基于数字孪生与多模型融合的多元负荷短期预测  

SHORT-TERM FORECASTING OF MULTIVARIATE LOAD BASED ON DIGITAL TWIN AND MULTI-MODEL FUSION Feng

作　　者：冯佳威 王海鑫[1] 杨子豪 陈哲[2] 李云路 杨俊友[1] Jiawei;Wang Haixin;Yang Zihao;Chen Zhe;Li Yunlu;Yang Junyou(College of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China;Department of Energy Technology,Aalborg University,Aalborg DK-9220,Denmark)

机构地区：[1]沈阳工业大学电气工程学院,沈阳110870 [2]丹麦奥尔堡大学能源技术系,奥尔堡DK-9220

出　　处：《太阳能学报》2024年第10期97-106,共10页Acta Energiae Solaris Sinica

基　　金：国家重点研发计划(2017YFB0902100);高等学校学科创新引智计划(D23005)。

摘　　要：针对多元负荷呈波动性和非线性特性导致预测模型稳定性差和精确度低等问题,提出一种基于数字孪生与多模型融合的多元负荷短期预测方法。首先,根据数字孪生体中气象和负荷信息,利用最大信息系数(MIC)分析多源数据信息间的耦合特性,基于数据时序性和周期性构建筛选信息特征。其次,采用自适应局部迭代滤波(ALIF)将历史多元负荷数据进行分解,得到不同频率下固有模态函数(IMF)分量。然后,采用核极限学习机(KELM)和双向长短期记忆网络(BiLSTM)预测高频和低频负荷分量,融合重构得到初始负荷短期预测结果。最后,利用数字孪生体补偿初始预测结果,得到最终负荷预测结果。仿真结果表明,与单预测模型及未基于数字孪生预测模型相比,所提方法具有更好的稳定性,能有效应对负荷波动变化和非线性,提升模型预测精度。Aiming at the problems of poor stability and low accuracy of the forecasting model caused by the volatility and nonlinear of multivariate load,a short-term forecasting method of multivariate load based on digital twin and multi-model fusion is proposed.Firstly,according to the meteorological and load information in the digital twin,the maximum information coefficient(MIC)is used to analyze the coupling characteristics between multi-source data information.Information features are constructed and filtered based on data temporality and periodicity.Secondly,adaptive local iterative filtering(ALIF)is used to decompose the historical multivariate load data to obtain intrinsic mode function(IMF)at different frequencies.Then,kernel extreme learning machine(KELM)and bi-directional long short-term memory network(BiLSTM)are used to forecast high-frequency and low-frequency load components.The short-term forecasting results of the initial load are obtained by fusing and reconstructing high-frequency and low-frequency components.Finally,the final load forecasting results are obtained by compensating the initial forecasting results with the digital twin.Compared with the single forecasting model and the non-digital twin forecasting model,the proposed method can effectively deal with the fluctuation and nonlinearity of multivariate load,and has better stability and accuracy.

关 键 词：数字孪生 负荷预测 自适应滤波 新型电力系统 核极限学习机 双向长短期记忆网络 

分 类 号：TM721[电气工程—电力系统及自动化]