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Impact of Typical Weather Year Selection Approaches on Energy Analysis of Buildings

Donghyun Seo Yu Joe Huang Moncef Krarti, PhD, PEStudent Member ASHRAE Member ASHRAE Member ASHRAE

OR-10-045 (RP-1477)

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This paper is based on findings resulting from ASHRAE Research Project RP-1477.

ABSTRACT

The paper summarizes the results of a series of analyses toassess the impact of the selection procedure used to generate oftypical year weather on annual building energy use. The build-ing energy analysis is carried out using detailed whole buildingsimulation tool that utilizes hourly typical year weather files.Annual energy use for prototypical office buildings are obtainedfor 10 sites representing a wide range of climatic conditions inthe U.S. In particular, the analyses presented in this paper eval-uate the impacts of weighting factors for various weather vari-ables and of the length of historical data used on predicting theenergy use of building systems.

The results of the analysis indicated that a maximum of 5%difference in annual office building energy use can result fromthe selection procedure used to generate typical weather yearfor the 10 US climates considered in this study.

INTRODUCTION

Detailed building energy simulation tools such as DOE-2 (LBL 1981) and EnergyPlus (Crawley et al. 2000) arecommonly used to design sustainable buildings. These toolsrequire hourly typical year weather files in order to estimatebuilding energy use and building indoor comfort. Severalprocedures do exist to develop typical weather data using asingle year of hourly data that are selected to represent therange of weather patterns that can be found in a multi-year dataset (Keeble 1990).

Several approaches have been utilized to develop andformat a typical weather year for building energy analysisincluding the ASHRAE Test Reference Year or TRY (ASH-

RAE 1976), Typical Meteorological Year or TMY (Hall et al.1978), the Weather Year for Energy Calculations (Crow 1981),TMY2 (Marion and Urban 1995), ASHRAE InternationalWeather for Energy Calculations or IWEC (Thevenard andBrunger 2002), and more recently TMY3 (Wilcox and Marion2008). Other selection approaches have been proposed (Hui1996).

Limited analyses have been reported to assess the impactof the selection criteria for generating the typical weather yearon predicting the performance of building energy systems(Arigirou et al. 1999 and Massie and Kreider 2001). In partic-ular, Argiriou et al. (1999) tested several different TMYweather files generation procedures for Athens with 20 years(1977 to 1996) measured weather data. They consideredseveral configurations of weighting factors and four methodsto generate typical weather year including: the TMY method(Hall et al. 1978), a Danish method (Lund and Eidorff 1980),Festa-Ratto method (Festa and Ratto 1993), and 20-year aver-age meteorological year. They developed weather data evalu-ation system based on building and solar systems.Specifically, they utilized a simple solar water heating system,a building, a photovoltaic system, and a large scale solar heat-ing system with inter-seasonal storage, and PV system.TRNSYS is used in the evaluation analysis (Anon 2000). Amodified Festa-Ratto method was found to provide the bestdata set for Athens. Massie and Kreider (2001) estimated thediscrepancies between TMY and TMY2s in predicting theperformance of a photovoltaic system and a wind turbine.

In this paper, a series of sensitivity analyses is presentedto assess the impact of the typical weather selection criteria on

©2010 ASHRAE. THIS PREPRINT MAY NOT BE DISTRIBUTED IN PAPER OR DIGITAL FORM IN WHOLE OR IN PART. IT IS FOR DISCUSSION PURPOSES ONLYAT THE 2010 ASHRAE WINTER CONFERENCE. The archival version of this paper along with comments and author responses will be published in ASHRAETransactions, Volume 116, Part 1. ASHRAE must receive written questions or comments regarding this paper by February 12, 2010, if they are to be included inTransactions.

Donghyun Seo is a graduate student and Moncef Krarti, PhD, PE is a Professor and Associate Chair in the Civil, Environmental, and Archi-tectural Engineering Department at the University of Colorado, Boulder, CO. Joe Huang is president of White Box Technologies, Inc., Moraga,CA, and formerly a staff scientist at Lawrence Berkeley National Laboratory, Berkeley, CA.


building energy analysis results. In particular, the impacts onannual energy use predictions of weighting factors associatedwith various weather variables and of the length of historicaldata used in the selection procedure are evaluated throughoutthe paper. The analysis is carried out for 10 U.S. sites for whichmeasured weather data for at least 30 years are reported. First,a brief description of the prototypical office building used inthe simulation analysis is provided. Then, the results of thesimulation analyses are presented and discussed.

BUILDING MODEL DESCRIPTION

For this analysis, a prototypical office building wasmodeled using a whole-building hourly simulation tool (LBL1981). The prototypical building model consists of 3-storyoffice building with a gross floor area of 48,000 ft2 (4461 m2)as illustrated in Figure 1. A power density of 0.8 W/ft2 (8.7 W/m2) is assumed for lighting systems equipped with electronicballasts and daylight control sensors. Daylight control cover-age area is 54% covering all the perimeter offices. Officeequipment power density is assumed to be 1.0 W/ft2 (10.8 W/m2) for computers, laser printers, photocopiers, and facsimilemachines. The building envelope is assumed to include 40%fenestration-to-wall ratio with glazing varying by climate to

meet ASHRAE Standard 90.1 (ASHRAE 2004). The outsideair ventilation rate is set to be 20 CFM/person (9.5 L/s perperson). The HVAC system for the building consists of a vari-able air volume (VAV) system with hot water reheat coils anddry-bulb outside air economizer. The central plant includes0.55 kW per ton centrifugal chillers and a 90% efficiency gas-fired boiler. Table 1 provides a summary of the basic featuresof the prototypical office building. Table 2 lists the 10 U.S.sites used to carry out the analysis presented in this paper.

Figure 1 Office building model.

