Photovoltaic generation forecasting using convolutional and recurrent neural networks

Due to climate change consequences, it is very important to replace fossil energy resources with renewable energy resources. Solar energy is one of the main types of renewable energy resources which is harnessed by Photovoltaic (PV) Cells. It is important to accurately forecast how much electricity these energy resources generate to help operate and maintain the electricity grid. But the generation of electricity by PV is often associated with large uncertainty due to varying features like radiation, wind, humidity, and temperature. Deep learning methods have proved useful for this forecasting problem but the spatial information of features for this type of method has not received the due attention for PV generation forecasting. This study aimed to explore how both spatial and temporal information can be considered via a deep learning approach. In this paper, we propose a PV generation forecaster that considers both spatial and temporal information. A convolutional neural network is used as a pre-processing step to capture spatial information. The convolutional neural network is followed by a gated recurrent unit neural network to model temporal characteristics. The proposed model enriches the forecaster model by feeding more meaningful features into the recurrent neural network rather than raw data. The proposed model can predict a horizon for which there is no available information on irradiance, humidity, or wind. We show experimentally that our method is competitive with the state-of-the-art in terms of time and memory requirement while resulting in better prediction performance. The proposed model is applied to real data collected by the research team, and its performance is compared with some state-of-the-art methods. The results show the advantage of the proposed method.