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Artificial Intelligence Approaches for Energies

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  • Gwanggil Jeon

    (Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

In recent years, it has been noted that deep learning, machine learning, and artificial intelligence models are growing in popularity when applying big data for energy control and decision-making processes [...]

Suggested Citation

  • Gwanggil Jeon, 2022. "Artificial Intelligence Approaches for Energies," Energies, MDPI, vol. 15(18), pages 1-3, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6651-:d:912529
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    References listed on IDEAS

    as
    1. Elianne Mora & Jenny Cifuentes & Geovanny Marulanda, 2021. "Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks," Energies, MDPI, vol. 14(23), pages 1-26, November.
    2. Konstantina Fotiadou & Terpsichori Helen Velivassaki & Artemis Voulkidis & Dimitrios Skias & Corrado De Santis & Theodore Zahariadis, 2020. "Proactive Critical Energy Infrastructure Protection via Deep Feature Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    3. Alejandro J. del Real & Fernando Dorado & Jaime Durán, 2020. "Energy Demand Forecasting Using Deep Learning: Applications for the French Grid," Energies, MDPI, vol. 13(9), pages 1-15, May.
    4. Ying Ji & Jianhui Wang & Jiacan Xu & Donglin Li, 2021. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-19, April.
    5. Dasheng Lee & Fu-Po Tsai, 2020. "Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner," Energies, MDPI, vol. 13(8), pages 1-25, April.
    6. Shota Inuzuka & Bo Zhang & Tielong Shen, 2021. "Real-Time HEV Energy Management Strategy Considering Road Congestion Based on Deep Reinforcement Learning," Energies, MDPI, vol. 14(17), pages 1-20, August.
    7. Louis Desportes & Inbar Fijalkow & Pierre Andry, 2021. "Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage," Energies, MDPI, vol. 14(15), pages 1-22, August.
    8. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    9. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
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