Performance Enhancement of Solar Energy Prediction using Machine Learning Algorithms

Predicting solar power generation is necessary to successfully integrate solar energy into the grid and reduce the impact of changing weather on the system. Predicting the solar power output can reduce costs and improve financial predictions for solar energy systems. It allows for better timing of maintenance, reducing downtime and improving the efficiency of solar systems. Due to its adaptable nature, Random Forest (RF) is appropriate for a range of solar energy input variables such as temperature, humidity, solar irradiance, and others, whether numerical or categorical. Artificial neural networks are successful when working with large datasets and maintain good performance as data volume increases. This study presents Artificial Neural Networks in combination with the Random Forest Algorithm. Customizing this approach can address specific challenges in predicting solar energy, like incorporating different types of input data (e.g., time-series data, weather conditions) and adjusting to varying prediction goals (e.g., short-term forecasts, long-term predictions). Combining RF and ANN techniques can potentially achieve significant improvements in solar energy prediction accuracy and reliability.