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基于多头概率稀疏自注意力模型的综合能源系统多元负荷短期预测OACSTPCD

Short-term Forecasting of Multienergy Loads of Integrated Energy System Based on Multihead Probabilistic Sparse Self-attention Model

中文摘要英文摘要

精准的多元负荷短期预测是综合能源系统调度和运行的基础.综合能源系统中的多种负荷之间存在较强的耦合作用,目前已有的单一负荷预测难以挖掘不同负荷之间复杂的内在联系.对此,提出一种基于多头概率稀疏自注意力模型的多元负荷短期预测方法.首先,采用皮尔逊相关系数分析多元负荷之间的相关性,并提取多元负荷之间的耦合特征;然后,使用改进位置编码的多头概率稀疏自注意力机制学习长序列输入的依赖关系,并且采用多元预测任务的参数软共享机制,通过不同子任务对共享特征的差异化选择,实现多元负荷的联合预测;最后,在亚利桑那州立大学Tempe校区的多元负荷数据集上对所提模型的性能进行验证,结果表明所提预测方法相较于其他预测模型能够有效提高预测精度.

Accurate short-term forecasting of multienergy loads is the basis for the dispatch and operation of integrated energy systems.There is a strong coupling between multiple loads in an integrated energy system,and the existing single load forecasting is challenging to explore the complex internal relationship between multiple loads.Therefore,a short-term forecasting method for multienergy loads based on a multihead probabilistic sparse self-attention(MPSS)model was proposed.First,the Pearson correlation coefficient was used to analyze the correlation between multiple loads,the coupling features between multiple loads were extracted,a multihead probabilistic sparse self-attention mechanism with improved location coding was used to learn the dependencies of long-sequence inputs,and the parameter soft sharing mechanism of multivariate prediction tasks was adopted.The sharing mechanism realizes the joint prediction of multiple loads through a differentiated selection of shared features using different subtasks.Finally,the performance of the proposed model was verified using the multiple-load dataset of the Tempe Campus of Arizona State University.Compared with other forecasting models,the results show that the proposed multivariate load forecasting method can effectively improve forecasting accuracy.

韩宝慧;陆玲霞;包哲静;于淼

浙江大学电气工程学院,杭州市 310027

动力与电气工程

综合能源系统多元负荷预测多头概率稀疏自注意力模型位置编码

integrated energy systemmultienergy load forecastingmultihead probabilistic sparse self-attention modellocation coding

《电力建设》 2024 (002)

127-136 / 10

This work is supported by Zhejiang Provincial Natural Science Foundation of China(No.LGG22F030008)and the Key Research and Development Program of Zhejiang Province(No.2021C01113).浙江省基础公益研究计划项目(LGG22F030008);浙江省重点研发计划项目(2021C01113)

10.12204/j.issn.1000-7229.2024.02.011

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