The application of WUTP-CNN-GRU model in power prediction of desert and floating photovoltaic system

Due to the intermittent nature of solar energy and meteorological uncertainty, accurate photovoltaic (PV) power generation prediction is crucial to grid stability. In this study, a new WUTP-CNN-GRU hybrid model is proposed based on the in-depth study and analysis of the measured data of Dongdatan desert and Yangzhong floating PV power stations. Firstly, focusing on the data preprocessing link, the fuzzy C-means clustering (FCM) method is innovatively introduced to effectively divide the weather types, and the prediction models suitable for different working conditions such as sunny, cloudy and rainy days are constructed based on the classification results. The model integrates CNN and GRU architecture, and introduces WUTP algorithm to optimize hyperparameters. The comparison experiments with WOA, PSO and DB0 optimization models show that the estimated performance of this model is significantly better than that of the comparison model, and it shows excellent adaptability and stability under different weather conditions, which provides an effective solution for smart grid management and PV system optimization.