ASHRAE OR-10-045-2010 Impact of Typical Weather Year Selection Approaches on Energy Analysis of Buildings《典型气象年选择方法对建筑物能量分析的影响RP-1477》.pdf

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1、416 2010 ASHRAEThis paper is based on findings resulting from ASHRAE Research Project RP-1477.ABSTRACTThe 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 an

2、alysis 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 pa

3、per 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 selecti

4、on procedure used to generate typical weather yearfor the 10 US climates considered in this study.INTRODUCTIONDetailed 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 ye

5、ar 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).Sever

6、al 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 1

7、995), 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

8、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 co

9、nsideredseveral 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 e

10、valu-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

11、 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 o

12、f the typical weather selection criteria onImpact of Typical Weather Year Selection Approaches on Energy Analysis of BuildingsDonghyun Seo Yu Joe Huang Moncef Krarti, PhD, PEStudent Member ASHRAE Member ASHRAE Member ASHRAEDonghyun Seo is a graduate student and Moncef Krarti, PhD, PE is a Professor

13、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.OR-10-045 (R

14、P-1477) 2010, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in ASHRAE Transactions 2010, Vol. 116, Part 1. For personal use only. Additional reproduction, distribution, or transmission in either print or digital form is not permitted with

15、out ASHRAEs prior written permission. ASHRAE Transactions 417building 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 th

16、roughoutthe 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

17、discussed.BUILDING MODEL DESCRIPTIONFor 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 pow

18、er 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 printer

19、s, photocopiers, and facsimilemachines. The building envelope is assumed to include 40%fenestration-to-wall ratio with glazing varying by climate tomeet 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 buildin

20、g 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.

21、 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 ModelArchitectural MechanicalFloor Area 16,000 sf (1487 m2) Design Temp. 75F/72F Cooling/Heating (24C/22C)G

22、ross Area 48000 sf (4461 m2) Thermo. Set 76F/82F (24.5C/28C) Cooling60F/64F (15.5C/18C) HeatingPeri. Depth 20 ft (6.1 m) 57% of floor area HVAC VAV+ReheatWall 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-22O

23、A 20 cfm/person (9.5 L/s per person)Economizer controlWWR 40% (floor to ceiling) Chiller 0.55 kW/tonGlazing Variable with climate Boiler 90%Daylight Control Perimeter zonesShading 3 ft (0.9 m) overhang on S, E, and W 50fc design levelLPD/ EPD 0.8 /1.0 W/sf(8.7/10.8 W/m2)Dimming controlTable 2. Selec

24、ted 10 U.S. SitesUSAF Stations STATE LAT LON ELEV (m) Climate Zone (ASHRAE Std 90.1)725180 ALBANY COUNTY ARPT NY 42.45N 073.48W 89 5A722190 ATLANTA INTL ARPT GA 33.39N 084.25W 315 3A911820 HONOLULU INTL ARPT HI 21.21N 157.56W 5 1A723860 LAS VEGAS/MCCARRAN NV 36.05N 115.10W 664 5B726410 MADISON/DANE

25、RGNL WI 43.08N 089.20W 264 6A722020 MIAMI INTL AIRPORT FL 25.49N 080.17W 4 2A725500 OMAHA/EPPLEY FIELD NE 41.18N 095.54W 299 5A722780 PHOENIX/SKY HARBOR AZ 33.26N 112.01W 337 3B727930 SEATTLE-TACOMA INTL WA 47.27N 122.18W 137 5B722740 TUCSON INTL AIR PORT AZ 32.07N 110.56W 779 3B 2010, American Soci

26、ety of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in ASHRAE Transactions 2010, Vol. 116, Part 1. For personal use only. Additional reproduction, distribution, or transmission in either print or digital form is not permitted without ASHRAEs prior written p

27、ermission. 418 ASHRAE TransactionsIMPACT OF TYPICAL WEATHER YEARUsing 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

28、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

29、, 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 wei

30、ghting 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 sim

31、ulation 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 Ph

32、oenix 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 differe

33、ncesare 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

34、 differences for all 10 cities range from 1.6%Table 3. Annual Total Building Energy Use Differences Between Typical Weather Data and 30-Year AverageIWEC EqWt TMY TMY2n TMY2rAlbany 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

35、.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

36、% 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% 3

37、33071 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%

38、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) 31

39、1337 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.

40、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.

41、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% 2010, American Society of Heating, Refrigerati

42、ng and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in ASHRAE Transactions 2010, Vol. 116, Part 1. For personal use only. Additional reproduction, distribution, or transmission in either print or digital form is not permitted without ASHRAEs prior written permission. ASHRAE Transacti

43、ons 419(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 peakd

44、emand 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 and

45、TMY2r 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 b

46、oth 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

47、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.IMPA

48、CT OF WEIGHTING FACTORSUsing the simulation building model, annual total energyconsumption are estimated for various TMY2 weather datasets obtained using different configurations weighting factorsand are compared to those mean values obtained from 30 yearsweather data. Table 6 lists the weighting fa

49、ctors 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 glob