Table 1. Summary of the Basic Features of the Prototypical Office Building Model

Architectural Mechanical

Floor Area 16,000 sf (1487 m2) Design Temp. 75°F/72°F [Cooling/Heating] (24°C/22°C)

Gross Area 48000 sf (4461 m2) Thermo. Set 76°F/82°F (24.5°C/28°C) [Cooling]

60°F/64°F (15.5°C/18°C) [Heating]

Peri. Depth 20 ft (6.1 m) [57% of floor area] HVAC VAV+Reheat

Wall Wood, metal frame, R-19 batt. Overall R-10.5 Fans Variable Speed Drives (VSDs)

Roof Built-up roof, metal frame, R-18 insulation, Overall R-22

OA 20 cfm/person (9.5 L/s per person)

Economizer control

WWR 40% (floor to ceiling) Chiller 0.55 kW/ton

Glazing Variable with climate Boiler 90%

Daylight Control Perimeter zones

Shading 3 ft (0.9 m) overhang on S, E, and W 50fc design level

LPD/ EPD 0.8 /1.0 W/sf(8.7/10.8 W/m2)

Dimming control

Table 2. Selected 10 U.S. Sites

USAF Stations STATE LAT LON ELEV (m) Climate Zone (ASHRAE Std 90.1)

725180 ALBANY COUNTY ARPT NY 42.45N 073.48W 89 5A

722190 ATLANTA INTL ARPT GA 33.39N 084.25W 315 3A

911820 HONOLULU INTL ARPT HI 21.21N 157.56W 5 1A

723860 LAS VEGAS/MCCARRAN NV 36.05N 115.10W 664 5B

726410 MADISON/DANE RGNL WI 43.08N 089.20W 264 6A

722020 MIAMI INTL AIRPORT FL 25.49N 080.17W 4 2A

725500 OMAHA/EPPLEY FIELD NE 41.18N 095.54W 299 5A

722780 PHOENIX/SKY HARBOR AZ 33.26N 112.01W 337 3B

727930 SEATTLE-TACOMA INTL WA 47.27N 122.18W 137 5B

722740 TUCSON INTL AIR PORT AZ 32.07N 110.56W 779 3B

2 OR-10-045 (RP-1477)


IMPACT OF TYPICAL WEATHER YEAR

Using the simulation building model, annual total energyconsumption and peak electrical demand are estimated forvarious annual typical weather data sets and are compared tothose mean values obtained from 30 years of simulation data.The typical weather year data sets considered in this analysisinclude:

• IWEC, generated data sets for the 10 U.S. sites using thebasic IWEC selection procedure (Thevenard and Brun-ger 2002),

• TMY, generated data sets for the 10 U.S. sites using theTMY selection procedure outlined by Hall et al. (1978),

• TMY2n, generated data sets for the 10 U.S. sites usingthe TMY2 selection procedure outlined by Marion andUrban (1995),

• TMY2r, existing data sets for the 10 U.S. sites publishedby NREL (Marion and Urban 1995), and

• EqWt, generated data sets for the 10 U.S. sites using aselection procedure based on equal weighting factors asdescribed in Donghyun et al. (2008).

To be consistent with the published TMY2r weather files,meteorological data sets of 30 years spanning from 1961 to1990 were used in generating IWEC, TMY, TMY2n, andEqWt weather files (Donghyun et al. 2008).

Table 3 summarizes the results of the simulation analysisand provides the annual energy use differences in terms ofheating, cooling, and total using the simulation resultsobtained using the 30-year average data set as a reference.Generally, the largest differences (over 5%) are found for heat-ing energy use and for warm climates such as Phoenix and LasVegas. For all sites and weather data sets, cooling energydifferences are generally small ranging from –3 to 1%. Interms of total building energy use, IWEC and TMY2n datasets provide the least differences and EqWt and TMY2r datasets exhibit the largest differences. However, these differencesare not significant and are generally below 2%.

Table 4 presents the differences in electrical annual peakdemand values obtained for various typical weather data usingthe 30-year averages as references. For all sites and weatherdata sets, peak demand differences are below 5%. The aver-ages of the differences for all 10 cities range from 1.6%

Table 3. Annual Total Building Energy Use Differences Between Typical Weather Data and 30-Year Average

IWEC EqWt TMY TMY2n TMY2r

Albany Elec (kWh) 296369 –0.5% 297680 0.2% 296918 0.0% 297164 0.1% 297259 –0.3%Gas (Mbtu/GJ) 736.4 –0.2% 735.7 1.3% 735.9 1.3% 735.4 1.2% 730.6 0.1%

Total (MBtu/GJ) 1747.9 1.4% 1751.7 –0.7% 1749.3 –0.5% 1749.6 –0.6% 1745.1 0.6%Atlanta Elec (kWh) 314559 –0.5% 315552 –0.2% 310582 –1.7% 314401 –0.5% 315018 –0.3%

Gas (Mbtu/GJ) 243.4 6.4% 222 –2.9% 253.5 10.9% 232.2 1.5% 228.6 0.0%Total (MBtu/GJ) 1317.0 0.7% 1299.0 –0.6% 1313.5 0.5% 1305.3 –0.2% 1303.8 –0.3%

Honolulu Elec (kWh) 379932 0.0% 378877 –0.3% 368211 –3.1% 380320 0.1% 377504 –0.7%Gas (Mbtu/GJ) 46.9 1.9% 46.9 1.9% 46.5 1.0% 46.9 1.9% 46.2 0.4%

Total (MBtu/GJ) 1343.6 0.0% 1340.0 –0.2% 1303.2 –3.0% 1344.9 0.1% 1334.6 –0.6%Las Vegas Elec (kWh) 333272 –0.1% 332231 –0.4% 333191 –0.1% 331743 –0.6% 333071 –0.2%

