Machine Learning Based Energy Forecasting for Photovoltaic Solar Plants

As the global population continues to grow and technological advancements progress, energy demand is becoming increasingly noticeable worldwide. Solar energy, which is among the sustainable energy sources to reduce the environmental impact of fossil fuels, has a critical role in the global energy transition. Energy generation forecasts play a vital role in supply-demand balance, grid stability and cost optimization. Moreover, accurate and reliable generation forecasts are essential to facilitate the integration of renewable energy sources and improve the efficiency of energy systems. In this study, generation data from April 2022 to April 2024 for a solar power plant in Şanlıurfa province and weather data obtained from Solcast API service are used. The performance of machine learning algorithms such as XGBoost, Extra Trees, k-Nearest Neighbors (KNN), Gradient Boosting, Random Forest and Linear Regression are evaluated. The results show that the KNN model outperforms the other algorithms with a Mean Square Error (MSE) of 172.92, Root Mean Square Error (RMSE) of 13.14, Mean Absolute Error (MAE) of 5.37 and R² score of 0.95. This study contributes to a more reliable estimation of solar power generation, facilitating the integration of renewable energy sources and offering significant potential for the optimization of energy management systems.