Forecasting Solar PV Panel Performance Using Linear Regression and Stepwise Linear Regression Machine Learning Algorithms

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 regression learner technique in machine learning for solar power prediction.In this paper, linear regression and step wise linear regression algorithms are giving fruitful results compared to other algorithms.A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and R scores.This study shows the effectiveness of ML in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems.We obtained a regression coefficient (R 2 ) of 1 and a MAPE of 0.7% and 0.45% for linear regression algorithm and stepwise linear regression algorithm respectively.