Artificial intelligence for hydrogen-enabled integrated energy systems: A systematic review
Siripond Mullanu, Caslon Chua, Andreea Molnar, Ali Yavari
Abstract
Hydrogen-enabled Integrated Energy Systems (H-IES) stand out as a promising solution with the potential to replace current non-renewable energy systems. However, their development faces challenges and has yet to achieve widespread adoption. These main challenges include the complexity of demand and supply balancing, dynamic consumer demand, and challenges in integrating and utilising hydrogen. Typical energy management strategies within the energy domain rely heavily on accurate models from domain experts or conventional approaches, such as simulation and optimisation approaches, which cannot be satisfied in the real-world operation of H-IES. Artificial Intelligence (AI) or Advanced Data Analytics (ADA), especially Machine Learning (ML), has the ability to overcome these challenges. ADA is extensively used across several industries, however, further investigation into the incorporation of ADA and hydrogen for the purpose of enabling H-IES needs to be investigated. This paper presents a systematic literature review to study the research gaps, research directions, and benefits of ADA, as well as the role of hydrogen in H-IES.