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Scalable prediction-based online anomaly detection for smart meter data

机译：基于可扩展的基于预测的智能电表数据在线异常检测

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摘要

Today smart meters are widely used in the energy sector to record energy consumption in real time. Large amounts of smart meter data have been accumulated and used for diverse analysis purposes. Anomaly detection raises the big data problem, namely the detection of abnormal events or unusual consumption behaviors. However, there is a lack of appropriate online systems that can handle anomaly detection for large-scale smart meter data effectively and efficiently. This paper proposes a lambda system for detecting anomalous consumption patterns, aiming at assisting decision makings for smart energy management. The proposed system uses a prediction-based detection method, combined with a novel lambda architecture for iterative model updates and real-time anomaly detection. This paper evaluates the system using a real-world data set and a large synthetic data set, and compares with three baselines. The results show that the proposed system has good scalability, and has a competitive advantage over others in anomaly detection. (C) 2018 Elsevier Ltd. All rights reserved.
机译：如今，智能电表已广泛应用于能源领域，以实时记录能耗。大量的智能电表数据已累积并用于各种分析目的。异常检测引发了大数据问题，即异常事件或异常消耗行为的检测。但是，缺乏合适的在线系统来有效地处理大规模智能电表数据的异常检测。本文提出了一种用于检测异常消耗模式的lambda系统，旨在辅助智能能源管理的决策。拟议的系统使用基于预测的检测方法，并结合新颖的lambda架构进行迭代模型更新和实时异常检测。本文使用真实数据集和大型综合数据集评估系统，并与三个基准进行比较。结果表明，所提出的系统具有良好的可扩展性，并且在异常检测方面具有优于其他系统的竞争优势。 （C）2018 Elsevier Ltd.保留所有权利。

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