Predicting Future Power Generation and Diagnosing Failure Rate at a Solar Power Plant in India Using Machine Learning Algorithms

In light of the global energy crisis and the need to meet the huge demands for electricity, it is crucial to make short-term predictions about energy production. Solar energy is a clean and sustainable source of electricity generation, and solar systems are connected to smart grids that feed all categories of users. Therefore, it is important to consider the potential for solar energy production in the short term. This paper presents an approach based on machine learning algorithms to predict solar energy generation in a solar plant in India. Performance of the proposed models, linear regression and random forest regression, is verified using metrics such as R2, mean square error (MSE), MAE, The RFR model demonstrated an accuracy rate of 99%, while the LR model achieved 98% in forecasting the amount of energy generated.