

Table A-3 Peak Electricity Demands for the Building HVAC System under each Weather 

File .................................................................................................................................... 29
xi

List of Acronyms

Acronym Definition

ACH air changes per hour

AEO Applied Economics Office

ARRA American Recovery and Reinvestment Act

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers

BA Building America

BSC Building Science Corporation

CFA conditioned floor area

CFL compact fluorescent lamp

CFM cubic feet per minute

COP Coefficient of Performance

DHW Domestic Hot Water

E+ EnergyPlus

EERE Office of Energy Efficiency & Renewable Energy

EIA Energy Information Administration

EL Engineering Laboratory

ELA effective leakage area

EPA Environmental Protection Agency

HRV Heat Recovery Ventilator

HSPF Heating Seasonal Performance Factor

HVAC Heating, Ventilation, and Air-Conditioning

IECC International Energy Conservation Code

NIST National Institute of Standards and Technology

NZERTF Net-Zero Energy Residential Test Facility

O C on center

PV photovoltaic

SEER Seasonal Energy Efficiency Ratio

SHGC Solar Heat Gain Coefficient

VT Visual Transmittance
1

1 Introduction

As of 2014, commercial and residential buildings were responsible for 41% of total U.S. energy 

consumption (Energy Information Agency (EIA) 2015). The federal government through 

legislation such as the American Recovery and Reinvestment Act of 2009 (ARRA) has provided 

funds to energy efficiency and renewable energy research and investment in hopes of reducing 

the energy demands of residential buildings. Improving the energy efficiency of new residential 

construction can be a cost-effective method to help meet this goal (Kneifel 2011a). According to 

the USGBC Research Committee, a 30% reduction in energy use relative to a more conventional 

house design can be achieved through various combinations of building envelope, water, 

lighting, and heating, ventilation, and air-conditioning efficiency measures. However, any 

building performance improvements of 50% or higher can only be secured if the aforementioned 

prescriptive measures are implemented in conjunction with a renewable energy system integrated 

into the household design (USGBC Research Committee 2008). System integration grows ever 

more important as the relative proportion of low-energy and net-zero energy buildings in new 

building stock increases. Buildings that are net-zero incorporate a wide variety of prescriptive 

energy efficiency measures to lower household energy consumption in conjunction with 

integration of renewable energy generation system(s) (e.g. solar photovoltaic [PV]) to provide a 

means for the home to produce energy on-site – leading to enough production to fully offset 

consumption over a year (Leckner and Zmeureanu 2011).

Researchers often utilize whole-building simulation models to predict the performance of 

individual building designs in a real-world environment (Crawley, Lawrie et al. 2000, Clarke 

2001). Simulation results can then be used to make accurate, well-informed decisions on 

building specifications and the types of renewable energy systems to be integrated in the building 

design. Whole-building energy simulation programs, similar to other simulation models, are 

limited by their assumptions and the availability of accurate data (Maile, Fischer et al. 2007). 

Some programs consider the impacts of various external factors, such as local weather conditions 

on simulated building performance. Local weather conditions are often incorporated through the 

use of weather data captured in Actual Meteorological Year (AMY) and Typical Meteorological 

Year (TMY) files. AMY files are the actual weather data sets that have been collected over a 

given year. A TMY file for a location is developed using 15 to 30 years of hourly weather data 

for that specific location, and is assumed to be representative of the average climatic conditions 

(National Renewable Energy Laboratory (NREL) 1995). Reflected in these weather files is an 

array of information including solar irradiance and ambient temperature over a given year. In 

regards to building performance, the number of HDDs and CDDs impact the total energy 

consumed by the building to meet heating and cooling loads. Simulated energy production by an 

integrated solar PV system is directly impacted by measures of solar irradiance and ambient 

temperature.
2

Simulation results generated using TMY weather files are interpreted as the performance of a 

building design under weather conditions that are “typical” for that location. The statistical 

approach used to compile a one-year TMY file, however, fails to consider any extreme weather 

anomalies that may have occurred (Dean 2010). The oversimplification of assumed localized 

weather conditions may produce some bias in the results and lead to inaccurate expectations of 

building performance.

A great deal of buildings literature examines the relationship between the uncertainty in local 

weather conditions and the energy performance of both commercial and residential buildings –

many of which compare the use of statistically-derived weather datasets to actual historical 

weather datasets in building simulation practices (Haberl, ONeal et al. 1995, Huang and Crawley 

1996, Crawley 1998, Yang, Lam et al. 2008, Wang, Mathew et al. 2012, Hong, Chang et al. 

2013). Researchers have gone a step further in many of these studies observing how varying 

levels of building energy-efficiency influence the impacts of weather uncertainty on energy use. 

