公共建筑电耗监测数据缺失及异常的预处理方法探究
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摘要
以北京市212栋大型公共建筑样本为例,对电力部门提供的建筑电耗数据中常见的缺失数据和异常数据分别进行诊断和处理。预处理方法包括电耗数据的完整性识别及补全、年单位面积电耗相对极差检测、异常样本箱线图检测、采用多参数预测回归模型补全整月电耗数据等过程。完整的处理过程能为建筑电耗数据的预处理工作提供参考。
Taking the monitored power consumption data consumed in 212 large-scale public buildings in Beijing as the basis, the missing and abnormal data sets were diagnosed and processed respectively in this paper. The preprocessing method consists of the completeness identification and completion, the detection of the relative range of power consumption per unit area, the detection of the abnormal sample by box plot, and the multi-parameter prediction regression model to complete the monthly power consumption data. This proposed complete framework provides a reference when dealing with the monitored building power consumption data.
Taking the monitored power consumption data consumed in 212 large-scale public buildings in Beijing as the basis, the missing and abnormal data sets were diagnosed and processed respectively in this paper. The preprocessing method consists of the completeness identification and completion, the detection of the relative range of power consumption per unit area, the detection of the abnormal sample by box plot, and the multi-parameter prediction regression model to complete the monthly power consumption data. This proposed complete framework provides a reference when dealing with the monitored building power consumption data.
引文
[1]北京市住房城乡建设委.我市公共建筑节能进入依法行政轨道1.3万栋公建已实施电耗限额管理[EB/OL].http://www.bjjs.gov.cn/bjjs/gcjs/jzjnyjcjg/nhxe/317725/index.shtml,2015–10–30.
[2]城市发展研究编辑部.城市发展研究——第7届国际绿色建筑与建筑节能大会论文集[C].2011.
[3]王怀亮.箱须图在识别统计数据异常值中的作用及R语言实现[J].商业经济,2011(3):64–65.
[4]KISSOCK J K,HABERL J S,CLARIDGE D E.Inverse Modeling Toolkit:Numerical Algorithms[J].ASHRAE Transactions,2003,109(18):425–434.
[2]城市发展研究编辑部.城市发展研究——第7届国际绿色建筑与建筑节能大会论文集[C].2011.
[3]王怀亮.箱须图在识别统计数据异常值中的作用及R语言实现[J].商业经济,2011(3):64–65.
[4]KISSOCK J K,HABERL J S,CLARIDGE D E.Inverse Modeling Toolkit:Numerical Algorithms[J].ASHRAE Transactions,2003,109(18):425–434.