基于数据挖掘的公共建筑能耗监管平台异常数据修复研究  

Research on abnormal data repair of public building energy consumption monitoring platform based on data mining

作　　者：张城瑀 赵天怡[1] 特日格乐[1] 马良栋[1] 娄兰兰 朱凯 Zhang Chengyu;Zhao Tianyi;Terigele;Ma Liangdong;Lou Lanlan;Zhu Kai(Dalian University of Technology,Dalian;Artificial Intelligence Institute,Dalian University of Technology,Dalian;Dalian Qunzhi Swarm Intelligent Technology,Dalian)

机构地区：[1]大连理工大学,大连116024 [2]大连理工大学人工智能大连研究院,大连 [3]大连群智科技有限公司,大连

出　　处：《暖通空调》2022年第8期73-82,共10页Heating Ventilating & Air Conditioning

基　　金：国家重点研发计划项目“基于全过程的大数据绿色建筑管理技术研究与示范”课题三“建筑运行大数据安全与数据质量保障关键技术”(编号:2017YFC0704203);国家自然科学基金面上项目“建筑热环境节能调控中的质-量参数设定值在线解耦机制”(编号:52078096)。

摘　　要：公共建筑用能设备多、建筑面积大、使用人数多,具有较大的节能潜力。但由于建设费用有限导致的数据分项计量异常及传感器或采集器故障导致的数据缺失和突变等问题,其配套的建筑能耗监管平台获取的电耗数据经常出现数据异常问题。本文研究以聚类算法为基础,提出了一种由KNN-Matrix算法与KNN-Slope算法共同构成的异常数据修复体系。KNN-Slope算法根据异常数据当日用电趋势线,寻找用电趋势一致的最近历史电耗数据,以加权计算后的电耗值作为插补值进行异常数据修复。KNN-Matrix算法引入以矩阵形式表征的用电强度量化等级,寻找量化等级与用电趋势均一致的最近历史数据或平均历史数据作为插补值进行异常数据修复。结果显示,在面向不同数据异常比例和不同公共建筑类型时,上述修复体系可使99%的异常数据在修复后与真实数据的相对误差在30%以下,且相对误差最大值、平均值均大幅下降。Public buildings have many energy-using equipment, large construction areas, and a large number of users, which have great energy-saving potential. However, due to the problems of the abnormal data itemization caused by limited construction costs and the data loss and mutation caused by sensor or collector failures, the power consumption data obtained by its supporting building energy consumption monitoring platform often have anomalies. Based on the clustering algorithm, this paper proposes an abnormal data repair system composed of KNN-Matrix algorithm and KNN-Slope algorithm. Based on the current power consumption trend line of the abnormal data, the KNN-Slope algorithm looks for the recent historical power consumption data that are consistent with power consumption trend, and uses the weighted calculated power consumption value as the interpolated value to repair the abnormal data. The KNN-Matrix algorithm introduces a quantitative grade of electricity intensity characterized in matrix form, and looks for the recent historical data or average historical data that are consistent with the power consumption trend as an interpolated value for abnormal data repair. The results show that when facing different data anomalies and different public building types, the above repair system can make 99% of the abnormal data have a relative error of less than 30% with the real data after repair, and the maximum and average values of the relative errors are greatly reduced.

关 键 词：公共建筑 能耗监管 数据挖掘 临近算法 量化等级 数据修复 

分 类 号：TU111.195[建筑科学—建筑理论]