Ultra-short-term multistep wind speed prediction model based on dual frequency encoder-decoder

Abstract

[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.

Keywords

deep learning / Transformer / frequency-time attention mechanism / two-layer long short-term memory units / encoder-decoder / multistep prediction / climate change / influencing factors