The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review

Tymoteusz Miller (), Irmina Durlik, Ewelina Kostecka, Polina Kozlovska, Marek Staude and Sylwia Sokołowska
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Tymoteusz Miller: Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
Irmina Durlik: Faculty of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Ewelina Kostecka: Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Polina Kozlovska: Faculty of Economics, Finance and Management, University of Szczecin, 71-415 Szczecin, Poland
Marek Staude: Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Sylwia Sokołowska: Polish Society of Bioinformatics and Data Science BioData, 71-214 Szczecin, Poland

Energies, 2025, vol. 18, issue 5, 1-29

Abstract: The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables is accompanied by new challenges, including the growing demand for rare-earth elements, the need for recycling end-of-life equipment, and the rising energy footprint of digital tools—particularly artificial intelligence (AI) models. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications in renewable energy systems. We examined empirical and conceptual studies published between 2010 and 2024 that address the deployment of AI in renewable energy, the circular economy paradigm, and model distillation and low-energy AI techniques. Our findings indicate that adopting distilled AI models can significantly reduce energy consumption in data processing, enhance grid optimization, and support sustainable resource management across the lifecycle of renewable energy infrastructures. This review concludes by highlighting the opportunities and challenges for policymakers, researchers, and industry stakeholders aiming to integrate circular economy principles into RE strategies, emphasizing the urgent need for collaborative solutions and incentivized policies that encourage low-footprint AI innovation.

Keywords: distilled AI models; renewable energy; circular economy; systematic review; PRISMA; sustainable transition; resource management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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