A Hybrid Decomposition and Deep Learning Model for Photovoltaic Power Forecasting Under Variable Meteorological Conditions

To improve photovoltaic (PV) power forecasting under variable meteorological conditions, this paper proposes a hybrid model combining signal decomposition, clustering, and deep learning. An improved complete ensemble empirical mode decomposition with adaptive noise method is used for multi-scale decomposition of meteorological inputs such as temperature, solar radiation, and wind direction. Sample entropy-guided K-means clustering segments signals into high, medium, and low-frequency components, with high-frequency parts further denoised using variational mode decomposition. A convolutional neural network-bidirectional long short-term memory network is then optimized by the crown porcupine optimization algorithm to fine-tune key hyperparameters. Experiments on real PV data show a 20% root mean squared error reduction (to 7.30 kW), demonstrating strong adaptability and robustness for intelligent PV scheduling.