Long-Term Wind Power Forecasting Using Variational Mode Decomposition and Convolutional Neural Netwrok

This research paper presents a two-step wind power forecasting in wind turbine applications. The proposed approach incorporates Variational Mode Decomposition (VMD) as a feature extraction method, followed by Convolutional Neural Network (CNN) model. The effectiveness of this method is evaluated using real wind power data, results demonstrate the accuracy and reliability of the proposed technique. Specifically, the VMD-CNN model trained with a 90% training and 10% testing split achieves the highest accuracy, yielding an RMSE value of 0.1307. The comparative analysis with previous architectures that do not employ decomposition reveals the superior performance of the proposed method. Moreover, it exhibits promising potential for long-term wind power forecasting, outperforming recently proposed methods.