Our work seeks to contribute to the existing literature by using a net-zero residential building 

simulation to analyze the impacts of weather variability on energy performance based on more 

than 30 years of historical data. Additionally, these results will then be compared to a simulation 

operating under “typical” weather conditions. For a net-zero building, its total energy use for a 

given year is roughly equal to the amount of renewable energy produced on site in that year; 

therefore, energy performance in our case will be evaluated based on annual energy consumption 

and on-site renewable energy generation. Using the U.S. Department of Energy’s EnergyPlus 

v8.3 whole-building energy simulation program, we observe the variability in the energy 

performance of a net-zero residential dwelling across 34 different AMY weather files 

constructed using data collected between 1980 and 2013 (U.S. Department of Energy (DOE) 

2015). The simulated net-zero design is based on the Net Zero Energy Residential Test Facility 

(NZERTF) developed by the National Institute of Standards and Technology (NIST) located in 

Gaithersburg, Maryland. Additionally, the simulation is run with the most recent TMY3 weather 

file from the KGAI weather station for the purpose of determining how well it represents

“typical” weather conditions for Gaithersburg, MD, and whether its use can accurately provide

insight on the projected energy performance of the NZERTF. 
3

2 Background

The rising popularity of low-energy and net-zero homes in the residential housing market has led 

to an upsurge in the demand for solar energy generation technologies. Homeowners anticipate 

recouping large levels of energy cost savings by relying on solar PV to offset some of their grid 

usage. Development of whole-building simulation programs has given building designers the 

ability to evaluate the impacts of solar PV integration on the overall energy performance of their 

designs, specific to weather conditions appropriate for their desired location. However, there can 

be a great deal of uncertainty in solar generation in response to variability in local climate 

conditions that is not accounted for by these programs.

Operating building simulation programs requires data on meteorological inputs for a specific 

location. Actual Meteorological Year (AMY) databases provide these inputs for a given location. 

However, weather data captured in AMY files vary from year to year and make it difficult to 

determine what the average simulated building performance will be. Typical Meteorological 

Year (TMY) databases were developed as a solution to this problem. The U.S. Department of 

Energy’s National Renewable Energy Laboratory (NREL) first developed TMY files in 1981, 

serving as a representation of the average weather conditions for a specific location. Files are 

created by looking at 15 to 30 years of hourly weather data, generally collected by a local 

weather station, and include data on various weather characteristics such as solar radiation and 

temperature, for a designated location. Statistical methods are then used to select each of the 

twelve months considered to be “typical” for that location (Weather Analytics 2014). 

Subsequent iterations of TMY data were created based on weather data collected for different 

periods. The Typical Meteorological Year version 2 (TMY2) files were developed using weather 

station data from 1961 to 1990 and is available for 239 locations in the U.S. (National 

Renewable Energy Laboratory (NREL) 1995). The latest version (TMY3) covers only 15 years 

(1991 – 2005) and includes weather information for more than 1,400 sites in the U.S. It also 

includes information on new variables such as precipitation (National Renewable Energy 

Laboratory (NREL) 2008).

Although the TMY files help to generalize weather conditions and simplify the forecasting 

process, there are some disadvantages associated with their use. For one, methods used to 

construct them disregard periods of extreme weather conditions or anomalies. NREL relies on 

the empirical Sandia method to develop their TMY3 files. The approach disregards atypical 

weather anomalies occurring during the set of years being considered. Therefore, any resulting 

decisions on building equipment for energy-efficient designs that are directly influenced by 

simulation results using these files may or may not account for strained used of the equipment in 

the case of brief periods of atypical weather conditions. Secondly, files may be developed using 

fewer than 15 years of data. NREL uses 15 years of data when constructing TMY3 files. A lack 

of 15 years of consistent data in some locations could limit the robustness of the generated data. 

In some instances, a lack of recent available data may lead to TMY3 files being constructed 
4

based on much older data (Weather Analytics n.d.). In constructing TMY files the data generally 

comes from airport data collection stations which means that the further away a simulated 

building’s location is from the collection site, the less valid the assumptions of local weather 

conditions will be. Each of these disadvantages could potentially lead to a bias in the simulation 

results, with modeled performance being over- or underestimated.

Issues regarding the use of TMY files in practice have been observed in modeling wholebuilding energy consumption for commercial buildings (Huang and Crawley 1996, Crawley 

1998, Wang, Mathew et al. 2012, Hong, Chang et al. 2013). In their work comparing the impacts 

of TMY3 data and actual collected weather data spanning 30 years on energy consumption and 

peak electricity demands for three prototype commercial buildings (office) at two levels of 

efficiency across the 17 ASHRAE climate zones, authors Hong, Chang et al. (2013) found that 

measured consumption ranged between -28% and 15%. They decided that the lack of agreement 

between the series of weather files is largely attributed to the TMY3 file not accurately capturing 

average weather conditions in each location. Similarly, Wang et al. (2012) finds that annual 

energy usage can vary between -4.0% to 6.1% when comparing the simulated office building’s 

annual consumption under TMY3 weather conditions to consumption levels estimated using 10 

years to 15 years of historical data across four U.S. cities. A broader study is conducted by 

Crawley (1998) in which he looks at the uncertainty in simulated energy use for commercial 

buildings linked to weather variability across multiple datasets. Using the DOE-2 1E building 

simulation model, he compares the impacts of different “typical” weather datasets to 30 years of 

actual hourly weather data captured in the SAMSON (Solar and Meteorological Surface 

Observational Network) weather dataset on the simulated annual energy consumption and costs, 

and annual peak electricity demand for a prototype office building across eight different 

locations. He discovers that the annual energy consumption will vary from -11% to 7% relative 

to the derived average consumption using the SAMSON dataset. Approximately half of the 

variation is observed with annual energy costs and peak electricity demand. Of the six typical 

datasets studied, the TMY2 weather data resulted in the least amount of variation in many of the 

locations, suggesting that the methodology used in formulating the weather file can provide 

accurate simulation results relative to other methodologies. This fact, however, is sensitive to the 

underlying data used and the location being considered.

