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The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

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  • Miltiadis D. Lytras

    (School of Business, Deree College—The American College of Greece, 153-42 Athens, Greece
    Effat College of Engineering, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia)

  • Kwok Tai Chui

    (Department of Technology, School of Science and Technology, The Open University of Hong Kong, Ho Man Tin, Kowloon, Hong Kong, China)

Abstract

Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue “Artificial Intelligence for Smart and Sustainable Energy Systems and Applications”, eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

Suggested Citation

  • Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3108-:d:257213
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    References listed on IDEAS

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    11. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Ali & Tamer Moussa, 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks," Energies, MDPI, vol. 12(11), pages 1-15, June.
    12. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
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    15. Ahmed Gowida & Salaheldin Elkatatny & Emad Ramadan & Abdulazeez Abdulraheem, 2019. "Data-Driven Framework to Predict the Rheological Properties of CaCl 2 Brine-Based Drill-in Fluid Using Artificial Neural Network," Energies, MDPI, vol. 12(10), pages 1-17, May.
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    Cited by:

    1. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
    2. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    5. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.

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