An evaluation of the effects of various parameter weights on typical meteorological years used for building energy simulation
In this paper, we evaluate the influence of different parameter weights in creating “typical year” weather data following the typical meteorological year (TMY) methodology, by studying two sets of 3600 alternate weather files created using different parameter weights for Beijing (China) and New York City (USA). A “typical year” weather file consists of twelve distinctive months, each considered typical for that month of the year. Such a typical month, named “typical meteorological month (TMM),” is commonly identified by using a certain combination of parameter weights, such as 4:4:4:12, for dry bulb temperature, dew point temperature, wind speed, and solar radiation as in the TMY weather files developed by US National Climate Data Center (NCDC), or 4:4:2:10 in the newer TMY2 and TMY3 weather files developed by National Renewable Energy Laboratory (NREL). In this study, we investigate the influence of varying the parameter weights on the TMMs and the resultant new TMY weather files (nTMY). We found that the distribution of new 3600 TMMs tend to cluster within one or a few years for each month, and that the probabilities are very high for significant overlap between the new TMMs and the original TMMs chosen using the TMY/TMY2 weighting. Compared to the TMM data in TMY, the deviations of air temperatures and solar radiation values of the new TMMs and nTMYs derived from the 20-year weather data are less than 10% for both Beijing and New York. This confirms that the creation of “typical year” weather data is not very sensitive to the weighting of the different weather parameters, and that most nTMYs created and evaluated in this study are empirically close to the TMY data intended for use of simulating building energy consumption.
Keywordstypical meteorological year (TMY) typical meteorological month (TMM) energy use density parameter weight building simulation weather data
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- Crawley DB, Lawrie LK, Pedersen CO, Winkelmann FC, Witte MJ, Strand RK, Liesen R, Buhl WF, Huang YJ, Henninger RH, Glazer J, Fisher DE, Shirey III DB, Griffith BT, Ellis PG, Gu L (2004). EnergyPlus: New, capable, and linked. Journal of Architectural and Planning Research, 21(4): 292–302.Google Scholar
- Hall IJ (1978). Generation of typical year for 26 SOLMET stations. Sandia Laboratories Report SAND78-1601. Sandia Laboratories, Albuquerque, NM, USA. pp. 189–200.Google Scholar
- Marion W, Urban K (1995). User’s Manual for TMY2s. National Renewable Energy Laboratory, Golden CO, USA.Google Scholar
- National Climatic Data Center (NCDC) (1981). Typical Meteorological Year User’s Guide. NCDC, National Oceanic and Atmospheric Administration, US Dept. of Commerce, Asheville, NC, USA.Google Scholar
- National Climatic Data Center (NCDC) (2003). Data Documentation for Data Set 3505 (DSI-3505), Integrated Surface Hourly Data. Asheville, NC, USA.Google Scholar
- Song F, Zhu Q, Wu R, Jiang Y, Xiong A, Wang B, Zhu Y, Li Q (2007). Meteorological data set for building thermal environment analysis of China. In: Proceedings of the 10th International Building Performance Simulation Association Conference and Exhibition, Beijing, China, pp. 9–16.Google Scholar
- Thevenard DJ, Brunger AP (2002a). The development of typical weather years for international locations, Part I: Algorithms. ASHRAE Transactions, 108(2): 376–383.Google Scholar
- Thevenard DJ, Brunger AP (2002b). The development of typical weather years for international locations, Part II: Production. ASHRAE Transactions, 108(2): 480–486.Google Scholar
- Wilcox S, Marion W (2008). Users Manual for TMY3 Data Sets. National Renewable Energy Laboratory Technical Report NREL/TP-581-43156, Golden CO.Google Scholar
- Winkelmann FC, Birdsall BE, Buhl WF, Ellington KL, Erdem AE, Hirsch JJ, Gates S (1993). DOE-2 Supplement Version 2.1E, LBL-34947. Lawrence Berkeley National Laboratory, Berkeley CA.Google Scholar
- Zhang Q, Huang J, Lang S (2002). Development of typical year weather data for chinese locations, LBNL-51436. ASHRAE Transactions, 108(2): 1063–1075.Google Scholar
- Zhang Q, Huang J (2004). Typical Year Chinese Weather Data for Architectural Applications. Beijing: China Machine Press. (in Chinese)Google Scholar