Similarly, Huang (1998) uses prototypical residential building simulations to compare the 

simulated heating and cooling loads under different weather datasets capturing average weather 

conditions to that of a 30-year average. He finds that heating and cooling energy consumption 

using the TMY2 weather data falls within 5% of the long-term averages.
5

Similar to building simulation models, solar PV energy production forecasting tools require the 

meteorological inputs of TMY files, also making them susceptible to a degree of uncertainty. 

Williams and Kerrigan (2012) suggest that TMY data – in particular the most recent TMY3 

dataset – can provide an adequate representation of solar conditions, potentially lowering the 

anticipated risk associated with using typical weather data to forecast production. Other analyses 

(Cameron, Boyson et al. 2008, Dean 2010, Yimprayoon and Navvab 2011) suggest some level of 

caution, stressing the likelihood of noticeable differences in production outputs whenever actual 

weather conditions are considered in place of typical ones. In comparing the TMY2 weather 

profile constructed over a 30-year period to an alternative weather profile depicting the 

“average” weather conditions over the same period, Dean (2010) discovers that the TMY2 data 

is not representative of a central tendency in the data, and appears to be an outlier when 

compared to the average weather file. He goes on to show that use of the PVWatts solar PV 

production tool in conjunction with the TMY2 data regularly overestimates monthly production 

by 6% over the average, and forecasted TMY2 production exceeds more than 90% of the 

estimated production based on historical data. Yimprayoon and Navvab (2011) discovered that 

the use of historical data in forecasting solar PV production helps to showcase a large degree of 

production uncertainty related to variations in annual weather conditions not captured by the 

statistically derived TMY3 data sets. Other authors such as Cameron et al. (2008) note that even 

though these discrepancies do exist when using TMY data, the output from most PV production 

models when using these datasets will lie within +/-5% of actual production. 

Some of the inconsistencies exhibited by TMY weather files are attributed to the methods in 

which they are constructed and/or the length of historical data used in constructing the files 

(Janjai and Deeyai 2009, Donghyun, Yu Joe et al. 2010). A study completed by Janjai and 

Deeyai (2009) on solar PV production in Thailand reveals the degree of uncertainty that exists 

across the Sandia, Danish, and the Festa & Ratto TMY data generation methods. Generated 

TMY data points such as temperature, relative humidity, and wind speed were well predicted 

across all three approaches when compared to actual measurements. However, significant 

variation in solar radiation estimates result in a large variation in the predicted electricity output 

for an installed system in that region. Despite the Sandia method being found to be more 

preferable to the others, the degree of variation in production is acceptable for the purposes of 

forecasting solar PV production.

The above literature suggests that when conducting building simulations, there is likely to be 

differences in simulated energy use and production when using typical year weather data to local 

weather conditions in place of multi-year historical data. There is also evidence of energy 

performance uncertainty associated with local weather variability. With evidence of a changing 

global climate and growing interest in high efficiency homes, it is imperative that building 

designers recognize that the use of weather datasets like TMY3 could lead to simulation results 

that fail to reflect accurate building responses to local weather. It is also important recognize the 
6

degree of consumption and production uncertainty present given the likelihood of varying

weather conditions from year to year. 
7

3 Methodology

3.1 The NIST Net-Zero Residential Test Facility

The NIST’s NZERTF is located on the NIST main campus in Gaithersburg, Maryland. The twostory, four-bedroom house was initially constructed to show that a residential home design could 

be net-zero and have the “look and feel” of a typical home in the area (Pettit and Gates 2014). It 

has approximately 251.7 m

2

 (2709 ft2) of total conditioned floor area and was designed to be 

roughly 60 % more energy-efficient than newly constructed homes in the area built according to 

the 2012 International Energy Conservation Code (IECC). Additionally, a 10.2 kW solar PV 

system was installed on the roof of the NZERTF to produce more electricity than is consumed 

over a given year. The facility serves as an on-site laboratory (Figure 1). During its first year of 

operation (July 2013 through June 2014), the house exceeded net-zero energy performance with 

a surplus of 596 kWh, which was enough electricity to fuel an electric vehicle for close to 

2253 km (1400 miles) (Kneifel, Payne et al. 2015). 

Figure 3-1: The NIST Net-Zero Energy Residential Test Facility

3.2 Simulation model specifications and general assumptions

Evaluation of the energy performance of any building design requires the use of whole-building 

simulation software. This study utilizes the results from the EnergyPlus 8.3.0 whole-building 

energy simulation program to observe the yearly energy performance of the NZERTF (U.S. 

