A CEEMDAN-CNN-BiLSTM-SDQN Framework for Photovoltaic Power Forecasting: Integrating Multi-Scale Decomposition with Adaptive Reinforcement Learning Compensation

Accurate photovoltaic (PV) power forecasting is crucial for grid stability and the integration of renewable energy. To address the multiscale, nonlinear characteristics of PV power series and the limitations of traditional methods in dynamic error compensation, a novel hybrid forecasting framework is proposed, integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM), and a Simplified Deep Q-Network (SDQN). The framework first decomposes the power series into subcomponents across different frequency bands via CEEMDAN. Subsequently, dedicated CNN-BiLSTM sub-models are employed in parallel to extract spatiotemporal features from each component. Finally, an SDQN agent is introduced to perform real-time error compensation. Validation based on operational data from a PV plant in Ningxia, China, demonstrates that the proposed framework achieves RMSE, MAE, MAPE, and R2 values of 0.4463, 0.1256, 1.2814%, and 92.58%, respectively, significantly outperforming benchmark models. Specifically, the CEEMDAN decomposition effectively mitigates mode mixing. The CNN-BiLSTM as the base predictor reduces RMSE by 25.04–65.68% compared to mainstream models. Furthermore, the SDQN compensation mechanism delivers an additional 24.5% reduction in prediction error. The proposed approach thus constitutes a high-precision, adaptive solution for PV power forecasting.