Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information

The construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potential economic losses. Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. Hence, this paper proposes a method to detect abnormal residential water and electricity usage by incorporating multi-source information. Specifically, the correlation between water and electricity usage of residential customers is analyzed based on real metering data and the use of the Copula distribution function, followed by the integration of two innovative data mining techniques to form an anomaly detection framework. The distance correlation coefficient algorithm is used to measure the relevance of users’ water and electricity usage data. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the distance correlation coefficient for users and detect abnormal users whose distance correlation coefficient curves deviate from the normal user clusters. This multi-source approach avoids single-source bias by improving the data accuracy over one-dimensional methods. Experiments are implemented in a real low-voltage transformer area to prove the validity of the proposed method.