Review and prospect of data-driven techniques for load forecasting in integrated energy systems

Abstract

With synergies among multiple energy sectors, integrated energy systems (IESs) have been recognized lately as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. The core of IES operation is to keep energy balance between supply and demand, where accurate load forecasting serves as one of the most crucial cornerstones. Recent advances in data-driven techniques have spawned a whole new branch of solution for load forecasting in IESs, which urges the need for a timely review accordingly. First, this overview reveals the uniqueness of the IES load forecasting problem compared with the conventional problem in electric power systems. The influential factors are much more complicated and volatile, while multivariate load series are forecasted simultaneously to address the coupling among different energy sectors. This uniqueness has contributed to increasing works and early breakthroughs for the IES load forecasting problem. Then, following the application and implementation procedures, essential issues of data-driven techniques in current works are reviewed with respect to the IES settings such as the variable decision, data preparation, feature engineering, model identification, and augmentation strategy adoption. The procedures are summarized according to current works and have covered all of the effective solutions for accurate forecasts. Finally, future trends and prospects of advanced topics therein are identified beyond current breakthroughs. Compatible with the distributed structure of IESs, federated learning is a promising solution for coordinated load forecasting among diverse energy sectors. On the other hand, automated machine learning builds deep learning and other data-driven models more intelligently to extremely improve load forecasting in complex IESs. The limited data issue in IESs also warrants further research efforts.