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Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

Author

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  • Konduru Sudharshan

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • C. Naveen

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Pradeep Vishnuram

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Damodhara Venkata Siva Krishna Rao Kasagani

    (Department of Electrical Engineering, National Institute of Technology, Srinagar 190006, India)

  • Benedetto Nastasi

    (Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, Italy)

Abstract

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

Suggested Citation

  • Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6267-:d:900099
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    Cited by:

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    2. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.
    3. Jeehong Kim & Seok-ho Lee & Kil To Chong, 2022. "A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant," Energies, MDPI, vol. 15(22), pages 1-19, November.
    4. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.
    5. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).
    6. Kuang-Sheng Liu & Iskandar Muda & Ming-Hung Lin & Ngakan Ketut Acwin Dwijendra & Gaylord Carrillo Caballero & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room," Sustainability, MDPI, vol. 15(2), pages 1-14, January.

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