基于深度卷积嵌入LSTM编码器的电力负荷数据异常检测方法
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作者单位:

1.河北工业大学 电子信息工程学院;2.天津商业大学 信息工程学院;3.朗新科技集团股份有限公司

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中图分类号:

TM769;TP311.13

基金项目:

国家重点研发计划智能机器人专项子课题(2019YFB1312102);河北省自然科学基金(F2019202364)


Anomaly detection method of power load data based on deep convolution embedded LSTM encoder
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1.School of Electronics and Information Engineering,Hebei University of Technology;2.School of Information Engineering,Tianjin University of Commerce;3.Long shine Technology Group Co,Ltd

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    摘要:

    针对传统的用电负荷数据异常检测方法精度低,提取时间特征困难,特征提取与检测过程分离的问题,提出了一种基于深度卷积嵌入LSTM编码器(Deep convolution embedded LSTM Auto-Encoder, DCE-LAE)的电力负荷数据异常检测方法。该方法将长短期记忆网络融入自编码器架构,利用编码器的非线性特征提取能力和长短期记忆网络的时序特征记忆能力提高电力负荷的异常检测精度,同时将深度卷积层嵌入至该架构中提高感受野以提取更多时间序列特征,此外,将卷积损失和重构损失相结合作为损失函数联合优化以防止卷积嵌入微调对重构空间的扭曲,进一步提高结果的可靠性。实例仿真通过与其他方法进行对比,证明了本文算法的异常检测精度与时序重构能力均优于其他算法。

    Abstract:

    Aiming at the problems of low accuracy, difficult time feature extraction and separation of feature extraction and detection process of traditional electricity data anomaly detection methods, a power load data anomaly detection method based on deep convolution embedded LSTM auto encoder (DCE-LAE) is adopted. In this method, the long-short term memory network is integrated into the self-encoder architecture, the nonlinear feature extraction ability of the encoder and the timing feature memory ability of the long-short term memory network are used to improve the timing reconstruction accuracy of power load, and the deep convolution layer is embedded into the architecture to improve the receptive field to extract more time series features. In addition, the combination of convolution loss and reconstruction loss is used as the loss function for joint optimization to prevent the distortion of convolution embedding fine-tuning to the reconstruction space and further improve the reliability of the results. By comparing with other methods, the example simulation is proved that the anomaly detection accuracy and timing reconstruction ability of this algorithm are better than other algorithms.

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唐豫川,苏彦莽,何少华,等. 基于深度卷积嵌入LSTM编码器的电力负荷数据异常检测方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-25
  • 最后修改日期:2022-03-06
  • 录用日期:2022-03-29
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