Gas (Mbtu/GJ) 123.4 –3.3% 124.5 –2.5% 128.7 0.8% 123.5 –3.3% 114.4 –10.4%Total (MBtu/GJ) 1260.9 –0.4% 1258.4 –0.6% 1265.9 0.0% 1255.7 –0.8% 1251.2 –1.2%

Madison Elec (kWh) 302346 0.0% 301992 –0.1% 302267 0.0% 301990 –0.1% 300074 –0.7%Gas (Mbtu/GJ) 793.2 –2.3% 790.1 –2.7% 817.9 0.7% 794.8 –2.1% 779.3 –4.0%

Total (MBtu/GJ) 1825.1 –1.0% 1820.8 –1.2% 1849.5 0.3% 1825.5 –1.0% 1803.5 –2.2%Miami Elec (kWh) 377545 –0.5% 377545 –0.5% 378260 –0.3% 377428 –0.5% 379842 0.1%

Gas (Mbtu/GJ) 49 1.4% 49 1.4% 49 1.4% 49 1.4% 47.8 –1.1%Total (MBtu/GJ) 1337.6 –0.4% 1337.6 –0.4% 1340.0 –0.2% 1337.2 –0.4% 1344.2 0.1%

Omaha Elec (kWh) 311337 0.2% 309205 –0.5% 310159 –0.2% 309008 –0.6% 310159 –0.2%Gas (Mbtu/GJ) 655.8 1.9% 631.9 –1.9% 627.8 –2.5% 634.9 –1.4% 627.8 –2.5%

Total (MBtu/GJ) 1718.4 0.8% 1687.2 –1.0% 1686.4 –1.1% 1689.5 –0.9% 1686.4 –1.1%Phoenix Elec (kWh) 358285 0.7% 357580 0.5% 355185 –0.2% 358034 0.6% 355185 –0.2%

Gas (Mbtu/GJ) 70.4 –11.5% 75.7 –4.8% 79.2 –0.4% 72.1 –9.3% 79.2 –0.4%Total (MBtu/GJ) 1293.2 0.0% 1296.1 0.2% 1291.4 –0.2% 1294.1 0.0% 1291.4 –0.2%

Seattle Elec (kWh) 268311 –0.7% 269452 –0.3% 270812 0.2% 269322 –0.3% 270812 0.2%Gas (Mbtu/GJ) 400.5 –0.2% 381.9 –4.8% 400.6 –0.2% 385.6 –3.9% 400.6 –0.2%

Total (MBtu/GJ) 1316.2 –0.5% 1301.5 –1.7% 1324.9 0.1% 1304.8 –1.4% 1324.9 0.1%Tucson Elec (kWh) 333749 –0.2% 333126 –0.4% 335585 0.3% 332106 –0.7% 335585 0.3%

Gas (Mbtu/GJ) 87.5 –6.4% 91.7 –2.0% 89.1 –4.7% 94.1 0.6% 89.1 –4.7%Total (MBtu/GJ) 1226.6 –0.7% 1228.7 –0.5% 1234.5 –0.1% 1227.6 –0.6% 1234.5 –0.1%

OR-10-045 (RP-1477) 3


(IWEC) to 2.4% (TMY). The use of TMY2n and TMY2r leadsto respectively 1.84% and 2.17% average differences for theannual electrical peak demand for the prototypical officebuilding considered in this analysis. Figures 2 and 3 providescatter diagrams for respectively the monthly electrical peakdemand and total building energy use obtained from theenergy analysis of the prototypical office building usingTMY2n and TMYr against the same results generated using30-year average data.

Figure 4 illustrates the scatter diagrams of monthly cool-ing and heating energy end-uses obtained using TMY2n andTMY2r weather data plotted against monthly means calcu-lated for the simulation results generated for the 30-yearperiod (1961 to 1990) for all the 10 sites. Table 5 summarizesthe statistical results of the comparative analysis betweenTMY2n and TMY2r and the mean values obtained for the 30-year for both heating and cooling energy end-uses.

Both TMY2n and TMY2r annual weather data setsprovide energy use predictions that are in good agreement withthe mean values of the simulation results compiled for 30years. Generally, the results obtained using TMY2n weatherare statistically slightly closer to the 30-year mean predictionsfor both natural gas and electricity annual energy use thanthose generated with TMY2r. The mean of the differencesassociated with TMY2n and TMY2r are only 0.15% and0.25%, respectively, while the standard deviation of the differ-ences is only 0% and 1.3%, respectively.

IMPACT OF WEIGHTING FACTORS

Using the simulation building model, annual total energyconsumption are estimated for various TMY2 weather datasets obtained using different configurations weighting factors

and are compared to those mean values obtained from 30 yearsweather data. Table 6 lists the weighting factors associatedwith all weather parameters (Donghyun et al. 2008). Dewpoint temperature (DPT) and wind speed (WSP) weightingfactors are set to be constant (10%) due to their relatively smalleffect on building energy use. A 6-digit code is assigned toeach weighting factor configuration. The first 2-digit set is theweight factor (in fraction multiplied by 10) associated withdry-bulb temperature (DBT), the second 2-digit set is theweighting factor associated with the global horizontal solarirradiation (GHI), and the third 2-digit set is the weightingfactor associated with the direct normal solar irradiation(DNI). For example, the code 040400 refers to the case wherea weight factor of 0.4 is assigned for DBT, 0.4 for GHI, and 0.0for DNI.

Table 7 summarizes the results of the simulation analysiswith various weighting factors and provides the annual energyuse differences in terms of heating, cooling, and total using thesimulation results obtained using the 30-year average data setas references. Generally, the largest differences (over 5%) arefound for heating energy use and for warm climates such asAtlanta, Phoenix and Las Vegas. For all sites and weather datasets, cooling energy differences are generally small rangingfrom 3.1% to –1.5%.

