电力建设2024，Vol.45Issue(2)：127-136,10.DOI:10.12204/j.issn.1000-7229.2024.02.011
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
Short-term Forecasting of Multienergy Loads of Integrated Energy System Based on Multihead Probabilistic Sparse Self-attention Model
摘要
Abstract
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.关键词
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Key words
integrated energy system/multienergy load forecasting/multihead probabilistic sparse self-attention model/location coding分类
信息技术与安全科学引用本文复制引用
韩宝慧,陆玲霞,包哲静,于淼..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[J].电力建设,2024,45(2):127-136,10.基金项目
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) （No.LGG22F030008）
浙江省重点研发计划项目(2021C01113) （2021C01113）
