Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model
Quoc Bao Phan, Tuy Tan Nguyen
Abstract
This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance accuracy. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the model by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.