PVHybNet: A hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data

Abstract

Photovoltaics has gained popularity as a renewable energy source in recent decades. The main challenge for this energy source is the instability in the amount of generated energy owing to its strong dependency on the weather. Therefore, prediction of solar power generation is important for reliable and efficient operation. Popular data sources for predictors are largely divided into recent weather records and numerical weather predictions. This study proposes adequate deep neural networks that can utilise each data source or both. Focusing on a 24-hour-ahead prediction problem, the authors first design two deep neural networks for prediction: a deep feedforward network that uses the weather forecast data and a recurrent neural network that uses recent weather observations. Finally, a hybrid network, named PVHybNet, combines the both networks to enhance their prediction performance. In predicting the solar power generation by Yeongam power plant in South Korea, the final model yields an R-squared value of 92.7%. The results support the effectiveness of the combined network that utilises both weather forecasts and recent weather observations. The authors also demonstrate that the hybrid model outperforms several machine learning models.