Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Su, S., Yan, X., Agbossou, K., Chahine, R. et Zong, Y. (2022). Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study. Journal of Physics: Conference Series, 2208 . Article 012013. ISSN 1742-6588 1742-6596 DOI 10.1088/1742-6596/2208/1/012013

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Abstract

In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik's weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.

Type de document:	Article
Mots-clés libres:	Bioinformatics Forecasting Neural networks Optimization Supply chains Artificial intelligence methods Case-studies Computer technology Electricity prices Hybrid renewable energies Load demand Optimization algorithms Output power Power load System resources Renewable energy resources
Date de dépôt:	22 janv. 2026 15:28
Dernière modification:	22 janv. 2026 15:28
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12544

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