Load Forecasting for Sustainable Demand-Side Energy Management Under User Heterogeneity and Temporal Dynamics
TL;DR
It is found that appropriate strategy design can improve MSE by more than 6% compared with using only a single model, and both inter-user heterogeneity and temporal dynamics provide useful and complementary information for improving day-ahead individual load forecasting.
Abstract
The increasing integration of renewable energy is a key pathway toward sustainable energy transitions, but it also introduces supply-side uncertainty that makes demand flexibility management increasingly important. In this context, accurate load forecasting becomes a key task for balancing uncertain supply with demand-side behavior and supporting more reliable energy management. Most existing studies approach load forecasting by developing advanced forecasting models. However, in real-world commercial-user forecasting, load data often exhibit inter-user heterogeneity and temporal dynamics, making forecasting performance depend not only on model architecture, but also on how models are trained, shared, combined, and used; we refer to these design choices as forecasting strategies. Therefore, we study day-ahead individual load forecasting from a strategy-level perspective using historical load observations only. We compare homogeneous strategies, inter-user-heterogeneity-aware strategies, and temporal-dynamics-aware strategies using three real-world electricity consumption datasets from China. The results show that forecasting strategy design substantially affects prediction performance, and that both inter-user heterogeneity and temporal dynamics provide useful and complementary information for improving day-ahead individual load forecasting. We find that appropriate strategy design can improve MSE by more than 6% compared with using only a single model. Overall, the study provides useful methodological tools and practical insights for load forecasting and sustainable energy management.