Department of Energy (DOE) 2015). Operation of these programs require a number of general 

assumptions and user-defined specifications, such as data best representative of local weather 

conditions, time step designations, and run periods. The program also requires the specifications 

of the simulated building design. 
8

Energy use based on alternative assumptions for local weather conditions is estimated with 

EnergyPlus by using the third iteration of the local TMY (TMY3) weather file and the AMY 

weather files spanning 1980 through 2013 for the KGAI weather station located less than seven 

miles from the NIST campus (Weather Analytics 2014, Kneifel, Payne et al. 2015). This weather 

station was selected because it is the nearest data collection station to the NZERTF facility, and 

has weather data spanning more than fifteen years (the minimum number of years of data used to 

develop a TMY weather file).

In addition to weather data, the EnergyPlus software requires the user to select a specific run 

period and time step. A run period is the days of the year the simulation will run. The time step is 

the frequency at which the model runs the simulation. A one-year run period is selected since we 

are observing the impacts of alternative weather conditions on annual energy use and production. 

A one minute time-step is also chosen since the assumed length of some household activities 

(e.g. hot water use by the sink) are modeled in one minute increments, and the use of a one 

minute time step allows us to more accurately capture system operation and the resulting energy 

consumption (Kneifel 2012).

1

 

The “validated” NZERTF simulation used in this analysis was designed according to the postdemonstration phase specifications of the facility to mimic actual performance specifications2

. 

Table 3-1 describes the most recent building specifications/energy efficiency measures 

incorporated into the NZERTF simulation design.

 

1 Weather files are based on hourly data and are interpolated for the shorter simulation model timestep.

2 Adjustments to the initial NZERTF simulation within EnergyPlus were made based on a comparison of the predemonstration simulated energy performance and the actual measured performance of the home at the end of its 

demonstration phase in June 2014. See Kneifel, Payne et al. (2015) for more details on the adjustments made to the 

NZERTF simulation.
9

Table 3-1: NZERTF Building Specifications 

Building Category Specifications Details

Windows U-Factor

SHGC

VT

1.14 W/m2-K (0.20 Btu/h*ft2-F)

0.25

0.40

Framing and 

Insulation

Framing

Exterior Wall

Basement Wall**

Roof

5.1 cm X 10.2 cm – 40.6 cm OC (2 in X 6 in – 24 in OC)

RSI-3.5 + 4.2 (R-20+24*)

RSI-3.9 (R-22*)

RSI-7.9 + 5.3 (R-45+30*)

Infiltration Air Change Rate

Effective Leakage 

Area

0.63 ACH50

1

st Floor = 132.6 cm2

(20.6 in2)

2

nd Floor = 120.9 cm2

(18.7 in2)

Lighting % of Efficient 

Lighting

100 % efficient built-in fixtures

HVAC Heating/Cooling

Outdoor Air***

Air-to-air heat pump (SEER 15.8/HSPF 9.05)

Separate HRV system (0.04 m3/s)

Domestic Hot Water Water Heater

Solar Thermal

50 gallon heat pump water heater (COP 2.36)

2 panel, 80 gallon solar thermal storage tank

Solar PV System System size 10.2 kW

* Interior + Exterior R-Value

** Basement floor insulation is the same for both

*** Minimum outdoor air requirements are based on ASHRAE 62.2-2010

The building envelope of the NZERTF is constructed to be “tighter” than identical homes built 

according to the 2012 IECC with regards to framing, insulation, windows, and air leakage. It is 

constructed using 5.1 cm X 15.2 cm – 61.0 cm (2 in X 6 in – 24 in) on center (OC) framing 

(“advanced framing”) as opposed to the traditional 5.1 cm X 10.2 cm – 40.6 cm (2 in X 4 in – 16 

in) OC framing. Thicker framing decreases the amount of wood used in framing the house, while 

allowing for more insulation within the wall cavity, potentially improving the overall thermal 

performance of the home (Lstiburek and Eng 2010)3. The exterior wall uses R-20 in the wall 

cavity plus an additional R-24 of rigid insulation. The interior of the basement wall uses R-10 

 

3 Use of 2x6 wood framing at 24-inch centers reduced the amount of lumber needed for construction (board-feet) by 

5 to 10%, and provides 60% more cavity insulation.
10

plus an additional R-12 of rigid insulation. Construction of the roof includes R-45 insulation in 

the rafters with rigid insulation R-30 added to the exterior of the roof. 

The U-Factor, Solar Heat Gain Coefficient (SHGC), and the Visible Transmittance (VT), are 

used in the simulation to specify the fenestration surface construction materials for the windows. 

The values listed in the table are based on minimum requirements specified in the 2012 IECC 

and the Building Science Corporation (BSC) window specifications (Building Science 

Corportation (BSC) 2009).

The measure of air tightness or infiltration for the NZERTF is characterized by the air changes 

per hour at 50 Pa (ACH50) using a blower door test, which was measured at 0.63. The air 

changes per hour was converted into effective leakage area (ELA) and split between the first and 

second floor of the simulated design based on fraction of volume because the use of ELA leads 

to more accurate simulation results (Kneifel, Payne et al. 2015).

