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Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Author

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  • José de Jesús Camacho

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnológico de Monterrey, Monterrey 64849, Mexico)

  • Bernabé Aguirre

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnológico de Monterrey, Monterrey 64849, Mexico)

  • Pedro Ponce

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnológico de Monterrey, Monterrey 64849, Mexico)

  • Brian Anthony

    (Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA)

  • Arturo Molina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnológico de Monterrey, Monterrey 64849, Mexico)

Abstract

As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.

Suggested Citation

  • José de Jesús Camacho & Bernabé Aguirre & Pedro Ponce & Brian Anthony & Arturo Molina, 2024. "Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review," Energies, MDPI, vol. 17(2), pages 1-32, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:353-:d:1316497
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    References listed on IDEAS

    as
    1. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    2. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    3. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    4. Pedro Ponce & Citlaly Pérez & Aminah Robinson Fayek & Arturo Molina, 2022. "Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework," Energies, MDPI, vol. 15(23), pages 1-19, November.
    5. AlHajri, Ibrahim & Ahmadian, Ali & Alazmi, Radhi, 2023. "A comprehensive technical, economic, and environmental evaluation for optimal planning of renewable energy resources to supply water desalination units: Kuwait case study," Energy, Elsevier, vol. 275(C).
    6. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
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