Forecasting Large Scale Solar Photovoltaic Power Generation Plant in Auchi Polytechnic Using Machine Learning Tools

Solar energy is an abundant, clean, and renewable energy source, crucial for addressing the current global energy crisis. Efficiently harvesting solar power to generate electricity for smart grids is vital. However, the variability of solar radiation presents significant challenges in accurately forecasting solar photovoltaic (PV) power generation. Elements such as cloud cover, atmospheric conditions, and seasonal changes greatly influence the amount of solar energy available for electricity conversion. Accurate estimation of solar power output is therefore critical to evaluate the potential of smart grids. This study explores the use of various machine learning models to predict solar PV power generation in Lubbock, Texas. Performance is measured using Mean Squared Error (MSE) and R² metrics. The findings reveal that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the others, achieving MSE values of 2.06% and 2.23%, and R² values of 0.977 and 0.975, respectively. These results indicate that RFR and LSTM are highly effective in capturing the complex patterns and relationships in solar power generation data. The developed machine learning models can assist solar PV investors in optimizing their processes and enhancing their planning for solar energy production