All electrical and mechanical systems in the house are more energy-efficient than federal 

requirements. All light fixtures in the NZERTF are high efficiency with the use of compact 

fluorescent (CFL), linear fluorescent, and light emitting diode (LED) bulbs. The Heating, 

Ventilation, and Air-Conditioning (HVAC) system uses a high-efficiency heat pump to meet the 

heating and cooling loads of the home, while incorporating a heat recovery ventilator (HRV) 

operating year-round to meet any mechanical ventilation requirements. The household domestic 

hot water (DHW) system includes a 50-gallon heat pump water heater with a coefficient of 

performance of 2.6 and an electric back-up with a thermal efficiency of 0.98. It also uses two 

solar thermal panels and an 80-gallon storage tank to preheat water entering the heat pump water 

heater. The on-site solar PV system is a 10.2 kW system, which was the largest system that could

be installed on the top roof. For additional details on the NZERTF simulation design 

specifications and general assumptions, refer to Kneifel, Payne et al. (2015). 
11

4 Results and Discussion

Weather-related factors such as the total number of heating degree-days, cooling degree-days, 

and solar irradiance can strongly impact the overall performance of a building. Researchers 

commonly rely on TMY weather files to simulate the building’s energy performance under 

“typical” weather conditions that are representative of that building’s assumed location. 

However, the use of a single TMY weather file cannot account for the significant variations and 

trends realized in weather patterns from year-to-year. In this section, we will discuss the impacts 

of weather variability on the energy performance of NIST’s NZERTF using 34 years’ worth of 

actual weather (AMY files). Additionally, we will examine how accurately the TMY3 weather 

file represents “typical” weather conditions for the given location.

4.1 Impacts of alternative weather profiles on NZERTF electricity consumption

Figure 4-1 shows annual electricity consumption by the NZERTF from 1980 through 2013, the 

trend line for consumption based on the AMY files (red), and annual consumption based on 

TMY3 weather conditions (dashed orange). Annual household electricity consumption varies 

from 11 300 kWh in 2001 to 12 983 kWh in 1989, with a long-term average downward trend of 

13.4 kWh/yr. Use of the TMY3 weather file results in 11 783 kWh of annual electricity 

consumption, and an average percent difference of -3.1 %. Use of the TMY3 weather file does 

not appear to be representative of average conditions as it leads to an underestimation of total 

electricity use.

Figure 4-1: Annual Household Electricity Use for AMY and TMY3 Files (1980-2013)

The noticeable spikes in electricity use in years 1989 and 1996, along with the downward trend 

in consumption across the AMY files, are associated with variability in annual heating and 

cooling loads. Figure 4-2 displays the total kilowatt-hours of electricity used to satisfy household

heating and cooling loads given each weather file. On average, electricity demands associated 
12

with heating trend downward, and cooling related electricity use trends upward. Indicated by 

differences in the slopes of the two trend lines, simulated household electricity demands for 

heating are more responsive (24.6 kWh/year) to changes in AMY file assumptions than cooling 

(12.3 kWh/year)4. With the state of Maryland being located in a moderately temperate climate 

that is heating-driven and growing warmer on average each year, the NZERTF will marginally 

continue swapping their heating demands for more cooling. Under typical weather conditions, 

heating- and cooling-related electricity consumption is below the 34-year averages. In the case of 

cooling, it is above the 34-year average. The results once again highlight the inability of the 

TMY3 to accurately reflect average weather conditions for the area.

Figure 4-2: Annual Electricity Usage for Household Heating and Cooling Demands

Figure 4-3 displays the electricity consumed each year to meet the simulated heating and cooling 

loads. In the case of the 1989 and 1996 AMY weather files, there are spikes in annual electricity 

demands (Figure 2). Figure 4-3 below suggests that in these years a combination of above 

average heating and cooling loads are responsible for the consumption spikes. In years such as 

1990 and 2006, above average cooling loads are outweighed by below average heating loads, 

leading to smaller overall electricity demands relative to the alternative weather files. 

 

4 Data for Figure 4-2 can be found in Table A-1 of the Appendix.
13

Figure 4-3: Proportional Electricity use for Household Heating and Cooling Demands for 

TMY3 and AMY Files (1980-2013)

Based on the results in Figure 4-2 and Figure 4-3, the average decline in heating loads and 

average growth in cooling loads suggest that the overall local climate captured by the AMY files 

is becoming warmer over time. A more accurate indicator of changing climate conditions is the 

variation in the number of HDDs and CDDs over time. Figure 4-4 shows the total number of 

HDDs, CDDs, and their sum across all 34 AMY files. From 1980 to 2013, there is a slight

downward trend in total HDDs. In the case of CDDs, a slightly upward trend is revealed. Fewer 

HDDs and more CDDs reveal a slowly changing climate that is growing warmer over time. This 

trend combined with the higher efficiency of the cooling equipment relative to the heating 

equipment could lead to continued reductions in energy use in the near future. Average weather 

conditions captured by the TMY3 file reflect a higher number of CDDs relative to HDDs; 

however, the existing process used to derive the TMY3 weather conditions will continue to 

produce results that understate the impacts of local weather conditions on household energy 

performance.

