Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2$R^2$ values up to 9.5% higher than those obtained using standard LSTM models. This study proposes a new wind power forecasting model based on a deep learning model, the long‐short term memory neural network, enhanced by multiple signal decomposition techniques. While the empirical mode decomposition initially generates more stable, stationary, and regular patterns of the original signal, the variational mode decomposition technique is further applied to the highest frequency component to overcome the most challenging part. As a result, the forecasting accuracy is improved.