摘要： 针对静态建筑能耗异常检测方法在动态高校建筑能耗环境中容易出现误判的问题,提出一种改进的高校建筑能耗异常检测方法。采用SA-DBSCAN算法根据能耗数据的统计特性自适应识别建筑能耗模式,利用C4.5算法构建能耗模式判定树,依据判定树得到实时能耗数据的相应类别后使用LOF算法进行离群分析检测异常。将判定正常的能耗增量地更新到建筑能耗模式中,并根据更新结果动态调整异常检测模型。实验结果表明该方法能有效检测异常能耗数据并逐步拟合高校建筑能耗环境的变化来减少误判。

关键词: 动态实时, 高校建筑能耗, 异常检测, 自适应识别, 增量更新

Abstract: The static energy consumption outlier detection method prones to misjudgment of justice in the dynamic campus building energy consumption environment.Therefore,an improved outlier detection method for energy consumption of campus building is proposed.The method uses SA-DBSCAN algorithm based on the statistical characteristics of energy consumption data to identify the building energy consumption mode adpatively.Then it uses C4.5 algorithm to build energy consumption pattern decision tree.After the corresponding category of the real-time energy consumption data is obained,according to the decision tree,it uses LOF algorithm to realize outlier analysis and anomaly detection.The normalized energy consumption is updated incrementally to the building energy consumption mode,and the anomaly detection model is dynamically adjusted according to the update results.Experimental results show that the method can detect the abnormal energy consumption data effectively and fit the change of the campus building energy environment step by step which reduces misjudgments.

Key words: dynamic and real-time, campus building energy consumption, outlier detection, adaptive identification, incremental update

中图分类号: 

• TP391.3