It is not clear what is driving the total amount of HDDs and CDDs in periods where there is a 

spike or a large drop in electricity demands. These anomalies may best be explained by natural 

climatic events. For example, the spike in electricity consumption given 2003 weather conditions 

is again primarily associated with larger heating loads. This weather year coincides with a 

moderate El Niño year, in which winters in the Southeast during this period are characterized as 

being cooler and wetter, which could explain larger heating loads and higher electricity demands. 

The underlying data does not provide enough information to discern if and what climatic 

occurrences are at play. However, it is important to acknowledge the possibility of some existing 

correlation.
14

Figure 4-4: Total Number of HDD and CDD for AMY Files (1980 through 2013)

Assuming typical weather conditions, total electricity use (11 783 kWh) is roughly 3 % and 

4 % below the average (12 157 kWh) and median (12 300 kWh) consumption levels, 

respectively, for AMY files 1983 through 2013. Figure 4-5 shows the distribution of annual 

electricity use for all 34 AMY files as well as indicators of the median and TMY3 

consumption levels. The distribution is left skewed implying that majority of the predicted 

consumption is concentrated to the right of the average. 

Figure 4-5: Histogram of Annual Household Electricity Use for the TMY3 and AMY Files 

(1983 through 2013)
15

4.2 Impacts of alternative weather profiles on the on-site solar photovoltaic electricity 

production

The NZERTF design is setup to operate solely on electricity, drawing its power from both the 

local utility and the electricity generated from the on-site 10.2 kW solar PV system. 

Figure 4-6 displays both annual solar PV electricity production and Global Horizontal 

Irradiance (GHI) measurements for all 34 AMY weather files and the TMY3 weather file. 

The least production (13 053 kWh) occurs under weather conditions captured by the 2003 

AMY weather file. The largest (14 931 kWh) is realized under 2010 conditions. Assumptions 

of typical weather conditions lead to an overestimation of solar electricity generation with an 

annual production of roughly 15 131 kWh. Global Horizontal Irradiance (GHI) is the total 

amount of shortwave radiation received from the sun by a surface lying horizontal to the 

ground. GHI measures include both the Diffuse Horizontal Irradiance (DHI) and the Direct 

Normal Irradiance (DNI) (National Renewable Energy Laboratory (NREL) 2014). Solar 

electricity production is largely a function of the level of solar radiation to which the system 

is exposed (Ganguli and Singh 2010). Figure 4-6 confirms this relationship by revealing that 

periods of lower irradiance coincide with lower levels of PV production, and higher 

irradiance levels coincide with greater production. A visible upward trend in both annual 

production and GHI levels suggests that, on average, more solar electricity generation by the 

home can be expected as average local solar radiation levels continue to increase over time. 

Data on the home’s annual PV production can be found in Table A-2 of the Appendix.

Figure 4-6: Annual NZERTF Solar PV Production and Global Horizontal Irradiance for 

TMY3 and AMY Files (1980 through 2013)

The noticeably higher solar electricity generation predicted using TMY3 data are largely 

explained by the inflated solar irradiance levels captured by the weather file. Examining the 

monthly GHI averages helps to further illustrate any variations between the TMY3 and AMY 
16

irradiance measures. Figure 4-7 shows the average GHI levels for the TMY3 dataset and four 

AMY datasets (1980, 1988, 2010, and 2013). The four datasets were selected based on 

having the highest average measured total global horizontal irradiance levels of all the 34

AMY files. The TMY3 irradiance levels are consistently higher than the actual collected data 

during the late spring/summer months (April through September). During the late fall and 

winter months, TMY3 average GHI levels are relatively comparable. Given the relationship 

between PV production and solar irradiance, it is clear that that above average “typical” solar 

irradiance values will likely cause an overestimation of solar renewable energy generation.

Figure 4-7: Average Global Horizontal Irradiance Measures for the TMY3 and Select 

AMY Weather Files

A histogram of solar PV electricity production developed based on the 35 simulations is 

shown in Figure 4-8. The average (not pictured), median, and mode (not pictured) production 

levels are 13 972 kWh, 13 965 kWh, and 13 703 kWh, respectively. All three measures fall 

within the 13 701 kWh to 14 050 kWh bin range. Given that TMY3 production falls at the 

top end of the distribution (15 131 kWh or 8 % above the average AMY production), it is 

very unlikely that actual yearly production will achieve these levels. 
17

Figure 4-8: Histogram of Annual Solar PV Electricity Production for the TMY3 and AMY 

Files (1983 through 2013)

4.3 Impacts of alternative weather profiles on annual net electricity production

Sections 4.1 and 4.2 discussed the simulated responses of household consumption and 

production to different assumption of local weather conditions separately. This section will 

consider the impacts of alternative weather files on net electricity production or the excess 

electricity produced after household energy demands have been satisfied.

