Advancements in hydrogen production through the integration of renewable energy sources with AI techniques: A comprehensive literature review

Mohammad Abdul Baseer, Prashant Kumar and Erick Giovani Sperandio Nascimento

Applied Energy, 2025, vol. 383, issue C, No S0306261925000844

Abstract: Hydrogen possesses the ability to produce energy with minimal greenhouse gas emissions when sustainably produced, making it a promising renewable energy carrier. Moreover, recent advancements in Artificial Intelligence (AI) can further enhance cleaner hydrogen production in a more optimised way. The main objective of this review paper is to comprehensively examine the current state-of-the-art in the integration of AI techniques with Renewable Energy Sources (RES), such as biomass, solar, algae power, geothermal, and wind to advance various hydrogen production methods, including electrolysis, biological, and photovoltaic processes. Furthermore, we aim to explore how AI optimisation can enhance sustainability, reliability, and commercial viability of Green Hydrogen (GH2) systems. These processes are crucial for reducing greenhouse gas emissions and meet the world's growing energy needs. The integration of RES with hydrogen production technologies has been recognised as a key strategy to attain a sustainable and environmentally friendly energy future, and the incorporation of AI can optimise efficiency and cost-effectiveness. This review found that there is a growing interest in the development of AI techniques to optimise GH2 production. While most of the studies focus on utilising wind and solar energy sources, this review found minimal existing research applying AI to GH2 production from algae, ocean, intermittency, and hybrid RES. Moreover, no works exploring AI to optimise GH2 production from sources like tidal and hydropower were found. Thus, prioritising AI-enabled system development to integrate and optimise these resources for GH2 production can help progress renewable generation capabilities towards a more sustainable, cleaner, carbon-free future for industry, transport, and societal sectors. Further extensive research is essential to fully harness the promise of AI in transforming diverse RES for clean hydrogen.

Keywords: Hydrogen production; Artificial intelligence; Renewable energy sources; Energy efficiency; Sustainable energy (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925000844
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:383:y:2025:i:c:s0306261925000844

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125354

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().