Predicting Power Output of Solar Photovoltaic Panels Using Machine Learning Techniques

The prediction of solar power generation is essential for effective integration of renewable energy into power grids, aiding in grid stability, energy planning, and efficient resource allocation. Due to the inherent variability of solar energy caused by factors like weather patterns, time of day, and seasonal changes, machine learning (ML) has appeared as a powerful tool to improve forecasting accuracy. Solar panels with various tilt angle combinations are set up to collect experimental data. This paper uses supervised regression learner in machine learning application for solar power prediction. In this paper, linear regression and step wise linear regression models are giving fruitful results out of 27 models that are analyzed. A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and regression coefficient R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores. This study shows the effectiveness of Machine Learning (ML) in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems. For the linear regression algorithm, we obtained regression coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of 1, mean absolute percentage error (MAPE) of 0.7% and for the stepwise linear regression algorithm, we obtained a regression coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of 1, mean absolute percentage error (MAPE) 0.45%.