For the annual total building energy use, the differences inthe simulation results obtained from the TMY2 data sets andmean value for the 30-year period are below 3% regardless ofweighting configuration. However, these differences canreach 8% for the annual heating energy use when the weight-ing factor on dry bulb temperature is 0.2 or zero.

Figure 5 illustrates the R2 variation of the correlationbetween monthly total building energy use values predictedusing TMY2 and those obtained from averaging 30 years of

Table 4. Annual Electrical Peak Demand Differences Between Typical Weather Data and 30-Year Average

IWEC EqWt TMY TMY2n TMY2r

Albany Peak (kW) 135.9 136.9 135.9 135.9 135.4Diff. % 0.3% 1.0% 0.3% 0.3% –0.1%

Atlanta Peak (kW) 154.5 154.9 151.8 154.5 147.8Diff. % 3.5% 3.7% 1.7% 3.5% –1.0%

Honolulu Peak (kW) 150.5 150.5 146.7 151.8 147.2Diff. % 0.5% 0.5% –2.0% 1.4% –1.7%

Las Vegas Peak (kW) 165.5 165.4 162.3 165.5 168.1Diff. % 1.6% 1.5% –0.4% 1.6% 3.2%

Madison Peak (kW) 144.5 144.3 147.1 139.4 145.3Diff. % 2.8% 2.7% 4.6% –0.8% 3.4%

Miami Peak (kW) 164.7 164.7 156.0 164.7 159.3Diff. % 3.1% 3.1% –2.4% 3.1% –0.3%

Omaha Peak (kW) 149.8 150.5 161.4 150.4 161.4Diff. % –1.6% –1.1% 6.1% –1.2% 6.1%

Phoenix Peak (kW) 171.1 171.1 169.8 171.1 169.8Diff. % –0.6% –0.6% –1.3% –0.6% –1.3%

Seattle Peak (kW) 117.2 115.0 121.3 121.7 121.3Diff. % 1.1% –0.8% 4.6% 4.9% 4.6%

Tucson Peak (kW) 158.3 154.9 156.4 154.9 156.4Diff. % 1.2% –1.0% 0.0% –1.0% 0.0%

4 OR-10-045 (RP-1477)


Figure 2 Scatter diagrams of monthly peak demand: TMY2n and TMY2r vs. 30-year weather data.

OR-10-045 (RP-1477) 5


Figure 3 Scatter diagrams of monthly energy use: TMY2n and TMY2r vs. 30-year weather data.

6 OR-10-045 (RP-1477)


simulation data as a function of the weighting factors associ-ated with 5 U.S. sites. The R2 values are almost equal to 1.0 forall weighting factor configurations except in Atlanta. Thelower R2 values (i.e., R2 > 0.95) are obtained when the weight-ing factor of DBT is set to zero implying that DBT is a crucialparameter to the TMY weather data selection process.

IMPACT OF THE NUMBER OF WEATHER YEARS

Using the simulation building model, annual total energyconsumption are estimated for various TMY2 weather datasets generated from several periods with varying number ofyears and are compared to those mean values obtained from 30years weather data. Table 8 summarizes the results of thesimulation analysis and provides the annual energy use differ-ences in terms of electricity, natural gas, and total energy use

using the simulation results obtained using the 30-year aver-age data set as references. Generally, the largest differencesare found as expected for TMY2 generated from small numberof years.

Figure 6 illustrates the R2 variation of the correlationbetween monthly total building energy use values predictedusing TMY2 and those obtained from averaging 30 years ofsimulation data as a function of the number of years utilized togenerate the typical weather year for 10 U.S. sites. For all sitesexcept Atlanta, the correlation R2 is above 0.95 when morethan 5 years are used to generate TMY weather data. ForAtlanta, there are large differences between monthly heatingenergy use values predicted by TMY2 and mean values for 30years. At lest 12 years are needed to generate TMY2 in orderfor the value of R2 to exceed 0.90 in Atlanta.

An analysis of the historical data show that there is anincrease in average winter temperatures for Atlanta as well asPhoenix and thus a decrease in annual heating degree overbetween 1961 and 1990 as shown in Figure 7. Thus, whenTMY2 is selected based on only few years (starting from1961), the heating degree days will be higher than thatobtained from either 30-year average, or TMY2 based on 25or 30 years. Moreover, the results of Figure 7 indicates that theyears selected for TMY2 during winter months can affectsignificantly the heating energy use predicted for an officebuilding in a warm climate with relatively non negligible heat-ing needs such as Atlanta.

SUMMARY AND CONCLUSIONS

The impact of the selection of typical weather year data onpredicting annual building energy use is investigated andcompared against 30-year average data using statistical anal-

Table 5. Mean and Standard Deviation of the Monthly Values for Monthly Cooling and Heating

Energy End-Uses Obtained for the 30-Year Average, TMY2n, and TMY2r

Cooling Energy (kWh) Heating Energy [MBtu (GJ)]

30 Avg.

Mean 27329.7 Mean 26.7

Standard Deviation

4986.3 Standard Deviation

42.1

Confidence Level (95.0%)

901.3 Confidence Level (95.0%)

7.6

Mean 27262.6 Mean 26.4

TMY2n

Standard Deviation


42.2



7.6

Mean 27287.6 Mean 26.2

TMY2r

Standard Deviation


42.2



7.5

Figure 4 Scatter diagrams of monthly cooling and heatingenergy obtained using TMY2n and TMY2r againstmonthly means for 30 years (1961-1990).

