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Anomaly detection method of daily energy consumption patterns for central air conditioning systems
Journal of Building Engineering ( IF 6.4 ) Pub Date : 2021-01-13 , DOI: 10.1016/j.jobe.2021.102179
Xuan Zhou , Tao Yang , Liequan Liang , Xuehui Zi , Junwei Yan , Dongmei Pan

Anomaly detection of operating patterns of complex systems is an important measure to achieve building energy conservation. In this paper, a low-cost anomaly detection method is proposed to identify the anomaly energy consumption patterns of central air conditioning systems (CACS). The complex process of anomaly detection is simplified as a binary classification problem without threshold. And information entropy is used as the characteristic parameter of daily energy consumption patterns (DECP) while traditional characteristic parameters are prone to cause high miss rates or false-positive rates due to the large data fluctuation, numerous influence factors and complex operational parameters of the complex systems. Moreover, three main influence factors are analyzed to divide the complex operating conditions of CACS and the normal DECPs data-set is updated online to improve the accuracy of the abnormal patterns detection. This non-threshold detection method is also verified by site survey which indicates that the detection accuracy is higher than the traditional detection method based on conventional characteristic parameters and regular K-Means clustering method.



中文翻译:

中央空调系统日常能耗模式的异常检测方法

复杂系统运行模式的异常检测是实现建筑节能的重要措施。本文提出了一种低成本的异常检测方法来识别中央空调系统(CACS)的异常能耗模式。异常检测的复杂过程被简化为没有阈值的二进制分类问题。信息熵被用作日常能源消耗模式(DECP)的特征参数,而传统特征参数由于数据波动大,影响因素众多以及复杂的复杂操作参数而容易导致高遗漏率或误报率。系统。此外,分析了三个主要影响因素以划分CACS的复杂操作条件,并在线更新正常DECPs数据集以提高异常模式检测的准确性。该非阈值检测方法还通过现场调查得到了验证,表明该方法的检测精度高于基于常规特征参数和常规K-Means聚类方法的传统检测方法。

更新日期:2021-01-22
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