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Machine Learning in Renewable Energy

Author

Listed:
  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

Abstract

The volume of energy produced and consumed from renewable sources increases by a significant rate both in absolute value and also as a proportion of the total energy produced and consumed [...]

Suggested Citation

  • Periklis Gogas & Theophilos Papadimitriou, 2023. "Machine Learning in Renewable Energy," Energies, MDPI, vol. 16(5), pages 1-3, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2260-:d:1081544
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    References listed on IDEAS

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    1. Dinh Thanh Viet & Vo Van Phuong & Minh Quan Duong & Quoc Tuan Tran, 2020. "Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms," Energies, MDPI, vol. 13(11), pages 1-22, June.
    2. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    3. Dong-Jin Bae & Bo-Sung Kwon & Kyung-Bin Song, 2021. "XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation," Energies, MDPI, vol. 15(1), pages 1-16, December.
    4. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
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