A Hybrid Framework for Offshore Wind Power Forecasting: Integration of Adaptive Decomposition and Collaborative Temporal-Channel Modeling

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power.