应用周期选择和变量交叉注意的光伏电力长时间序列预测  

Photovoltaic Power Long-sequence Time Series Forecasting via Period Selection and Variable Cross-attention

作　　者：周恒 艾青 张婧汇 ZHOU Heng;AI Qing;ZHANG Jing-Hui(College of Intelligent Systems Science and Engineering,Hubei Minzu University,Enshi 445000,China)

机构地区：[1]湖北民族大学智能科学与工程学院,恩施445000

出　　处：《计算机系统应用》2025年第4期256-265,共10页Computer Systems & Applications

摘　　要：准确的综合能源负荷预测是区域综合能源系统前期规划和后期按需协调运行的关键前提.近期基于Transformer的方法由于其优秀的全局建模能力,在长序列预测方面显示了显著潜力.然而,Transformer中的排列不变自注意力机制导致了时间信息丢失,且忽视了多能源负荷预测中不同变量之间的关键依赖关系.为解决上述挑战,本文提出了一种补丁与变量混合模型(patch and variable mixing model,PVMM)以实现准确多能源负荷预测.PVMM采用补丁嵌入技术,将输入的多能源负荷序列转换为3D向量,从而保留补丁的时间和变量信息.其次,本文提出了基于深度可分离卷积的补丁混合模块(patch mixing module,PMM)建立时间依赖关系模型.另外,本文还提出了变量动态投影注意力模块(variable dynamic projection attention module,VDP-AM)将查询(Query)和数值(Value)变量映射到更高维空间,并通过自注意力机制处理多变量之间的相互作用.最后,本方法在亚利桑那州立大学公开的在线系统数据集的预测精度和泛化能力均超越现有方法.Accurate integrated energy load forecasting is a key prerequisite for the preliminary planning and subsequent on-demand coordinated operation of regional integrated energy systems.The recent Transformer-based method has shown significant potential in long sequence forecasting for its excellent global modeling capabilities.However,the permutationally invariant self-attention mechanism in Transformer leads to the loss of temporal information and ignores the key dependencies between different variables in multi-energy load forecasting.To address the above challenges,this study proposes a patch and variable mixing model(PVMM)to achieve accurate multi-energy load forecasting.PVMM uses patch embedding technology to convert the input multi-energy load sequence into a 3D vector,thereby retaining the temporal and variable information of the patch.Secondly,this study proposes a patch mixing module(PMM)based on deep separable convolution to establish a temporal dependency model.In addition,this study also proposes a variable dynamic projection attention module(VDP-AM)to map Query and Value variables to a higher dimension and handle the interaction between multiple variables through a self-attention mechanism.Finally,the prediction accuracy and generalization ability of this method on the online system dataset publicly available at Arizona State University surpass existing methods.

关 键 词：综合能源系统 多能源负荷预测 深度可分离卷积 注意力机制 

分 类 号：TM615[电气工程—电力系统及自动化] TM614