| 引用本文: | 冯 涛,艾学轶,韦善阳,等.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(07):69-79. |
| FENG Tao,AI Xueyi,WEI Shanyang,et al.Short-term wind speed interval prediction based on a temporal pyramid dual-layer ensemble learning architecture[J].Power System Protection and Control,2026,54(07):69-79 |
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| 摘要: |
| 风速区间值预测通过捕捉实际风速波动范围，能够有效反映风速的随机性和不确定性。然而，由于风速区间值序列的多尺度波动特性，单一预测模型往往难以全面表征其复杂波动趋势，预测性能受限。基于此，提出一种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方法。该方法主要包括数据预处理优化、多模型融合集成预测机制构建、集成输出优化三部分。在数据预处理优化中，采用红嘴蓝鹊(red-billed blue magpie optimizer, RBMO)算法优化变分模态分解参数，实现信号在频域上的有效分离。在多模型融合集成预测机制中，结合时序金字塔注意力机制融合多种模型优势，对子序列的多尺度特征模式进行差异化建模。集成输出优化则利用灰狼优化(grey wolf optimizer, GWO)算法优化子序列预测结果的输出权重，以捕捉各子序列与真实风速之间的不同特征相关性。算例分析表明：所提预测方法能够显著提升短期风速预测精度，具有较强的泛化能力和稳定性。 |
| 关键词: 区间值预测 集成学习 Pyraformer 金字塔注意力机制 |
| DOI：10.19783/j.cnki.pspc.250781 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(52407103) |
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| Short-term wind speed interval prediction based on a temporal pyramid dual-layer ensemble learning architecture |
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FENG Tao1, AI Xueyi1, WEI Shanyang2, GAN Wei3, AI Xiaomeng2
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1. School of Management, Wuhan University of Science and Technology, Wuhan 430065, China; 2. State Key Laboratory of Advanced Electromagnetic Technology (School of Electrical and Electronic Engineering, Huazhong University of Scienceand Technology), Wuhan 430074, China; 3. School of Engineering, Cardiff University, Cardiff CF24 3AA, Wales, UK
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| 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. |
| Key words: interval prediction ensemble learning Pyraformer pyramidal attention mechanism |