Table 6. Weighting Factors Used for the Sensitivity Analysis

Weights DBT DPT WSP GHI DNI

080000 0.8 0.1 0.1 0 0

060200 0.6 0.1 0.1 0.2 0

040400 0.4 0.1 0.1 0.4 0

020600 0.2 0.1 0.1 0.6 0

000800 0 0.1 0.1 0.8 0

060002 0.6 0.1 0.1 0 0.2

040202 0.4 0.1 0.1 0.2 0.2

020402 0.2 0.1 0.1 0.4 0.2

000602 0 0.1 0.1 0.6 0.2

040004 0.4 0.1 0.1 0 0.4

020204 0.2 0.1 0.1 0.2 0.4

000404 0 0.1 0.1 0.4 0.4

020006 0.2 0.1 0.1 0 0.6

000206 0 0.1 0.1 0.2 0.6

000008 0 0.1 0.1 0 0.8

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Table 7. Annual Energy Use Predictions Using TMY2 with Different Weighting Factors Compared Against Mean Annual Energy Use Obtained with Multi-Year Simulations

Numberof yr Energy

Albany Atlanta Honolulu Las Vegas Madison

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

30 yr Avg of Multi Year Simulation

Elec (MWh) 141 — 160 — 224 — 334 — 302 —

Gas (MBtu) 726 — 229 — 46 — 128 — 812 —

Total (MBtu) 1207 — 775 — 811 — 1266 — 1844 —

Total (MWh) 354 — 227 — 237 — 371 — 540 —

080000 Elec (MWh) 142 0.6 158 –1.0 222 –0.7 334 0.2 302 –0.1

Gas (MBtu) 734 1.1 239 4.3 46 0.2 128 0.3 813 0.1

Total (MBtu) 1218 0.9 779 0.6 805 –0.6 1269 0.2 1844 0.0

Total (MWh) 357 228 236 372 540

060200 Elec (MWh) 142 0.8 158 –1.3 222 –0.7 334 0.1 302 0.0

Gas (MBtu) 730 0.5 237 3.8 47 1.9 123 –3.3 798 –1.7

Total (MBtu) 1215 0.6 776 0.2 806 –0.5 1263 –0.2 1830 –0.7

Total (MWh) 356 227 236 370 536

060002 Elec (MWh) 142 0.7 159 –0.7 223 –0.4 334 0.0 302 0.0

Gas (MBtu) 734 1.0 241 5.4 46 –0.3 123 –3.7 793 –2.4

Total (MBtu) 1218 0.9 783 1.1 808 –0.4 1261 –0.4 1824 –1.1

Total (MWh) 357 229 237 369 534

040400 Elec (MWh) 140 –0.7 158 –1.3 224 –0.1 333 –0.1 302 0.1

Gas (MBtu) 737 1.4 232 1.5 47 1.9 123 –3.3 804 –1.0

Total (MBtu) 1214 0.6 771 –0.4 811 0.0 1261 –0.4 1837 –0.4

Total (MWh) 356 226 238 369 538

040202 Elec (MWh) 142 0.7 157 –1.8 224 0.0 333 –0.2 303 0.1

Gas (MBtu) 735 1.2 232 1.5 46 –0.3 123 –3.8 804 –1.0

Total (MBtu) 1219 1.0 769 –0.8 810 –0.1 1259 –0.6 1837 –0.4

Total (MWh) 357 225 237 369 538

040004 Elec (MWh) 141 0.0 157 –1.8 224 –0.1 332 –0.6 304 0.4

Gas (MBtu) 726 –0.1 232 1.5 46 –0.3 124 –3.0 804 –1.0

Total (MBtu) 1207 –0.1 769 –0.8 810 –0.1 1256 –0.8 1841 –0.2

Total (MWh) 353 225 237 368 539

020600 Elec (MWh) 141 0.0 157 –2.2 222 –0.7 331 –0.7 301 –0.3

Gas (MBtu) 736 1.3 235 2.9 47 1.9 138 7.7 791 –2.6

Total (MBtu) 1217 0.8 769 –0.7 806 –0.6 1269 0.2 1819 –1.3

Total (MWh) 356 225 236 372 533

020402 Elec (MWh) 141 0.0 158 –1.3 223 –0.5 331 –0.9 302 –0.1

Gas (MBtu) 736 1.3 233 1.8 47 1.9 124 –3.1 789 –2.9

Total (MBtu) 1217 0.8 772 –0.4 808 –0.3 1252 –1.1 1819 –1.3

Total (MWh) 356 226 237 367 533

020204 Elec (MWh) 141 0.1 159 –0.8 224 0.0 332 –0.6 303 0.2

Gas (MBtu) 721 –0.7 225 –1.5 46 –0.3 124 –3.2 795 –2.1

Total (MBtu) 1203 –0.4 767 –1.0 811 0.0 1255 –0.9 1828 –0.9

Total (MWh) 352 225 237 368 535

020006 Elec (MWh) 141 0.2 158 –1.1 223 –0.2 332 –0.6 303 0.3

Gas (MBtu) 726 –0.1 226 –1.4 46 –0.3 126 –1.3 791 –2.6

Total (MBtu) 1207 0.0 766 –1.2 809 –0.2 1257 –0.7 1826 –1.0

Total (MWh) 354 224 237 368 535

8 OR-10-045 (RP-1477)


ysis. A comparative analysis based on the average simulationpredictions obtained for 30-year period is carried out. Impactsof weighting factors and the numbers of years used to generatetypical weather data are investigated for 10 U.S. sites.

