Multi-Task Graph Attention Net for Electricity Consumption Prediction and Anomaly Detection

Abstract

Precise electricity consumption forecasting and anomaly detection constitute fundamental requirements for maintaining grid reliability in smart power systems. While consumption patterns demonstrate quasi-periodic behavior with region-specific fluctuations influenced by environmental factors, existing approaches may fail to systematically model these dynamic variations or quantify environmental impacts. This limitation results in a compromised prediction accuracy and ambiguous anomaly identification. To overcome these challenges, we propose a novel Multi-Task Graph Attention Network (MGAT) framework leveraging an adaptive entropy analysis. Our methodology comprises four key innovations: (1) the temporal decomposition of consumption data with entropy-based adaptive clustering into predictable low-entropy components (processed via multi-scale attention networks) and volatile high-entropy components; (2) the graph-based representation of high-entropy fluctuations through numerical correlation encoding, complemented by temporal environmental graphs quantifying external influences; (3) the hierarchical fusion of environmental and fluctuation graphs via a specialized Graph Attention Autoencoder that jointly models dynamic patterns and environmental dependencies; (4) the integrated synthesis of all components for simultaneous consumption prediction and anomaly detection. Experiments verify the MGAT’s performance in both forecasting precision and anomaly identification compared to conventional methods.