基于时序金字塔双层集成学习架构的短期风速区间值预测OA

Wind speed interval prediction captures the fluctuation range of actual wind speed,effectively reflecting its randomness and uncertainty.However,due to the multi-scale fluctuation characteristics of wind speed interval sequences,a single prediction model often fails to fully represent their complex dynamics,leading to limited prediction performance.To address this issue,this paper proposes a short-term wind speed interval prediction method based on a temporal pyramid dual-layer ensemble learning architecture.The method mainly comprises three parts:data preprocessing optimization,multi-model ensemble prediction mechanism construction,and ensemble output optimization.The data preprocessing optimization employs the red-billed blue magpie optimizer to optimize the parameters of variational mode decomposition,enabling effective signal decomposition in the frequency domain.In the multi-model ensemble prediction stage,a temporal pyramid attention mechanism is introduced to integrate the advantages of multiple models and perform differentiated modeling of multi-scale feature patterns in sub-sequences.In the ensemble output optimization stage,the grey wolf optimizer is used to optimize the output weights of sub-sequence predictions,capturing the varying feature correlations between each sub-sequence and actual wind speed.Case studies demonstrate that the proposed method can significantly improve the accuracy of short-term wind speed prediction,and has strong generalization ability and stability.