The results of the simulation analyses presented in thispaper for prototypical office buildings show a maximum 5%difference between the simulation results obtained using anytypical weather data sets (TMY, IWEC, and TMY2) and thoseobtained by averaging the results for 30 years for the 10 U.S.climates considered in this paper. In terms of total building

energy use, IWEC and TMY2n data sets provide the leastdifferences against long-term energy use predictions. More-over, the results indicate that, for the 10 U.S. climates consid-ered in the study, 15 years of recorded data would be sufficientto generate a typical weather year suitable for energy analysisof prototypical office buildings. Additional work is needed togeneralize the results for other climates and building types.

REFERENCES

Argiriou A., S. Lykoudis, S. Kontoyiannidis, C.A. Balaras,A. Asimakopoulos, M. Petrakis, and P. Kassomenos.1999. Comparison of methodologies for TMY genera-tion using 20 years data for Athens, Greece. SolarEnergy 66(1):33-45.

Anon. 2000. TRNSYS: Transient system simulation program.Solar Energy Laboratory, University of Wisconsin,Madison, WI.

ASHRAE. 1976. ASHRAE handbook of fundamentals.American Society of Heating Refrigeration and Air-Conditioning Engineers, Inc., Atlanta.

Crawley, D.B., L.K. Lawrie, C.O. Pedersen, and F.C. Win-kelmann. 2000. Energy plus: Energy simulation pro-gram. ASHRAE Journal 42(4):49-56.

Crow, L.W., 1981, Development of hourly data for weatheryear for energy calculations (WYEC), including solar

000800 Elec (MWh) 140 –0.7 158 –1.4 224 –0.2 331 –0.8 301 –0.3

Gas (MBtu) 739 1.8 234 2.4 47 1.9 131 2.5 775 –4.5

Total (MBtu) 1217 0.8 773 –0.3 810 –0.1 1260 –0.5 1804 –2.2

Total (MWh) 356 226 237 369 528

000602 Elec (MWh) 140 –0.7 158 –0.9 223 –0.2 329 –1.5 302 –0.1

Gas (MBtu) 739 1.8 211 –7.5 47 1.9 133 4.2 784 –3.5

Total (MBtu) 1217 0.8 752 –2.9 810 –0.1 1254 –0.9 1814 –1.6

Total (MWh) 356 220 237 367 531

000404 Elec (MWh) 140 –0.5 158 –0.9 225 0.2 332 –0.3 303 0.2

Gas (MBtu) 739 1.8 240 4.7 45 –1.4 129 0.7 772 –5.0

Total (MBtu) 1218 0.8 780 0.7 812 0.1 1263 –0.2 1805 –2.1

Total (MWh) 357 229 238 370 529

000206 Elec (MWh) 141 0.3 157 –1.7 224 –0.1 333 –0.2 303 0.2

Gas (MBtu) 725 –0.2 244 6.8 45 –1.4 122 –4.4 772 –5.0

Total (MBtu) 1207 0.0 781 0.8 809 –0.2 1259 –0.6 1805 –2.1

Total (MWh) 354 229 237 369 529

000008 Elec (MWh) 140 –0.5 158 –1.1 225 0.4 330 –1.0 303 0.2

Gas (MBtu) 736 1.3 244 6.8 46 –0.3 126 –1.1 768 –5.4

Total (MBtu) 1214 0.6 784 1.2 813 0.3 1254 –1.0 1802 –2.3

Total (MWh) 356 230 238 367 528

Table 7. Annual Energy Use Predictions Using TMY2 with Different Weighting Factors Compared Against Mean Annual Energy Use Obtained with Multi-Year Simulations (continued)

Numberof yr Energy

Albany Atlanta Honolulu Las Vegas Madison

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

Energy Use

Diff%

Figure 5 Impact of TMY2 weighting factors on totalbuilding use predictions for 5 U.S. sites.

OR-10-045 (RP-1477) 9


Tabl

e 8.

An

nual

Ene

rgy

Use

Pre

dic

tions

Usi

ng T

MY

2 G

ener

ated

with

Diff

eren

t Nu

mb

er o

f Yea

rs C

om

pare

d A

gai

nst

Mea

n A

nnu

al

Ene

rgy

Use

Obt

aine

d w

ith M

ulti-

Year

Sim

ulat

ion

Num

ber

of y

rE

nerg

y

Alb

any

Atl

anta

Hon

olul

uL

as V

egas

Mad

ison

Mia

mi

Om

aha

Pho

enix

Seat

tle

Tuc

son

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

30 y

r Avg

. of

Mul

ti Y

ear

Sim

ulat

ion

Ele

c (M

Wh)

297

—16

0—

224

—33

4—

302

—37

9—

311

—35

6—

270

—33

5—

Gas

(M

Btu

)72

6—

229

—46

—12

8—

812

—48

—64

4—

80—

401

—94

—

Tota

l (M

Btu

)17

40—

775

—81

1—

1266

—18

44—

1343

—17

04—

1294

—13

23—

1235

—

28 y

r(6

3-90

)E

lec

(MW

h)29

6–0

.431

596

.938

069

.833

2–0

.530

1–0

.337

7–0

.630

9–0

.635

90.

826

9–0

.333

3–0

.3

Gas

(M

Btu

)74

82.

922

0–3

.747

1.9

130

1.7

793

–2.3

48–0

.563

5–1

.477

–3.0

386

–3.9

90–4

.2

Tota

l (M

Btu

)17

571.

012

9567

.213

4566

.012

62–0

.318

21–1

.213

35–0

.616

90–0

.913

010.

613

05–1

.412

28–0

.6

26 y

r(6

5-90

)E

lec

(MW

h)29

70.

031

596

.837

969

.433

2–0

.330

20.

037

8–0

.431

0–0

.335

70.

426

9–0

.433

3–0

.3

Gas

(M

Btu

)72

3–0

.521

1–7

.747

1.9

128

0.4

818

0.7

48–1

.563

2–1

.984

6.2

382

–4.8

90–4

.2

Tota

l (M

Btu

)17

36–0

.212

8565

.913

4265

.612

63–0

.318

490.

