能源模拟程序的用户有各种各样的天气数据可供选择——从本地记录的天气数据到预选的“典型”年份，通常有一系列令人困惑的选项。在过去五年中，几个组织开发了新的典型天气数据集，包括WYEC2、TMY2、CWEC和CTZ2。不幸的是，这些新数据如何影响能源模拟结果，以及它们如何与记录的天气数据进行比较，都没有很好的记录。本文介绍了DOE-2.1E小时能源模拟项目的结果，该项目针对一座原型办公楼，受当地多年实测天气数据和美国八个地点的多个天气数据集的影响。我们比较了各种天气数据集对模拟年能源使用和成本以及年峰值电力需求、热负荷和冷负荷的影响。 还提供了不同位置和数据集的温度、加热和冷却度日数以及太阳辐射的统计数据。在可能的情况下，作者解释了与开发每个数据集时使用的不同设计相关的变化。还显示了实际天气数据中固有的变化及其对模拟结果的影响。最后，基于这些结果，问题得到了回答:你应该使用哪些天气数据？单位:I-PCITION:研讨会，ASHRAE交易，1998年，第104卷，第2部分，多伦多

Users of energy simulation programs have a wide variety of weather data from which to choose--from locally recorded weather data to preselected "typical" years, often a bewildering range of options. In the last five years, several organizations have developed new typical weather data sets including WYEC2, TMY2, CWEC, and CTZ2. Unfortunately, neither how these new data influence energy simulation results nor how they compare to recorded weather data is well documented.This paper presents results from the DOE-2.1E hourly energy simulation program for a prototype office building as influenced by local measured weather data for multiple years and several weather data sets for eight U.S. locations. We compare the influence of the various weather data sets on simulated annual energy use and costs and annual peak electrical demand, heating load, and cooling load.Statistics for temperature, heating and cooling degree-days, and solar radiation for the different locations and data sets are also presented. Where possible, the author explains the variation relative to the different designs used in developing each data set. The variation inherent in actual weather data and how it influences simulation results is also shown. Finally, based on these results, the question is answered: which weather data should you use?Units: I-P