A hybrid TimeGAN-xLSTM-Transformer framework for photovoltaic power forecasting under complex environmental conditions.

The rapid development of photovoltaic (PV) energy and its growing penetration in power systems have made accurate and robust PV power forecasting a critical challenge. However, the strong stochasticity, nonlinearity, and heterogeneity of PV output, driven by complex environmental conditions, hinder the performance of conventiona l forecasting methods. To address these issues, this study proposes a novel hybrid framework that integrates TimeGAN-based data augmentation, extended LSTM (xLSTM), and Transformer networks for probabilistic and accurate PV power prediction. First, TimeGAN is employed to synthesize realistic PV time series data, effectively capturing temporal correlations while preserving the irradiance-temperature dependency, thus mitigating the limitations of scarce or imbalanced historical datasets. Second, a hybrid xLSTM-Transformer architecture is developed, where the matrix memory-enhanced xLSTM module focuses on local feature extraction, and the Transformer module models long-range dependencies via self-attention mechanisms. Finally, the proposed model is validated using real-world operation data from the State Grid of China. Experimental results demonstrate that the proposed framework significantly improves prediction accuracy under realistic operating conditions. Compared with conventional LSTM and Transformer baselines, the proposed TimeGAN-xLSTM-Transformer model achieves a reduction of approximately 48.1% in RMSE and 44.1% in MAE, highlighting its superior capability in capturing both short-term fluctuations and long-term temporal dependencies of photovoltaic power generation. This research contributes to advancing intelligent PV forecasting technologies by leveraging data generation, temporal modeling, and attention mechanisms, offering theoretical and practical support for the reliable integration of PV in renewable-dominated power systems.