Short-Term Wind Power Forecasting Based on Improved Modal Decomposition and Deep Learning

With the continued growth in wind power installed capacity and electricity generation, accurate wind power forecasting has become increasingly critical for power system stability and economic operations. Currently, short-term wind power forecasting often employs deep learning models following modal decomposition of wind power time series. However, the optimal length of the time series used for decomposition remains unclear. To address this issue, this paper proposes a short-term wind power forecasting method that integrates improved modal decomposition with deep learning techniques. First, the historical wind power series is segmented using the Pruned Exact Linear Time (PELT) method. Next, the segmented series is decomposed using an enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to extract multiple modal components. High-frequency oscillatory components are then further decomposed using Variational Mode Decomposition (VMD), and the resulting modes are clustered using the K-means algorithm. The reconstructed components are subsequently input into a Long Short-Term Memory (LSTM) network for prediction, and the final forecast is obtained by aggregating the outputs of the individual modes. The proposed method is validated using historical wind power data from a wind farm. Experimental results demonstrate that this approach enhances forecasting accuracy, supports grid power balance, and increases the economic benefits for wind farm operators in electricity markets.