基于深度自编码器的电力能耗异常检测方法

doi:10.16182/j.issn1004731x.joss.22-FZ0931

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (12): 2557-2565.doi: 10.16182/j.issn1004731x.joss.22-FZ0931

基于深度自编码器的电力能耗异常检测方法

孙宁可(), 王艳(), 纪志成

江南大学物联网技术应用教育部工程研究中心，江苏无锡 214122

收稿日期:2022-08-07 修回日期:2022-09-26 出版日期:2022-12-31 发布日期:2022-12-21
通讯作者: 王艳 E-mail:191253041@qq.com;wangyan@jiangnan.edu.cn
作者简介:孙宁可(1998-)，男，硕士生，研究方向为工业装备与节能控制。E-mail：191253041@qq.com
基金资助:
国家重点研发计划(2018YFB1701903);国家自然科学基金(61973138)

Anomaly Detection Method of Electrical Power Consumption Based on Deep Autoencoder

Ningke Sun(), Yan Wang(), Zhicheng Ji

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2022-08-07 Revised:2022-09-26 Online:2022-12-31 Published:2022-12-21
Contact: Yan Wang E-mail:191253041@qq.com;wangyan@jiangnan.edu.cn

摘要/Abstract

摘要：

针对电力能耗数据的非线性和不平稳特征，提出了一种基于深度自编码器的电力能耗异常检测模型。将深度学习的门控循环单元网络和自编码器结构相结合，通过传统自编码器的编码器和解码器部分采用门控循环单元网络来实现，充分发挥门控循环单元的数据特征提取能力和自编码器结构的数据重构功能。根据原始数据和重构数据之间的重构误差来检测电力能耗异常数据点。将所提方法应用于实际的车间电力能耗数据集，结果表明：所提方法能够对电力能耗数据进行异常点检测，检测效果良好。

关键词: 能耗异常检测, 门控循环单元, 自编码器, 深度自编码器, 重构误差

Abstract:

Aiming at the nonlinear and non-stationary characteristics of electrical power consumption data, an abnormal electrical power consumption detection model based on deep autoencoder is proposed. Gated recurrent unit (GRU) network of the deep learning is combined with autoencoder structure, and the encoder and decoder parts of traditional autoencoder are realized by gated recurrent unit network, which gives full play to the data feature extraction capability of gated recurrent unit and the data reconstruction function of autoencoder structure. Based on the reconstruction error between original data and reconstructed data, abnormal data points of the electrical power consumption are detected. By applying the proposed method to actual workshop electrical power consumption data set, it is shown that the proposed method can detect the abnormal points of power consumption data, and the detection effect is better.

Key words: anomaly detection of energy consumption, gated recurrent unit, autoencoder, deep autoencoder, reconstruction error

中图分类号:

TP391.9

孙宁可, 王艳, 纪志成. 基于深度自编码器的电力能耗异常检测方法[J]. 系统仿真学报, 2022, 34(12): 2557-2565.

Ningke Sun, Yan Wang, Zhicheng Ji. Anomaly Detection Method of Electrical Power Consumption Based on Deep Autoencoder[J]. Journal of System Simulation, 2022, 34(12): 2557-2565.

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参数名称	参数设置
optimizer	Adam
dropout rate	0.2
learning rate	0.001
epoch	200
batch size	24

检测模型	P	R	F₁
传统RNN自编码器	0.948	0.891	0.918
LSTM深度自编码器	0.970	0.951	0.961
GRU深度自编码器	0.973	0.953	0.963

检测模型	平均运行时间
传统RNN自编码器	157.63
LSTM深度自编码器	474.66
GRU深度自编码器	204.95

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基于深度自编码器的电力能耗异常检测方法

Anomaly Detection Method of Electrical Power Consumption Based on Deep Autoencoder

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