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Detecting anomalous energy consumption using contextual analysis of smart meter data

机译:使用智能仪表数据的上下文分析检测异常能量消耗

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Energy consumption is dependent on temperature, humidity, occupancy, occupant type, building area etc. All these factors collectively define the context of an energy meter. Once the context is known, the meters within the same context can be grouped and their behaviour can be analyzed together. This paper presents four heuristics, including one novel heuristic, to identify abnormal energy consumption. Using these heuristics, data collected from fifty smart meters deployed inside hostels of IIIT-Delhi was investigated for abnormal energy consumption detection. The anomalies and possible causes were discussed with IIIT-Delhi campus administrator. Energy consumption per occupant for one of the meters was found four times when compared to rest of the meters. The results demonstrated that the proposed heuristics successfully found abnormal energy consumption behaviour.
机译:能量消耗取决于温度,湿度,占用,乘员类型,建筑面积等。所有这些因素都集中定义了能量计的背景。 一旦上下文知道,可以分组相同上下文的仪表,并且它们的行为可以一起分析。 本文展示了四种启发式,包括一个新的启发式,以识别异常能耗。 使用这些启发式学,研究了从IIIT-DELHI的五十智能仪表中收集的数据,用于异常能耗检测。 与IIIT-DELHI校园管理员讨论了异常和可能的原因。 与仪表彼此相比,每乘客的每乘员的能耗有四次。 结果表明,拟议的启发式成功地发现了异常的能量消耗行为。

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