313

37–0

.416

89–0

.913

040.

813

01–1

.712

28–0

.6

24 y

r(6

7-90

)E

lec

(MW

h)29

70.

131

496

.438

170

.333

50.

330

30.

237

9–0

.230

8–0

.836

01.

126

9–0

.433

4–0

.3

Gas

(M

Btu

)72

5–0

.221

8–4

.845

–1.4

125

–2.2

811

–0.2

48–1

.564

3–0

.277

–3.0

388

–3.3

90–4

.2

Tota

l (M

Btu

)17

390.

012

9066

.513

4766

.212

670.

118

440.

013

40–0

.216

95–0

.613

050.

913

07–1

.312

28–0

.6

22 y

r(6

9-90

)E

lec

(MW

h)29

70.

031

697

.438

069

.533

40.

030

30.

237

8–0

.430

9–0

.736

01.

126

9–0

.333

4–0

.3

Gas

(M

Btu

)72

70.

020

6–9

.745

–1.4

128

0.4

831

2.4

48–1

.563

2–1

.977

–3.0

386

–3.8

92–2

.1

Tota

l (M

Btu

)17

400.

012

8465

.713

4165

.512

670.

018

651.

113

37–0

.416

85–1

.113

050.

913

05–1

.412

30–0

.4

20 y

r(7

1-90

)E

lec

(MW

h)29

70.

131

596

.837

969

.133

3–0

.130

30.

338

10.

431

0–0

.436

01.

227

0–0

.233

40.

0

Gas

(M

Btu

)72

0–0

.921

0–8

.047

1.9

130

1.5

840

3.5

46–4

.064

3–0

.177

–3.0

390

–2.9

89–4

.7

Tota

l (M

Btu

)17

35–0

.312

8565

.913

3965

.212

670.

118

751.

713

460.

316

99–0

.313

061.

013

10–1

.112

31–0

.4

18 y

r(7

3-90

)E

lec

(MW

h)29

70.

031

797

.938

170

.133

50.

630

30.

338

00.

231

0–0

.336

11.

526

9–0

.533

40.

0

Gas

(M

Btu

)72

0–0

.919

1–1

6.5

471.

912

1–5

.683

32.

646

–4.0

647

0.5

77–3

.338

6–3

.889

–4.7

Tota

l (M

Btu

)17

34–0

.312

7164

.113

4766

.212

65–0

.118

681.

313

440.

117

050.

013

091.

213

03–1

.512

31–0

.4

16 y

r(7

5-90

)E

lec

(MW

h)29

7–0

.131

899

.038

170

.233

60.

830

40.

537

7–0

.631

0–0

.236

01.

126

9–0

.433

60.

5

Gas

(M

Btu

)70

3–3

.221

6–5

.447

1.9

121

–5.6

868

6.9

490.

665

61.

978

–2.0

382

–4.9

89–4

.7

Tota

l (M

Btu

)17

15–1

.413

0368

.213

4866

.412

680.

219

053.

313

35–0

.617

140.

613

060.

913

00–1

.812

370.

1

14 y

r(7

7-90

)E

lec

(MW

h)29

70.

131

999

.438

571

.833

81.

430

40.

637

7–0

.731

0–0

.336

11.

527

0–0

.133

80.

9

Gas

(M

Btu

)71

0–2

.321

5–6

.145

–1.8

121

–5.6

875

7.7

48–0

.367

34.

675

–6.2

381

–5.0

951.

8

Tota

l (M

Btu

)17

24–0

.913

0368

.213

5967

.612

750.

719

123.

713

34–0

.617

311.

613

071.

013

03–1

.612

471.

0

10 OR-10-045 (RP-1477)


12 y

r(7

9-90

)E

lec

(MW

h)29

80.

331

899

.138

471

.633

60.

730

30.

338

10.

630

8–0

.936

32.

226

9–0

.333

91.

2

Gas

(M

Btu

)72

2–0

.520

2–1

1.5

46–0

.312

0–5

.882

92.

147

–3.0

677

5.2

73–8

.639

6–1

.294

0.1

Tota

l (M

Btu

)17

39–0

.112

8966

.413

5867

.512

670.

118

641.

113

490.

417

281.

413

131.

513

15–0

.612

501.

2

10 y

r(8

1-90

)E

lec

(MW

h)29

80.

231

898

.538

471

.233

3–0

.330

40.

638

10.

630

7–1

.136

62.

926

9–0

.333

70.

9

Gas

(M

Btu

)71

2–2

.020

9–8

.445

–1.8

127

–0.8

793

–2.4

47–3

.065

21.

273

–8.6

390

–2.8

952.

0

Tota

l (M

Btu

)17

28–0

.712

9367

.013

5467

.112

62–0

.418

30–0

.713

490.

417

01–0

.213

222.

213

10–1

.012

470.

9

08 y

r(8

3-90

)E

lec

(MW

h)29

90.

632

110

0.4

384

71.3

334

0.0

304

0.7

378

–0.2

311

0.1

363

2.2

272

0.5

339

1.3

Gas

(M

Btu

)70

2–3

.418

8–1

7.6

460.

612

5–2

.177

4–4

.847

–3.0

650

1.0

73–8

.640

40.

695

1.9

Tota

l (M

Btu

)17

22–1

.012

8265

.513

5667

.312

63–0

.218

12–1

.713

39–0

.317

120.

413

131.

513

300.

512

511.

3

06 y

r(8

5-90

)E

lec

(MW

h)29

70.

132

099

.938

572

.033

91.

530

40.

438

10.

531

10.

036

32.

127

31.

134

22.

3

Gas

(M

Btu

)69

1–4

.820

1–1

2.3

460.

