Building Simulation: An International Journal

Article Title

An evaluation of the effects of various parameter weights on typical meteorological years used for building energy simulation


typical meteorological year (TMY), typical meteorological month (TMM), energy use density, parameter weight, building simulation, weather data


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.


Tsinghua University Press