PV power prediction method based on ground-based cloud map and hybrid neural network

Under cloudy weather, PV power will fluctuate dramatic due to the sporty cloud shading. However, most existing PV power prediction models do not utilize the cloud map sufficiently. In this paper, we propose a novel method to improve the prediction accuracy through combining cloud map features and hybrid neural network. Firstly, we extract the static and dynamic features of the ground-based cloud map using image processing techniques. Secondly, we establish an PV power prediction model based on the ensemble empirical mode decomposition-bi-long short-term memory (EEMD-BiLSTM) with numerical weather information and historical power data. Finally, in order to further improve the power prediction accuracy of cloudy weather, we build an error correction model using the cloud map features, based on the light gradient boosting machine (LightGBM) decision tree. Experimental results on the data from a PV plant in western China shows that our method can effectively improve the accuracy of PV power prediction under cloudy weather.