Net electricity production in this study is described as the difference between total electricity 

generated on-site and the total electricity consumed by the NZERTF each year. In Figure 

4-9, the NZERTF not only achieves net-zero, but is also a net producer across all 34 AMY 

files. Similar to PV production, the rising trend line indicates that the home, on average, will 

increasingly become a net producer of energy over time. The simulated NZERTF design 

realizes the greatest excess production under 2001 weather conditions. Use of the 2001 

weather file corresponds to the least amount of electricity consumption and the third largest 

level of PV production relative to all other simulations. The low annual consumption coupled 

with high annual production result in significant net production. Net production using the 

TMY3 file is higher than all but one AMY file. Data for the annual net production can be 

found in Table A-2 of the Appendix.
18

Figure 4-9: Annual Net Electricity Production for TMY3 and AMY Files (1980 through 

2013)

The histogram of annual net production shown in Figure 4-10 shows that both the median 

and average (1815 kWh) net electricity production lie within the 1501 kWh to 2000 kWh bin 

range

5

. Like PV production, the likelihood that actual net production will be comparable to 

our TMY3 simulation is minimal based on where it lies within the distribution.

Figure 4-10: Histogram of Annual Net Electricity Production for the TMY3 and AMY 

Files (1983 through 2013)

 

5 All calculated net production levels for the 34 simulations are unique – therefore, no mode exists.
19

4.4 Impacts of alternative weather profiles on building HVAC system peak electricity 

demands

Annual peak electricity demands for the net-zero home’s HVAC system are displayed in 

Figure 4-116. Use of the TMY3 weather file can underestimate peak electricity demands by 

as much 69 % (2059 W), with only a slight overestimation of demand up to 1.7 % (52 W). 

The relationship between electricity demands and the number of HDDs and CDDs –

observed in Figure 4-4 – is more succinctly reflected in Figure 4-11. As the local climate 

grows warmer, particularly during the heating season, the household requires less 

electricity each year to heat the home. Despite increases in average cooling-based 

electricity demands, it is not enough to offset the average reductions in heating-based 

electricity, resulting in less annual electricity consumption related to use of the HVAC 

system over time. The negatively sloped trend line in Figure 4-11 suggests that average 

peak demands will level off as the local climate shifts towards being more cooling 

dominated and the annual electricity demands of the HVAC system decline.

Figure 4-11: Annual Peak Electricity Demands for Building HVAC System 

 

6 Data for Figure 4-11 are listed in Table A-3 in the Appendix.
20

5 Conclusion

This study seeks to expand upon existing literature by observing the variability in the energy 

performance of a net-zero energy residential building across 34 years of weather data specific 

to the Gaithersburg, MD area. In an effort to determine the likelihood that the Typical 

Meteorological Year 3 (TMY3) data is closely representative of “typical” weather conditions 

in the Gaithersburg area, we compare simulation results using the TMY3 data file to those 

simulations using actual historical data collected between 1980 and 2013. 

Despite variation in annual household electricity use between 11 300 kWh and 12 983 kWh 

from year to year there is a consistent long-term average reduction in energy use of 

approximately 13 kWh per year. Under assumptions of “typical” weather conditions, annual 

electricity use is roughly 11 783 kWh and lower than 76 % of the measured consumption 

realized using the 34 AMY files. A majority of annual household energy usage is based on 

the heating and cooling needs of its occupants and is primarily dependent on external weather 

conditions. Therefore, any realized average consumption trends will be dependent on

changes in the local climate. A slight reduction in the average number of annual HDDs 

combined with an average increase in the total number of CDDs from year to year suggests a 

shifting from a heating dominated climate towards a cooling dominated one. Additionally, 

homes built according to the NZERTF specifications in the area – which include equipment 

that is more efficient at cooling than heating – are likely to respond to the changing climate 

conditions by consuming less electricity. Given that total electricity usage falls well below 

average consumption in the case of TMY3 weather conditions, it is unlikely that the TMY3 

file is appropriate for use in predicting typical annual household consumption.

Integrating the 10.2 kW solar PV system into the NZERTF design leads to annual generation 

of 13 000 kWh or more across all 35 weather files. Annual solar electricity generation is 

functional on yearly solar irradiance, where periods of higher solar irradiance coincide with 

greater production. Historical GHI measurements in the AMY files indicate marginal 

increases in local average solar radiation levels. Because solar electricity generation is 

functional on solar irradiance, it comes as no surprise that the simulated yearly NZERTF 

solar PV production follows a similar trend as rising GHI levels. The use of the KGAI TMY3 

weather file in the simulation of the NZERTF results in a PV production more than 200 kWh 

greater than when using any of the AMY weather files from previous years. Thus, use of this 

file in the future may overestimate that production, as well as generate other results that are 

not typical for that location as shown by historical data.

Measures of annual net electricity production provide insight into just how “net-zero” the 

NZERTF is under different assumptions of local weather conditions. Net production is 

greater than zero in all 35 cases and will likely become more of a net producer over time 

assuming equipment efficiencies increase and building parameters remain stable over time. 

Use of the TMY3 weather file causes an overestimation of net electricity production and, 
21

given its distance from both the average and median level of net production, it is not likely 

that net production simulated using the TMY3 data will accurately predict actual 

performance of the NZERTF. 

Similar to total annual household electricity consumption, a less heating dominated climate 

leads to lower average peak electricity demands for the HVAC system. The “tighter” 

building envelope ensures that comfortable set point temperatures within the home are more 

likely to be sustained and that less electricity will be needed to heat it during periods of cold 

weather. Building simulations conducted based on assumptions of TMY3 weather conditions 

can significantly understate predicted peak demands. 

