A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

Abstract

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.