Self-supervised inference of missing energy sources for enhancing energy consumption forecasting

While incorporating multiple correlated energy sources significantly improves energy consumption forecasting accuracy, practical data collection limitations often prevent access to complete multi-energy datasets across all regions. This paper addresses the critical gap between the theoretical benefits of multi-energy forecasting and the practical constraints of incomplete data collection by introducing a novel cross-regional inference framework. We propose the first self-supervised learning approach for inferring missing energy sources in multi-energy forecasting, which leverages complete datasets from reference regions to infer missing energy consumption patterns in target regions with incomplete data collection. The key innovation lies in our self-supervised representation learning method that captures complex inter-energy correlations beyond traditional distance-based metrics, enabling effective clustering of households with similar multi-energy consumption patterns. Our approach provides theoretical guarantees for clustering convergence and includes comprehensive complexity analysis, addressing fundamental questions about the scalability and reliability of cross-regional energy inference. Through extensive experiments on real-world datasets, we demonstrate substantial improvements over baseline approaches. Specifically, our method achieves up to 30% better inference accuracy compared to simple averaging methods. Furthermore, in the downstream forecasting task, it reduces the forecasting error (MAE) by an average of 15.2% and a maximum of 27.0% compared to single-source baselines, showing robust performance across different temporal patterns and regional characteristics. The source code for the proposed framework is available at Anonymous GitHub Repo.