Optimizing renewable energy systems through artificial intelligence: Review and future prospects

Kingsley Ukoba, Kehinde O. Olatunji, Eyitayo Adeoye, Tien-Chien Jen and Daniel M. Madyira

Energy & Environment, 2024, vol. 35, issue 7, 3833-3879

Abstract: The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Keywords: Renewable energy; energy management systems; climate change; artificial intelligence; energy modeling (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:engenv:v:35:y:2024:i:7:p:3833-3879

DOI: 10.1177/0958305X241256293

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