Machine Learning-Based Approaches for Solar Energy Forecasting

The increasing global interest in clean energy sources and the decreasing costs of solar panels position solar power as an advantageous option for wider adoption. However, the rapid uptake of intermittent renewable energy presents challenges, potentially causing power instability due to f luctuations between power generation and demand. Therefore, the accuracy of solar Photovoltaic (PV) power prediction becomes crucial to ensure stable system operations and optimize the integration of renewable sources. The current methods for forecasting solar PV power play a vital role in upholding system reliability and maximizing renewable energy integration. This scholarly paper offers a comprehensive and comparative evaluation of different Machine Learning (ML) techniques employed for PV power prediction, specifically focusing on short-term forecasts. The study provides insights into the factors influencing solar PV power prediction and presents an overview of existing prediction methods in the literature, with an emphasis on models based on Machine Learning approaches like Mutliple linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression and ensemble laerning methods like Random forest Regression ,Gradient boosting Regressor,ADA boost Regressor. To facilitate a more insightful comparison and a deeper understanding of advancements in this domain, the research conducts simulations to assess the performance of various ML methods used in predicting solar PV power. The article concludes a best machine learning model with a thorough discussion of the study's findings and their implications