Multi-View Stacking Ensemble for Power Consumption Anomaly Detection in the Context of Industrial Internet of Things

被引:35
|
作者
Ouyang, Zhiyou [1 ,2 ]
Sun, Xiaokui [1 ,2 ]
Chen, Jingang [3 ]
Yue, Dong [1 ,2 ,4 ]
Zhang, Tengfei [2 ]
机构
[1] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210023, Jiangsu, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Sch Econ, Nanjing 210023, Jiangsu, Peoples R China
[4] Nanjing Univ Posts & Telecommun, Jiangsu Engn Lab Big Data Anal & Control Act Dist, Nanjing 210023, Jiangsu, Peoples R China
来源
IEEE ACCESS | 2018年 / 6卷
关键词
Internet of Things; machine learning; smart grids; time series analysis; feature extraction; power consumption; anomaly detection;
D O I
10.1109/ACCESS.2018.2805908
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Anomaly detection of power consumption, mainly including electricity stealing and unexpected power energy loss, has been one of the essential routine works in power system management and maintenance. With the help of Industrial Internet of Things technologies, power consumption data was aggregated from distributed various power devices. Hence, the power consumption anomaly was able to be detected by machine learning algorithms. In this paper, a three-stage multi-view stacking ensemble (TMSE) machine learning model based on hierarchical time series feature extraction (HTSF) methods are proposed to solve the anomaly detection problem: HTSF is a novel systematic time series feature engineering method to represent the given data numerically and as input data for machine learning algorithms, while TMSE is designed to ensemble meta-models to archive more accurate performance by using multi-view stacking ensemble method. Performance evaluation in real-world data shows that the proposed method outperforms the existing time series feature extraction means and dramatically decreases the time consumed for ensemble learning process.
引用
收藏
页码:9623 / 9631
页数:9
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