Short-Term Wind Power Forecasting Using an HOA–VMD–CNN–iTransformer–LSTM Deep Learning Framework

Reliable short-term wind power forecasts are difficult to obtain because power records are strongly non-stationary and hybrid networks are highly sensitive to parameter choices. This study proposes a Pearson–HOA–VMD–CNN–iTransformer–LSTM framework to address these challenges. Pearson coefficients are used to screen the candidate meteorological inputs. The hiking optimization algorithm (HOA) subsequently searches the mode number and penalty parameter of variational mode decomposition (VMD), together with the principal hyperparameters of the forecasting network. The optimized decomposition reduces sequence complexity, while a convolutional neural network (CNN) extracts local fluctuation patterns. iTransformer captures global variable dependencies using variate-wise self-attention, and long short-term memory (LSTM) models learn long-term temporal information. The framework was evaluated using four datasets containing 2976, 2880, 2976, and 2976 records, respectively, drawn from 2020 operational data from two Chinese wind farms with installed capacities of 99 and 200 MW. The sampling interval was 15 min, and the forecast horizons were 15, 30, and 45 min. Tests on four datasets show consistent gains over CNN–iTransformer–LSTM. Averaged across the datasets at the 15-min forecast horizon, RMSE, MAE, and MAPE decrease by 67.48%, 71.17%, and 49.66%, respectively, and mean R² rises from 0.9672 to 0.9974.