TWZA-MVTFSF: A novel short-term photovoltaic power forecasting model

Photovoltaic (PV) power generation is affected by meteorological and geographical factors, displaying volatility, nonlinearity, and characteristics across multiple timescales. Accurate short-term forecasting of PV power is essential for enhancing PV resource utilization in the power grid and maintaining the quality of renewable energy integration. This paper introduces a Multi-Variable Time-Frequency Synergistic Fusion network model, enhanced by the Triangular Wave Zebra Algorithm (TWZA), for short-term PV power forecasting. To tackle the challenges of disjointed time-frequency feature fusion and limited generalization in complex fluctuation scenarios, a dual-parallel branch structure is developed to synergistically model temporal and spectral features in both time and frequency domains. In the time-domain branch, a kLSTM encoder is employed, which integrates block embedding and a Multi-Variable Correlation Attention mechanism to uniformly model variable coupling relationships and long-term dependencies. The frequency-domain branch incorporates the frequency decomposition architecture of the Multi-order Kolmogorov–Arnold Network (KAN), decomposing the power signal into multi-scale frequency components. By combining multi-order KAN representation learning with deep separable convolutions, it extracts nonlinear spectral features. A cross-domain attention-based time-frequency fusion mechanism is developed for adaptive and collaborative integration of time-domain and frequency-domain features. Second, to address the inability of time-frequency models to adaptively tune hyperparameters, we introduce TWZA to adaptively optimize time-frequency fusion weights, network hyperparameters, and convolution kernel sizes. This enhances the model's global search and local exploration capabilities while reducing training loss. Extensive experimental validation demonstrates that the proposed model outperforms all baseline models in terms of R2, root mean square error, and mean absolute error, achieving improvements of at least 18.2%, 27.3%, and 14.9%, respectively.