Electrical behavior recognition based on bidirectional LSTM

XIE Lu-jiang; DUAN Li; TAN Gang; JIANG Rong; ZHONG Tao-tao; LIU Mei-chuan; LEI Yang; LIAO Jun

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Electrical behavior recognition based on bidirectional LSTM

更新时间：2026-09-04

- Electrical behavior recognition based on bidirectional LSTM
- Vol. 38, Issue 5, Pages: 36-41(2021)
- 作者机构：
  
  1. 国网重庆市电力公司信息通信分公司,重庆,400010
  2. 重庆智网科技有限公司信息通信分公司,重庆,400000
  3. 重庆大学大数据与软件学院,重庆,401331
- 作者简介：
- 基金信息：
- DOI：
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- Published：2021
- 稿件说明：
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谢禄江, 段立, 谭刚, 蒋荣, 钟淘淘, 刘美川, 雷洋, 廖军. 基于双向LSTM的用户用电行为识别[J]. 微电子学与计算机, 2021, 38(5): 36-41.

XIE Lu-jiang, DUAN Li, TAN Gang, et al. Electrical behavior recognition based on bidirectional LSTM[J]. 2021, 38(5): 36-41.
谢禄江, 段立, 谭刚, 蒋荣, 钟淘淘, 刘美川, 雷洋, 廖军. 基于双向LSTM的用户用电行为识别[J]. 微电子学与计算机, 2021, 38(5): 36-41. DOI：

XIE Lu-jiang, DUAN Li, TAN Gang, et al. Electrical behavior recognition based on bidirectional LSTM[J]. 2021, 38(5): 36-41. DOI：

摘要

为了解决由非技术性损失所造成的用户用电异常问题，本文提出了一种基于双向长短时记忆神经网络(Bi-LSTM)的用户异常用电行为检测方法.该方法首先采用插值法处理用电缺失数据，并通过分位数归一化平滑用电异常行为值的不对称分布性.然后，结合LSTM神经网络单元和双向网络构建Bi-LSTM模型，用于获取用户异常用电行为中的隐含关系.最后，采用交叉熵确定最优参数，从而识别出异常用电用户行为.实验表明，该方法的识别性能显著优于其他模型，并且结合国家电网的实际数据验证了该方法的准确性和稳定性.

Abstract

To solve the problem of abnormal user power consumption caused by non-technical losses

a method for detecting abnormal power consumption behavior based on a bidirectional long-term short-term memory neural network (Bi-LSTM) is proposed. In this method

firstly

the missing data is processed by interpolation method

and the asymmetric distribution of abnormal electricity consumption behavior value is smoothed by quantile normalization. Then

the Bi-LSTM model is constructed by combining the LSTM neural network unit and bidirectional network

which is used to obtain the implicit relationship of abnormal electricity consumption behavior of users. Finally

the cross entropy is used to determine the optimal parameters

thereby detecting the abnormal power consumer. The experiments are conducted based on real energy consumption data

and the results show that the detection performance of this proposed method outperforms other methods in terms of accuracy and stability.

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references

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赵文清, 沈哲吉, 李刚. 基于深度学习的用户异常用电模式检测[J]. 电力自动化设备, 2018, 38(9): 34-38. DOI: 10.16081/j.issn.1006-6047.2018.09.006. ZHAO W Q, SHEN Z J, LI G. Anomaly detection for power consumption pattern based on deep learning[J]. Electric Power Automation Equipment, 2018, 38(9): 34-38. DOI: 10.16081/j.issn.1006-6047.2018.09.006.

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田野, 张程, 毛昕儒, 等. 运用PCA改进BP神经网络的用电异常行为检测[J]. 重庆理工大学学报(自然科学), 2017, 31(8): 125-133. DOI: 10.3969/j.issn.1674-8425(z).2017.08.021. TIAN Y, ZHANG C, MAO X R, et al. Research on abnormal behavior of power consumption based on BP neural network with PCA[J]. Journal of Chongqing University of Technology (Natural Science), 2017, 31(8): 125-133. DOI: 10.3969/j.issn.1674-8425(z).2017.08.021.

GAO Y Q, FOGGO B, YU N P. A physically inspired data-driven model for electricity theft detection with smart meter data[J]. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5076-5088. DOI: 10.1109/TⅡ.2019.2898171.

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