A Novel Hybrid Model Proposal derived from Prevalent Methods for Power Generation Prediction of Solar Power Plants

Renewable energy sources play a pivotal role in contemporary distributed energy generation, owing to their significance in reducing energy costs and mitigating carbon emissions. Ensuring predictability in energy demand and production is crucial for effective future planning, wherein intuitive predictions for renewable energy sources are indispensable. In this study, we propose a novel method for predicting power generation in solar power plants. We develop a hybrid prediction model by combining prevalent machine learning models trained with meteorological data, yielding superior results compared to individual model outcomes. Through analysis, we evaluate the performance of the models trained with real meteorological and production data, while emphasizing the advantages of the proposed hybrid approach. The proposed method offers valuable insights into enhancing the predictability of solar power plant generation, thereby contributing to the advancement of renewable energy utilization.