Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals

Raghu Raman (), Sangeetha Gunasekar, Deepa Kaliyaperumal and Prema Nedungadi
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Raghu Raman: Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri 690525, Kollam, India
Sangeetha Gunasekar: Amrita School of Business, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India
Deepa Kaliyaperumal: Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, India
Prema Nedungadi: Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri 690525, Kollam, India

Sustainability, 2024, vol. 16, issue 21, 1-25

Abstract: The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.

Keywords: artificial intelligence; sustainable development goals; energy optimization; energy efficiency; renewable energy; industry collaboration; funding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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