Enhancing Wind Speed Forecasting Accuracy Using a GWO-Nested CEEMDAN-CNN-BiLSTM Model
In the past few years, accurate wind speed forecasting has become progressively important due to the growing demand for renewable energy sources. In this study, we propose a novel 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. The proposed model integrates three powerful techniques: CEEMDAN, CNN, and BiLSTM. Firstly, CEEMDAN with two nested layers is used to decompose the wind speed time series into a set of intrinsic mode functions (IMFs) to enhance the accuracy of the model. CNN is then applied to extract features from the IMFs, and BiLSTM is used to capture the temporal dependencies and make accurate predictions. To improve the performance of the model further, we also introduce GWO and apply it to select the best hyperparameters for the forecasting models based on decomposition results. The model is tested using wind speed data collected in Basel in 2022. The final combination result demonstrates that the proposed model achieve a better performance compared to several modern AI-base models in the wind speed forecasting field, with high accuracy reflected by a mean absolute percentage error (MAPE) of nearly 3%.
Email Address of Submitting Authornguyentantuy@gmail.com
ORCID of Submitting Author0000-0002-9485-7720
Submitting Author's InstitutionNorthern Arizona University
Submitting Author's Country
- United States of America