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Artificial Intelligence and Machine Learning in Energy Conversion and Management

Author

Listed:
  • Konstantinos Mira

    (Computer Science Department, CentraleSupélec Paris-Saclay University, 3 Rue Joliot Curie, Gif-sur-Yvette, 91190 Paris, France)

  • Francesca Bugiotti

    (Computer Science Department, CentraleSupélec Paris-Saclay University, 3 Rue Joliot Curie, Gif-sur-Yvette, 91190 Paris, France
    Le Laboratoire de Recherche en Informatique, CNRS, Paris-Saclay University, 6 Rue Noetzlin, Gif-sur-Yvette, 91190 Orsay, France)

  • Tatiana Morosuk

    (Institute for Energy Engineering, Technische Universität Berlin, Marchstr. 18, 10587 Berlin, Germany)

Abstract

In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

Suggested Citation

  • Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7773-:d:1287773
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    References listed on IDEAS

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