建筑空调能耗关键变量通用提取方法及工具的开发

建筑空调能耗关键变量是所有可能对建筑空调能耗产生影响的变量中起决定性作用的少数变量。关键变量的确定对于常用的两类能耗预测模型(白箱模型和黑箱模型)都非常重要，基于关键变量而非全部变量建立模型可大大简化建模过程但不过度损失模型精度。关键变量的确定是比较复杂的过程，且容易受到初始边界条件的影响。提出1种关键变量通用提取方法，该方法分别对空调负荷相关和系统相关的特征进行分析，采用Morris法和回归法两种敏感性分析方法从初始变量集中提取出关键变量，并基于Python和Eppy开发了关键变量自动提取工具，该工具适用于不同气候区的各类建筑。案例分析结果表明，使用该方法提取的关键变量集可以用少数变量较准确地描述空调能耗变化。

Method and tool development of key variables identification for building HVAC energy consumption

The key variables of building HVAC energy consumption are the few decisive variables among all the variables that may influence the energy consumption of building HVAC energy consumption. The HVAC key variables are important for the two commonly used energy consumption prediction models (namely white box model and black box model). The modeling process of key-variables based energy prediction is greatly simplified without excessively sacrificing accuracy compared with traditional way. The determination of key variables is a complicated process and is easily affected by the initial boundary conditions.A general identification method of key variable is proposed in this paper. The key variable are identified separately from HVAC load related variables and system related variables. This method applies both Morris method and regression method for key variable identification.Also an automatic key variable identification tool is developed based on Python and Eppy. This tool is applicable to all kinds of buildings in different climate zones. The case study shows that the key variables identified by the method proposed in this paper is able to accurately describe the variation and feature of HVAC energy consumption with a few variables.