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Assembling Typical Meteorological Year Data Sets for Building Energy Performance Using Reanalysis and Satellite-Based Data

机译:使用重新分析和基于卫星的数据组合典型的气象年数据集,以提高建筑的能源性能

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We present a method to generate Typical Meteorological Year (TMY) data sets for use in calculations of the energy performance of buildings, based on satellite derived solar radiation data and other meteorological parameters obtained from reanalysis products. The great advantage of this method is the availability of data over large geographical regions, giving global coverage for the reanalysis and continental-scale coverage for the solar radiation data, making it possible to generate TMY data for nearly any location, independent of the availability of meteorological measurement stations in the area. The TMY data generated with this method have been validated against 487 meteorological stations in Europe, by calculating heating and cooling degree days, and by running building energy performance simulations using EnergyPlus. Results show that the generated data sets using a long time series perform better than the TMY data generated from station measurements for building heating calculations and nearly as well for cooling calculations, with relative standard deviations remaining below 6% for heating calculations. TMY data constructed using the proposed method yield somewhat larger deviations compared to TMY data constructed from station data. We outline a number of possibilities for further improvement using data sets that will become available in the near future.
机译:我们提供了一种方法,可基于卫星衍生的太阳辐射数据和从重新分析产品获得的其他气象参数,生成用于计算建筑物能源性能的典型气象年(TMY)数据集。这种方法的最大优点是可以在较大的地理区域内获得数据,从而为重新分析提供了全球覆盖范围,并且为太阳辐射数据提供了大陆范围的覆盖范围,从而使得几乎可以在任何位置生成TMY数据,而与该地区的气象测量站。通过计算供热和制冷度天数,以及通过使用EnergyPlus进行的建筑能效模拟,已经使用欧洲487个气象站对使用此方法生成的TMY数据进行了验证。结果表明,使用较长时间序列生成的数据集的性能要比从站点测量生成的TMY数据更好,用于建筑物供暖计算,对于制冷计算也差不多,相对标准偏差保持在6%以下。与从测站数据构造的TMY数据相比,使用建议的方法构造的TMY数据产生的偏差更大。我们概述了使用不久将可用的数据集进行进一步改进的多种可能性。

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