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

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1136 - 1136

Published: Feb. 25, 2025

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

Language: Английский

Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 4, 2025

Language: Английский