With the development of automatic electrical devices in smart grids, the data generated by time and transmitted are vast and thus impossible to control consumption by humans. The problem of abnormal detection in power consumption is crucial in monitoring and controlling smart grids. This article proposes the detection of electrical meter anomalies by detecting abnormal patterns and learning unlabeled data. Furthermore, a framework for big data and machine learning-based anomaly detection framework are introduced. The experimental results show that the time series anomaly detection for electric meters has better results in accuracy and time than the expert alternatives.

中文翻译：

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使用正向无标记学习和多重符号聚合逼近的自动仪表智能系统中的异常检测。

随着智能电网中自动电气设备的发展，时间产生和传输的数据海量，因此人类无法控制消耗。用电异常检测问题对于智能电网的监控至关重要。本文提出通过检测异常模式和学习未标记数据来检测电表异常。此外，还介绍了大数据框架和基于机器学习的异常检测框架。实验结果表明，电表时间序列异常检测在准确度和时间上均优于专家替代方案。