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基于二次分解因果分析和深度学习的短期风电功率预测 被引量:1

Short-term Wind Power Prediction Based on Twice Decomposition Causal Analysis and Deep Learning
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摘要 为实现精准的风电功率预测,提出了一种基于二次分解因果分析和深度学习的风电功率预测模型。首先,通过完备集成经验模态分解算法对风电功率和风速序列进行一次分解,并采用经验小波变换算法对风电功率和风速序列的高频分量进行二次分解,以降低原始序列的复杂程度。其次,通过Granger因果关系检验方法对各风速分量与风电功率分量进行因果分析,以此实现风电功率各分量的输入变量选择。最后,利用耦合注意力机制的双向门控循环单元对风电功率分量进行预测,并集成得到最终的风电功率预测结果。通过风电厂实际运行数据进行试验,并与多个典型模型进行比较,结果表明所提模型具有较高的预测精度,其决定系数达到了0.98,能够实现较精准的风电功率预测。 Accurate wind power prediction is instrumental in effectively reducing the fluctuations induced by wind power uncertainty.To achieve precise wind power prediction,a wind power prediction model based on twice decomposition causal analysis and deep learning was proposed.Firstly,the wind power and wind speed series undergo a single decomposition using the complete integrated empirical mode decomposition algorithm.Subsequently,the high-frequency components of both the wind power and wind speed series are decomposed twice by the empirical wavelet transform algorithm,thereby reducing the complexity of the original sequence.Secondly,the Granger causality test is employed to analyze the causality between wind speed components and wind power components.This analysis aids in selecting input variables for each wind power component.Finally,the bidirectional gated cyclic unit with a coupled attention mechanism is utilized to predict the wind power component,and the final wind power predictions are integrated.The results demonstrate that the proposed model exhibits a high level of prediction accuracy,with a determination coefficient reaching 0.98,which can achieve more accurate wind power predictions.
作者 梅晓辉 李国翊 李铁良 关猛 MEI Xiaohui;LI Guoyi;LI Tieliang;GUAN Meng(State Grid Hengshui Electric Power Supply Company,Hengshui 053000,China;Beiing Paike Shenghong Electronic Technology Co.Ltd.,Beijing 100070.China)
出处 《河北电力技术》 2024年第1期77-83,共7页 Hebei Electric Power
基金 国网河北省电力有限公司科技项目(kj2022-008)。
关键词 风电功率 二次分解 GRANGER因果关系检验 双向门控循环单元 注意力机制 wind power twice decomposition granger causality test bidirectional gated recurrent circulation unit attention mechanism
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