电力建设2026，Vol.47Issue(3)：51-63,13.DOI:10.12204/j.issn.1000-7229.2026.03.005
基于优化模态分解与DGRUK的综合能源系统负荷预测
Load Forecasting for Integrated Energy System Based on Optimized Modal DGRUK Analysis
摘要
Abstract
[Objective]To further explore the potential structure of load sequence data in integrated energy systems(IES)and enhance the overall prediction accuracy and reliability of IES load forecasting models,this paper proposes a novel load forecasting method for IES based on optimized modal decomposition and the DGRUK network.[Methods]Firstly,for the multi-energy load sequence decomposition stage,an improved ivy algorithm is employed to optimize the parameters of the improved complete ensemble empirical mode decomposition.Decomposes cooling,heating,electricity,and other multi-energy load sequences into intrinsic mode function components,thereby reducing the non-stationarity and complex coupling of the original sequences.Secondly,during the feature extraction phase,the discrete cosine transform is integrated into the channel attention mechanism to efficiently capture global correlations among different channels and enhance the representation of key features.Finally,a DGRUK network is constructed by leveraging the advantages of Kolmogorov-Arnold networks in nonlinear mapping.This step compensates for the limitations of traditional fully connected layers in handling complex nonlinear relationships,thereby improving the model's capability to process high-dimensional,non-stationary load data.[Results]The proposed method achieves mean absolute percentage errors(MAPE)of 2.045%,2.379%,and 1.234%for cooling,heating,and electrical load forecasting,respectively.All error metrics are lower than those of other commonly used methods,verifying the effectiveness of the proposed approach.[Conclusions]The proposed method effectively addresses the issues of non-stationarity and complex coupling in multi-energy load sequences of integrated energy systems.It provides scientific support for the optimal scheduling and operational management of integrated energy systems.关键词
综合能源系统/负荷预测/模态分解/神经网络/注意力机制Key words
integrated energy system/load forecasting/modal decomposition/neural network/attention mechanism分类
信息技术与安全科学引用本文复制引用
司伟壮,吐松江·卡日,郭志明,张紫薇,孙天智..基于优化模态分解与DGRUK的综合能源系统负荷预测[J].电力建设,2026,47(3):51-63,13.基金项目
国家自然科学基金项目(52067021,52207165) （52067021,52207165）
新疆维吾尔自治区自然科学基金面上项目(2022D01C35) This work is supported by National Natural Science Foundation of China(No.52067021,No.52207165)and General Program of Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01C35). （2022D01C35）
