Short-Term Wind Speed Forecasting Based on Residual Network and Multi-Channel LSTM Encoder-Decoder Network

Wind energy is a crucial renewable resource extensively utilized in wind power generation. Accurate wind speed prediction enhances equipment operation and maintenance scheduling, optimizing the use of wind energy resources. This study introduces a wind speed forecasting model that employs Residual Network (ResNet) and multi-channel Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the proposed model achieves more stable data fitting and superior prediction accuracy for short-term, multi-step wind speed forecasting tasks.