TBiLSTM Hybrid Network for Photovoltaic Power Forecasting Based on Multi-Temporal Feature Fusion
Photovoltaic (PV) power forecasting is fundamentally constrained by the stochastic volatility of irradiance, temperature, and atmospheric pressure, which degrades the accuracy of conventional statistical and single-branch recurrent architectures. This study proposes a TBiLSTM hybrid network that fuses multi-temporal features through a Transformer encoder and a bidirectional long short-term memory (BiLSTM) network. Input meteorological sequences—irradiance, humidity, temperature, and pressure—are min-max normalized to [0,1] and augmented with learnable positional encodings to preserve temporal ordering. The Transformer encoder applies multi-head self-attention to capture global cross-time-step dependencies, with residual connections and layer normalization stabilizing gradient propagation. The encoded representation is then passed to a BiLSTM, which extracts forward and backward local temporal dynamics; the final hidden state is mapped to the power sequence via a fully connected layer. Pearson correlation analysis on 3002 samples identifies irradiance (r = 0.95), humidity (r = −0.53), temperature (r = 0.52), and pressure (r = 0.25) as the dominant features, while wind speed (r = 0.04) and wind direction (r = 0.03) are excluded. On two public datasets, the proposed model reduces MAE by 37.16%, 47.63%, and 12.43% versus BiLSTM, CNN, and CNN-BiLSTM, respectively; MAPE by 55.59%, 68.80%, and 8.79%; MSE by 52.11%, 68.47%, and 20.98%; and RMSE by 33.75%, 45.49%, and 11.13%, while improving R² by 4.03%, 4.03%, and 2.13%. These results confirm the model's reliability and superiority for grid dispatch and PV plant operation.