Hierarchical Decomposition and Adaptive Optimization for CrossLinear-Based Wind–Photovoltaic Power Forecasting

Accurate short-term wind power forecasting is essential for ensuring power system stability and facilitating the large-scale integration of renewable energy. However, existing forecasting models often suffer from complex architectures that hinder practical deployment, insufficient modeling of multivariate dependencies, and limited capability in capturing multi-scale nonlinear temporal patterns. To address these challenges, this study proposes a novel PGA-HDD-Crosslinear framework for short-term wind and photovoltaic power forecasting. Specifically, the Crosslinear architecture is employed to efficiently capture multivariate dependencies with low computational complexity. A Hierarchical Dual Decomposition (HDD) strategy is further introduced, where STL decomposition is first applied to separate the input sequence into trend, seasonal, and residual components, followed by a multi-order Kolmogorov–Arnold Network (KAN) to extract latent nonlinear features from residual signals. In addition, the Plant Growth Algorithm (PGA) is utilized to optimize key hyperparameters and enhance model generalization. Experimental results on real-world wind and photovoltaic datasets demonstrate the effectiveness of the proposed framework. Compared with the baseline Crosslinear model, RMSE, MAE, and MAPE are reduced by 15.9%, 17.4%, and 18.2% on the Xinjiang photovoltaic dataset, respectively, while reductions of 16.0%, 18.9%, and 15.8% are achieved in wind power forecasting tasks.