电力系统保护与控制2026，Vol.54Issue(7)：69-79,11.DOI:10.19783/j.cnki.pspc.250781
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
Short-term wind speed interval prediction based on a temporal pyramid dual-layer ensemble learning architecture
摘要
Abstract
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.关键词
区间值预测/集成学习/Pyraformer/金字塔注意力机制Key words
interval prediction/ensemble learning/Pyraformer/pyramidal attention mechanism引用本文复制引用
冯涛,艾学轶,韦善阳,甘伟,艾小猛..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[J].电力系统保护与控制,2026,54(7):69-79,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52407103). 国家自然科学基金项目资助(52407103) （No.52407103）