Our findings highlight the importance of considering weather variability in the energy 

performance of net-zero building designs. Both determinants of a building being net-zero 

from year to year (energy consumption and production) are directly impacted by outside 

weather conditions. Any changes in annual local climatic conditions stand to influence a 

building’s ability to be a net energy producer. As shown by our results, relying solely on 

TMY3 data to capture average weather conditions can produce results inconsistent with longterm historical averages. Decisions to leave out extreme weather anomalies along with a 

number of other factors when generating TMY3 weather files may cause files not to 

accurately capture average weather conditions for a given location. It is not to say that all 

TMY3 weather files will produce misleading results in all locations for all types of building 

designs. However, some consideration should be given for actual historical weather data 

when conducting building simulations and testing the robustness of the building’s 

performance capabilities. Future research should consider alternative methods to developing 

representative weather data files, either through different statistical approaches to developing 

a TMY file or through multi-year simulation run periods. Also, alternative residential 

building designs and/or locations should be considered to determine if the results can be 

generalized to a broader range of scenarios.
22

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25

A Appendix

Table A1: Annual Houshold Electricity Demands, Heating-based Electricity Demands, and 

Cooling-based Electricity Demands under each Weather File7

Weather 

File

Consumption

(kWh)

Consumption –

Heating (kWh)

Consumption –

Cooling (kWh)

Consumption –

Other (kWh)

1980 12 417 3342 2072 7003

1981 12 331 3364 1950 7017

1982 12 578 3272 2297 7009

1983 12 494 3156 2314 7024

1984 12 456 3011 2411 7034

1985 12 325 3108 2200 7017

1986 12 267 2972 2292 7003

1987 12 469 3006 2461 7002

1988 12 117 2989 2167 6961

1989 12 983 3278 2694 7011

1990 11 589 2169 2431 6989

1991 12 031 2489 2542 7000

1992 11 925 2808 2081 7036

1993 12 403 3089 2303 7011

1994 12 464 3078 2372 7014

1995 12 439 3056 2386 6997

1996 12 817 3381 2414 7022

1997 11 656 2794 1867 6995

1998 11 500 2114 2383 7003

1999 11 500 2406 2125 6969

2000 11 717 2775 1958 6984

2001 11 300 2267 2064 6969

 

7

“Consumption – Other” includes all other electricity consumption, including DHW, plug loads and appliances, and 

lighting.
26

2002 11 783 2400 2408 6975

2003 12 597 2969 2603 7025

2004 12 603 2739 2856 7008

2005 12 417 2769 2658 6990

2006 11 492 2008 2511 6973

2007 12 106 2650 2483 6973

2008 11 800 2444 2353 7003

2009 12 228 2742 2475 7011

2010 12 339 2817 2569 6953

2011 12 275 2389 2900 6986

2012 11 561 2044 2550 6967

2013 12 367 2703 2686 6978

TMY3 11 783 1733 3108 6942
27

Table A2: Annual Houshold Electricity Demands, Solar PV Production, and Net Solar PV 

Production under each Weather File

Weather File Electricity Consumption

(kWh)

PV Production 

(kWh)

Net Production 

(kWh)

1980 12 417 14 175 1 758

1981 12 331 13 911 1 581

1982 12 578 13 714 1 136

1983 12 494 13 600 1 106

1984 12 456 13 283 828

1985 12 325 13 678 1 353

1986 12 267 13 711 1 444

1987 12 469 13 967 1 497

1988 12 117 14 739 2 622

1989 12 983 13 456 472

1990 11 589 14 014 2 425

1991 12 031 13 861 1 831

1992 11 925 13 164 1 239

1993 12 403 13 703 1 300

1994 12 464 13 886 1 422

1995 12 439 14 142 1 703

1996 12 817 13 425 608

1997 11 656 14 372 2 717

1998 11 500 13 983 2 483

1999 11 500 14 697 3 197

2000 11 717 14 169 2 453

2001 11 300 14 836 3 536

2002 11 783 13 975 2 192

2003 12 597 13 053 456

2004 12 603 13 564 961
28

2005 12 417 13 703 1 286

2006 11 492 14 483 2 992

2007 12 106 14 703 2 597

2008 11 800 14 178 2 378

2009 12 228 13 297 1 069

2010 12 339 14 931 2 592

2011 12 275 13 964 1 689

2012 11 561 14 492 2 931

2013 12 367 14 214 1 847

TMY3 11 783 15 131 3 347
29

Table A3: Peak Electricity Demands for the Building HVAC System under each Weather 

File

Weather File Peak Electricity Demand (W)

1980 3423

1981 4541

1982 4857

1983 4925

1984 4481

1985 5041

1986 3259

1987 4428

1988 3099

1989 4500

1990 3028

1991 3004

1992 2993

1993 4120

1994 4832

1995 3299

1996 4461

1997 3117

1998 2959

1999 2956

2000 3278

2001 2931

2002 2976

2003 2966

2004 3341

2005 3170
30

2006 2986

2007 3079

2008 3005

2009 3030

2010 2978

2011 3139

2012 3024

2013 2962

TMY 3 2983