Ultra-Short-Term Wind Power Forecasting Using a Two-Stage Signal Decomposition and iTransformer-LSTM-KAN Hybrid Framework
TL;DR
An ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN that achieves clear improvements in forecasting accuracy and fitting capability is proposed.
Abstract
Accurate ultra-short-term wind power forecasting is of great significance for grid integration scheduling and the secure operation of power systems. However, due to meteorological disturbances and turbine operating states, wind power series generally exhibit non-stationary, multi-scale fluctuations and strong nonlinearity. To improve forecasting accuracy, this paper proposes an ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN. First, a cascaded decomposition module is constructed using the wavelet transform (WT) and ICEEMDAN to attenuate the non-stationarity of the original power series and to extract multi-scale features. An iTransformer branch is then employed to model global dependencies among multiple variables, while an LSTM branch captures temporal dynamics in the historical power series. Subsequently, a cross-attention mechanism is introduced to achieve cross-branch feature fusion, and a KAN output layer is adopted to enhance the model’s representation of the wind speed–power nonlinear mapping. A particle swarm optimization (PSO) algorithm, combined with a cosine annealing strategy, is used to optimize key hyperparameters and improve training stability. Experimental results using SCADA data from a 150 MW wind farm in southern Hunan Province show that the proposed model achieves an MAE of 9.8327 MW, an RMSE of 13.1872 MW, an SMAPE of 18.8474%, and an R2 of 0.7798. These values correspond to the fixed main comparison protocol used for baseline evaluation, while the ablation study reports multi-seed mean and standard deviation results to assess module-level robustness. Compared with LSTM and WT-ICEEMDAN-CNN-LSTM, the proposed model achieves clear improvements in forecasting accuracy and fitting capability. Additional cross-wind-farm validation on a second wind farm shows that WT-ICEEMDAN-iTransformer-LSTM-KAN-PSO (hereafter referred to as ILKP) maintains the best overall performance, achieving an MAE of 27.2193 MW, an RMSE of 36.1862 MW, an SMAPE of 27.8429%, and an R2 of 0.5189, demonstrating transferability and robustness under different operating conditions.