Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

Juan D. Velásquez, Lorena Cadavid and Carlos J. Franco ()
Additional contact information
Juan D. Velásquez: Departamento de Ciencias de la Computación y la Decisión, Facultad de Minas, Universidad Nacional de Colombia, Medellín 1027, Colombia
Lorena Cadavid: Departamento de Ciencias de la Computación y la Decisión, Facultad de Minas, Universidad Nacional de Colombia, Medellín 1027, Colombia
Carlos J. Franco: Departamento de Ciencias de la Computación y la Decisión, Facultad de Minas, Universidad Nacional de Colombia, Medellín 1027, Colombia

Energies, 2023, vol. 16, issue 19, 1-45

Abstract: In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems.

Keywords: sustainability; renewable energy; tech mining; artificial intelligence; bibliometric analysis; machine learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/19/6974/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/19/6974/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:19:p:6974-:d:1254637

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().