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September 10, 2025

AI in Sustainable Energy: Applications and Innovations

Key Points

AI improves energy generation efficiency while addressing challenges associated with grid stability and renewable sources.
Specific AI and machine learning techniques enhance renewable energy applications, particularly in solar and wind prediction.
The study employs topic modeling methods including LDA and BERT to categorize research themes in energy applications.
Future research recommendations aim to guide policymakers in integrating AI with social equity and environmental considerations.

Abstract

Incorporation of Artificial Intelligence (AI) into sustainable energy systems is a major achievement in maximizing efficient energy generation, distribution, and usage. This study examines the potential of AI to be a transformative technology, complements existing studies and attempts to pinpoint potential gaps that require further research while introducing a new model for adoption. The evaluation identifies specific AI and machine learning techniques that are technically viable, as well provides a multicriteria approach for ranking energy applications to be opportunities for AI and machine learning. Leading applications encompass solar and wind prediction, fault diagnostics, and grid stability, among others, although associated issues related to for instance intermittency and computational scalability continue as well. The moral and environmental implications of using AI as it relates to more than just optimizing the technology itself are discussed. The idea of ‘Sustainable AI’ is proposed, in an effort to promote a conceptual frame that integrates environmental sustainability with considerations of social equity. A combination of LDA, BERT, and clustering on topic modeling is utilized to break down the literature into the eight main research themes, such as smart buildings, and renewable energy evaluation. The findings give rise to 14 recommendations for future research, which can provide a guide for policymakers and practitioners to synthesize theoretical knowledge with field practice. The use and potential of A.I . brings also new emerging risks as well as opportunities for progress in over 134 Sustainable Development Goal (SDG) targets . Challenges, such as the ‘black box’ problem of machine learning, privacy concerns, and the energy requirements of machine learning algorithms, require governance and to be puzzles over . It concludes by highlighting the need for interdisciplinarity, suggesting a form of STEAM ( STEM + Arts) approach to help advance innovation in an inclusive manner . Policy-based recommendations highlight support for mixed renewable systems, adaptable ML designs, and life-cycle- conscious AI research. The review thus also ultimately lays out a pathway for using AI to set in motion socio-technical carbon-neutral energy transitions

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