Forecasting techniques for green hydrogen energy community scheduling: A comprehensive review and case study

The increasing deployment of distributed energy resources is accelerating the transition toward sustainable and decentralised energy systems. In this context, Hydrogen Energy Communities (HECs) have emerged as a promising framework for integrating local energy generation, hydrogen production, storage, and consumption. This paper presents a comprehensive review of forecasting-informed HEC operation by integrating HEC architectures, deep learning-based forecasting, uncertainty management, and optimisation-based scheduling within a unified operational framework. The review identifies three major research gaps, including the lack of an integrated HEC operational framework, limited integration of forecasting with uncertainty-aware scheduling, and insufficient practical validation of forecasting-informed operation. State-of-the-art forecasting and uncertainty-modelling approaches are critically compared. A numerical case study benchmarks four deep learning forecasting models and demonstrates their integration with optimisation-based scheduling. The findings highlight the potential of forecasting-informed scheduling to improve HEC operational efficiency and community self-sufficiency while identifying future research directions.