基于双频编码器-解码器架构的超短期多步风速预测模型OA

[Objective]The high-precision ultra-short-term wind speed prediction technology is investigated to address the key bottleneck constraining the efficient utilization of wind power:the challenge of improving prediction accuracy caused by the non-stationary nature of wind speed and the inherent limitations of deep learning models.[Methods]Taking two wind farms in Jiangxi as the research objects,a dual-frequency encoder-decoder model based on an attention mechanism and long short-term memory(LSTM)was established.The model applied convolution theory to perform multi-scale decomposition of wind speed sequences,effectively mitigating the impact of non-stationarity.A novel frequency-time attention mechanism was proposed to adaptively capture high-frequency fluctuation characteristics through the interaction of features in the frequency and time domains.A two-layer LSTM encoder-decoder architecture was adopted to effectively extract low-frequency trend features through hierarchical nonlinear mapping and suppress error accumulation in multistep predictions.[Results]Comparative experiments using data from the two wind farms showed that the proposed model demonstrated excellent performance in 15-step,30-step,and 60-step predictions.For 30-step prediction,the mean absolute error(MAE),mean squared error(MSE),and root mean squared error(RMSE)were 0.340 1,0.281 6,and 0.530 6,respectively.For 60-step prediction,the MAE,MSE,and RMSE were 0.471 3,0.504 7,and 0.710 2,respectively.This demonstrated that the proposed model showed a significant improvement in prediction accuracy compared to baseline models,along with outstanding generalization ability.[Conclusion]By leveraging collaborative modeling in the frequency and time domains and hierarchical feature extraction,the proposed model effectively addresses the non-stationarity of wind speed and the limitations in model accuracy.It demonstrates high precision and excellent generalization in multistep prediction,providing a reliable technical solution for ultra-short-term wind speed prediction in wind power generation scenarios.