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Correlation-driven multi-level learning for anomaly detection on multiple energy sources
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DOI：10.1016/j.asoc.2024.111636.png)
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摘要
En 中文
Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently, other energy sources such as water, gas, and heating have also been actively collected. As a result, it is necessary to develop a unified methodology for anomaly detection across multiple energy sources; however, research efforts have rarely been made to tackle this issue. In this study, we propose a new correlation -driven multilevel learning model for anomaly detection on multiple energy sources. The distinguishing property of the model incorporates multiple energy sources in multi -levels based on the strength of the correlations between them. Our model is scalable to integrate arbitrary new energy sources, with further performance improvement, considering both correlated and non -correlated sources. Through extensive experiments on real -world datasets consisting of three to five energy sources, we demonstrate that the proposed model clearly outperforms the existing multimodal learning and recent time -series anomaly detection models and makes further performance improvement as more energy sources are integrated, showing the scalability of the proposed model.
Keyword:
Multiple energy sources
Multi-energy anomaly detection
Correlation-based anomaly definition
Multi-level learning
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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