Optimizing Solar Power Forecasting by Employing Various Machine Learning and Deep Learning Strategies for Enhanced Accuracy and Reliability

Before investing in a solar power system, it is critical to properly assess the viability of solar power generation. This evaluation is carried out using a detailed forecasting procedure that entails examining a variety of parameters to predict the probable success and efficiency of using solar energy in that specific geographical location. In the pursuit of advancing solar power forecasting, this research explores a range of machine learning and deep learning strategies to optimize accuracy and reliability. Through a comprehensive evaluation, the Random Forest Regression (RFR) model emerges as the most effective, exhibiting lower Root Mean Square Error (RMSE) and higher R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) values compared to alternative models. These results underscore the significance of leveraging diverse and sophisticated methodologies for achieving superior solar power predictions. The study contributes valuable insights to the field, emphasizing the importance of model selection in enhancing the performance of solar energy forecasting systems.