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

Konduru Sudharshan, C. Naveen (), Pradeep Vishnuram, Damodhara Venkata Siva Krishna Rao Kasagani and Benedetto Nastasi ()
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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

Energies, 2022, vol. 15, issue 17, 1-39

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.

Keywords: solar energy; forecast; time series models; hybrid model; ensemble learning; AI techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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