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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
P. Sivakumar
Department of Petroleum Engineering and Technology, JCT College of Engineering and Technology, Coimbatore-641 105, Tamilnadu,
Control & Systems Engineering Electrical & Electronics Engineering

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Machine learning techniques for solar energy generation prediction in photovoltaic systems Sumithra, J.; Vinitha, J. C.; Suganya, M. J.; Anuradha, M.; Sivakumar, P.; Balaji, R.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i3.pp2055-2062

Abstract

For photovoltaic (PV) systems to be as effective and dependable as they possibly can be, it is vital to make an accurate prediction of the amount of power that will be generated by the sun. Using machine learning, it is now much simpler to forecast the amount of solar energy that will be generated. These approaches are more accurate and are able to adapt to the ever changing conditions of the nature of the environment. We take a look at the most recent machine learning algorithms for predicting solar energy and examine their methodology, as well as their strengths and drawbacks, in this paper. Using performance metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) makes it possible to evaluate important algorithms like support vector machines, decision trees, and linear regression. The results show that machine learning could help make predictions more accurate, lower the amount of uncertainty in operations, and help people make decisions in real time for PV systems. The study also points out important areas where research is lacking and suggests ways to move forward with the use of machine learning in systems that produce renewable energy.
Co-Authors Anuradha, M. Balaji, R. Suganya, M. J. Sumithra, J. Vinitha, J. C.