Research on Power Data Anomaly Detection Based on Deep Neural Network Techniques

Zhang Beilei; Wang Guoliang; Xie Fenlong; Sun Dashan; Yang Xicheng

PDF(1563 KB)

Integrated Circuits and Embedded Systems ›› 2023, Vol. 23 ›› Issue (10) : 36-39.

TECHNOLOGY REVIEW

Research on Power Data Anomaly Detection Based on Deep Neural Network Techniques

Author information +

History +

Abstract

In this paper,a deep learning-based method for detecting anomalies in electricity data is proposed,which solves the problem in two stages:firstly,a neural network based on long short-term memory (LSTM) is built for predicting the next hour's samples.Secondly,the output of the first stage is used as the input to the LSTM self-encoder for learning normal consumption features using the LSTM self-encoder.If the input differs from the output,it indicates the presence of anomalies,and an exponential moving average is used as a threshold to distinguish between local anomalies and global anomalies.In addition,weather features,time and lag features are considered in this paper,and feature selection is performed to find the best combination.By comparing the validation methods of anomalous and normal consumption,the results show a significant increase in power consumption during anomalies,and the time and lag features can improve the efficiency and performance of the proposed method.

Key words

LSTM auto encoder / deep learning / anomaly detection / electricity consumption / anoma-lous consumption

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhang Beilei , Wang Guoliang , Xie Fenlong , et al . Research on Power Data Anomaly Detection Based on Deep Neural Network Techniques[J]. Integrated Circuits and Embedded Systems. 2023, 23(10): 36-39

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	陈启鑫, 郑可迪, 康重庆, 等. 异常用电的检测方法:评述与展望[J]. 电力系统自动化, 2018, 42(17):189-199. Cited in this article [1]

[2]	黄丹, 王凯, 庄磊, 等. 一种基于敏感性分析的用电异常检测模型[J]. 信息技术, 2022, 46(11):77-81. Cited in this article [1]

[3]	林统喜, 钟福龙. 基于大数据分析的异常通信信号智能检测系统设计[J]. 单片机与嵌入式系统应用, 2021, 21(12):35-38. Cited in this article [1]

[4]	陈敏. LSTM混合算法在用户电量数据异常检测中的应用[J]. 单片机与嵌入式系统应用, 2022, 22(10):21-24,28. Cited in this article [1]

[5]	WENG Y, ZHANG N, XIA C. Multi-agent-based unsupervised detection of energy consumption anomalies on smart campus[J]. IEEE Access, 2018(7):2169-2178. Cited in this article [1]

[6]	LIU X, DING Y, TANG H, et al. A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data[J]. Energy and Buildings, 2021(231):110601. Cited in this article [1]

[7]	张昕, 孙莉, 许高俊. 基于深度森林算法的异常用电行为检测方法[J]. 电子设计工程, 2022, 30(19):115-119. Cited in this article [1]

[8]	黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报, 2018, 41(7):1619-1647. Cited in this article [1]

[9]	SU Y, KUO C-C J. On extended long short-term memory and dependent bidirectional recurrent neural network[J]. Neurocomputing, 2019(356):151-161. Cited in this article [1]

[10]	VISSER L, ALSKAIF T, VAN SARK W. The importance of predictor variables and feature selection in day-ahead electricity price forecasting[C]// proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), 2020. Cited in this article [1]