Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning

• Published in: Information systems (Oxford) 2025-01, Vol.127, p.102457, Article 102457
• Main Authors: Guan, Yongming, Shi, Yuliang, Wang, Gang, Zhang, Jian, Wang, Xinjun, Chen, Zhiyong, Li, Hui
• Format: Article
• Language: English
• Subjects: Anomaly detection; Contrastive learning; Electricity data; Gated fusion; Missing value completion
• ISSN: 0306-4379
• DOI: 10.1016/j.is.2024.102457

Abnormal electricity usage detection is the process of discovering and diagnosing abnormal electricity usage behavior by monitoring and analyzing the electricity usage in the power system. How to improve the accuracy of anomaly detection is a popular research topic. Most studies use neural networks for anomaly detection, but ignore the effect of missing electricity data on anomaly detection performance. Missing value completion is an important method to improve the quality of electricity data and to optimize the anomaly detection performance. Moreover, most studies have ignored the potential correlation relationship between spatial features by modeling the temporal features of electricity data. Therefore, this paper proposes an electricity anomaly detection model based on multi-feature fusion and contrastive learning. The model integrates the temporal and spatial features to jointly accomplish electricity anomaly detection. In terms of temporal feature representation learning, an improved bi-directional LSTM is designed to achieve the missing value completion of electricity data, and combined with CNN to capture the electricity consumption behavior patterns in the temporal data. In terms of spatial feature representation learning, GCN and Transformer are used to fully explore the complex correlation relationships among data. In addition, in order to improve the performance of anomaly detection, this paper also designs a gated fusion module and combines the idea of contrastive learning to strengthen the representation ability of electricity data. Finally, we demonstrate through experiments that the method proposed in this paper can effectively improve the performance of electricity behavior anomaly detection. •Integrating temporal and spatial features to jointly accomplish electricity anomaly detection.•Designing a gated fusion module to capture the dependencies between multi-features.•Introducing contrastive learning to enhance the representation ability of electricity data.