Proposal of typical and design weather year for building energy simulation
Proposal of typical and design weather year for building energy simulation
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建筑能耗模拟典型气象年和设计气象年的建议
DOI：
10.1016/j.enbuild.2017.01.056
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发表时间：
2017
影响因子：
6.7
通讯作者：
Hideki Kikumoto
中科院分区：
文献类型：
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作者：
Yusuke Arima;Ryozo Ooka;Hideki Kikumoto
在建筑设计或研究过程中，使用天气数据进行建筑能源模拟（BES）。北京谱仪的气象数据有两种类型：典型气象年用于估算年制冷/制热负荷，设计气象数据用于估算最大制冷/制热负荷。在这项研究中，我们提出了一种新的类型的天气年数据（称为典型和设计天气年：TDWY），可以用作典型天气年和设计天气数据。为了创建TDWY，我们选择了一个基于Finkelstein-Schafer统计的平均年，并将分位数映射（QM）应用于具有父多年（MY）天气数据的平均年。由QM创建的TDWY的累积分布函数完全由QM中使用的所有天气分量的父MY天气数据组成。由于基于QM的TDWY的月平均和年平均值等于母MY天气数据的月平均和年平均值，因此可以预期TDWY作为典型天气年的高性能。此外，TDWY的每小时值包括每个月父MY天气数据的最小值到最大值，因此TDWY也可以用作设计天气数据。为了验证TDWY的性能，我们进行了BES。的TDWY表现出更好的两倍以上的性能估计平均冷/热负荷相比，现有的典型天气年，可以准确地估计最大冷/热负荷。
In building design or research processes, building energy simulations (BES) are conducted using weather data. There are two types of weather data for BES: typical weather year is used to estimate annual cooling/heating loads and design weather data to estimate maximum cooling/heating loads. In this study, we propose a new type of weather year data (called the Typical and Design Weather Year: TDWY) that can be used as both typical weather year and design weather data. To create the TDWY, we selected an average year based on Finkelstein-Schafer statistics and applied quantile mapping (QM) to the average year with parent multi-year (MY) weather data. The cumulative distribution functions of the TDWY created by QM consist completely of parent MY weather data for all the weather components used in QM. As the monthly and annual averages of the TDWY based on QM are equal to those of the parent MY weather data, high performance of the TDWY as typical weather year can be expected. In addition, the hourly values of the TDWY include from the minimum value to the maximum value of the parent MY weather data each month, so the TDWY can also be used as design weather data. To validate the performance of the TDWY, we conducted BES. The TDWY showed better than double the performance for estimating average cooling/heating loads compared to the existing typical weather year and could accurately estimate maximum cooling/heating loads.