612

90.

775

5–7

.147

–3.6

640

–0.5

69–1

3.7

392

–2.2

962.

2

Tota

l (M

Btu

)17

06–1

.912

9266

.813

6167

.912

841.

417

91–2

.913

480.

417

01–0

.213

091.

213

250.

112

642.

3

05 y

r(8

6-90

)E

lec

(MW

h)29

80.

332

110

0.6

387

72.8

335

0.5

302

0.1

384

1.2

311

0.0

365

2.6

273

0.9

342

2.3

Gas

(M

Btu

)68

2–6

.119

7–1

4.0

46–0

.512

3–3

.570

4–1

3.4

47–2

.557

3–1

1.0

73–8

.736

4–9

.493

–0.4

Tota

l (M

Btu

)16

98–2

.412

9266

.813

6768

.612

680.

117

36–5

.913

581.

116

34–4

.113

181.

912

94–2

.312

622.

1

04 y

r(8

7-90

)E

lec

(MW

h)29

90.

731

999

.638

371

.234

12.

130

40.

538

31.

031

40.

936

73.

127

20.

834

22.

1

Gas

(M

Btu

)65

3–1

0.0

194

–15.

245

–1.8

127

–0.3

715

–11.

947

–3.6

593

–7.9

73–8

.636

3–9

.493

–0.2

Tota

l (M

Btu

)16

74–3

.812

8465

.713

5467

.012

901.

917

52–5

.013

550.

916

63–2

.413

242.

412

93–2

.312

591.

9

03 y

r(8

8-90

)E

lec

(MW

h)29

80.

432

210

1.5

384

71.4

339

1.7

303

0.2

387

2.0

309

–0.4

366

2.9

271

0.2

344

2.7

Gas

(M

Btu

)68

0–6

.418

8–1

7.8

45–1

.813

98.

774

6–8

.246

–4.4

579

–10.

072

–9.7

406

1.1

86–7

.7

Tota

l (M

Btu

)16

97–2

.412

8866

.313

5667

.312

972.

417

80–3

.513

671.

816

35–4

.113

222.

113

300.

512

591.

9

02 y

r(8

9-90

)E

lec

(MW

h)29

7–0

.132

610

3.7

378

69.0

339

1.7

303

0.3

391

3.2

311

0.0

367

3.2

274

1.5

344

2.7

Gas

(M

Btu

)66

1–9

.018

2–2

0.4

46–0

.314

09.

680

4–1

.046

–4.2

646

0.4

74–6

.842

56.

089

–5.3

Tota

l (M

Btu

)16

73–3

.812

9467

.113

3865

.012

982.

518

38–0

.313

822.

917

070.

113

282.

613

612.

912

622.

1

Tabl

e 8.

An

nual

Ene

rgy

Use

Pre

dic

tions

Usi

ng T

MY

2 G

ener

ated

with

Diff

eren

t Nu

mb

er o

f Yea

rs C

om

pare

d A

gai

nst

Mea

n A

nnu

al

Ene

rgy

Use

Obt

aine

d w

ith M

ulti-

Year

Sim

ula

tion

(con

tinue

d)

Num

ber

of y

rE

nerg

y

Alb

any

Atl

anta

Hon

olul

uL

as V

egas

Mad

ison

Mia

mi

Om

aha

Pho

enix

Seat

tle

Tuc

son

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

Ene

rgy

Use

Dif

f.%

OR-10-045 (RP-1477) 11


data, at 21 stations throughout the US. ASHRAE Trans-actions 87(1):896-905.

Donghyun, S., M. Krarti, and J. Huang. 2008. Evaluation oftypical weather year selection approaches. Submitted toASHRAE Transactions, Atlanta.

Festa, R. and C.F. Ratto. 1993. Proposal of a numerical pro-cedure to select reference years. Solar Energy 50:9-17.

Hall, I.J., R.R. Prairie, H.E. Anderson, and E.C. Boes. 1978.Generation of a typical meteorological year. In: Pro-ceedings of the 1978 annual meeting of the AmericanSection of ISES, pp. 669-671.

Hui, C.M. 1996. Energy performance of air-conditionedbuildings in Hong Kong, PhD Thesis. City University ofHong Kong.

Keeble, E. 1990. Availability of UK climatic data for use insimulation, BEPAC Technical Note 90/1, Building Re-search Establishment, U.K., October.

LBL. 1981. DOE reference manual version 2.1A. Preparedfor the U.S. Department of Energy by Lawrence Berke-ley Laboratory, Report LBL-8706 Rev. 2.

Lund, H. and S. Eidorff. 1980. Selection methods for pro-duction of test reference years. Appendix D, contract284-77 ES DK. Final report EUR 7306 EN. Copenha-gen: Technical University of Denmark, Thermal Insula-tion Laboratory.

Marion, W. and K. Urban. 1995. Users manual for TMY2stypical meteorological years. National RenewableEnergy Laboratory, Golden, CO.

Massie, D.D. and J.F. Kreider. 2001. Comparison of and dis-crepancies between TMY and TMY2s predictions forsimple photovoltaic and wind energy simulation, ASMETrans, vol. 123.

Thevenard, D.J. and A.P. Brunger. 2002. The development oftypical weather years for international locations, Part II,Production, ASHRAE Transaction 108(2):480-486.

Wilcox, S. and W. Marion. 2008. Users manual for TMY3data sets, NREL Technical Report NREL/TP-581-43156,April, Golden, CO.

Figure 6 Impact of number of years used to generate TMY2data sets on R2 of the correlation total buildinguse predictions from TMY2 and mean of 30 yearsresults for 10 U.S. sites.

Figure 6 Historical annual heating degree days (HDD)variation from 1961 to 1990.

12 OR-10-045 (RP-1477)

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