Short-Term Photovoltaic Power Forecasting Using BiGRU-MDSA Based on Clustering and Hybrid Feature Extraction
This study addresses the inherent volatility and uncertainty in photovoltaic (PV) power generation by proposing a short-term forecasting model that integrates fuzzy C-means (FCM) clustering, hybrid scale feature extraction (HSFE), and a multi-head dynamic sparse attention (MDSA) mechanism within a bidirectional gated recurrent unit (BiGRU) framework. The methodology begins with preprocessing historical PV data, including outlier removal via boxplot analysis and feature selection using Pearson correlation coefficients. Principal component analysis (PCA) reduces dimensionality, followed by FCM clustering to classify weather patterns. The clustered data feeds into a BiGRU model augmented with HSFE to capture multi-scale temporal dependencies, while MDSA dynamically adjusts focus on critical time steps. Comparative simulations demonstrate that the proposed FCM-BiGRU-HSFE-MDSA model achieves superior accuracy and generalization across diverse climatic conditions. The integration of HSFE and MDSA mitigates noise, enhances robustness, and reduces overfitting, offering a reliable tool for intelligent dispatch in PV systems. The model's performance validates its effectiveness for short-term PV power forecasting, providing a novel pathway for improving operational scheduling in renewable energy grids.