基于知识嵌入和DNN的工商业用户异常用电检测
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TM933

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国家自然科学基金资助项目(61501195)


Non-technical loss detection based on energy measurement knowledge and deep neural network among industrial and commercial customers
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    摘要:

    目前的异常用电检测研究主要以居民用户为对象,并不适用于工商业用户。针对此问题,文中提出一种融合了电能计量原理的基于深度学习的异常用电检测方法。首先,分析了各类异常用电的数据现象,结果说明单纯采用智能电表数据不足以准确检测异常用电。文中遵循电能计量原理,将描述电气参量内部逻辑关系的指标作为知识嵌入智能电表数据,构建深度学习样本模型。然后提出一个改进的深度混合残差神经网络,从海量智能电表数据中学习用于识别异常用电的高级特征。实验结果表明,相比多个基准算法,文中方法在所有评估指标上均取得了明显的提升。

    Abstract:

    The current research on non-technical loss(NTL) detection is mainly aim on residential customers, however, the related methods are not suit industrial and commercial customers. According to this problem, a deep learning based NTL detection method by embedding the principle of electricity measurement is proposed. Firstly, various NTL is analyzed and the phenomena show that only smart meter data is not enough for detecting NTL. Hence, smart meter data and some principles of electricity measurement are organized which describe the inherent relationship among electricity magnitudes as samples for deep learning. Secondly, an improved hybrid residual neural network is proposed to extract advanced features of NTL from massive smart meter data for detecting NTL. The experiment results show that the approach in this paper has achieved significant improvement on all metrics by comparing with the baselines.

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李江腾,王非.基于知识嵌入和DNN的工商业用户异常用电检测[J].电力工程技术,2020,39(3):158-165

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历史
  • 收稿日期:2019-12-03
  • 最后修改日期:2020-01-12
  • 录用日期:2019-12-11
  • 在线发布日期: 2020-06-08
  • 出版日期: 2020-05-28