Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Hugo Gaspar Hernandez-Palma, Jonny Rafael Plaza Alvarado, Jesús Enrique García Guiliany, Guilherme Luiz Dotto and Claudete Gindri Ramos
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Hugo Gaspar Hernandez-Palma: Faculty of Engineering, Ibero-American University Corporation, Calle 67#5-27, Bogotá, Colombia; & Faculty of Engineering, EAN University, Av. Chile: Calle 71 # 9-84, Bogota Colombia
Jonny Rafael Plaza Alvarado: Faculty of Engineering, EAN University, Av. Chile: Calle 71 # 9-84, Bogota Colombia
Jesús Enrique García Guiliany: Corporación Universitaria Latinoamericana, Calle 58 # 55 - 24A, Barranquilla, Colombia
Guilherme Luiz Dotto: Research Group on Adsorptive and Catalytic Process Engineering (ENGEPAC), Federal University of Santa Maria, Av. Roraima, 1000–7, 97105–900 Santa Maria, RS, Brazil
Claudete Gindri Ramos: Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002, Barranquilla, Atlántico, Colombia

International Journal of Energy Economics and Policy, 2024, vol. 14, issue 2, 1-10

Abstract: The global energy industry fundamentally transformed towards renewable energy sources, driven by the sustainability paradigm. This shift was crucial in addressing the challenges of climate change and resource scarcity. Machine Learning (ML) played a pivotal role in enhancing the efficiency and reliability of renewable energy systems. This study conducted a comprehensive analysis of scientific production at the intersection of ML and renewable energy generation, focusing on Latin America. Employing a methodology based on documentary research and bibliometric processes, utilizing the Scopus database with the support of R and VOSviewer software, our research revealed a significant increase in interest and investment in research related to ML and renewable energies since 2020. This exponential growth scenario in this knowledge area had significant implications for Latin America and the world, fostering technological advancements and the adoption of renewable energies. Countries such as China, India, the United States, South Korea, and Saudi Arabia represented 61% of the global scientific production in this field, underscoring its global relevance. This growth indicated a growing interest and investment in ML applications in renewable energies, aligning with the 2030 Agenda for Sustainable Development. This research aligns with the Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure). It contributed to progress toward a more sustainable future, benefiting society through more efficient and sustainable energy systems, the energy industry through increased innovation and the adoption of clean technologies, and Latin America, which could leverage these findings to sustainably drive its economic and environmental development.

Keywords: Machine Learning; Power Generation; Renewable Energies (search for similar items in EconPapers)
JEL-codes: A30 Q42 Q56 (search for similar items in EconPapers)
Date: 2024
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