综合智慧能源2026，Vol.48Issue(1)：59-66,8.DOI:10.3969/j.issn.2097-0706.2026.01.006
基于双重特征处理的园区综合能源系统供热负荷预测研究
Research on heat load prediction of integrated energy systems in parks based on dual feature processing
摘要
Abstract
The heat load of integrated energy systems in parks is affected by multi-energy flows,and existing prediction models have insufficient feature extraction capabilities.To address this,a dual feature processing model for heat load prediction was proposed,integrating improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and multivariate phase space reconstruction.The heat load time series was decomposed using the ICEEMDAN method,and the components were reconstructed by calculating their sample entropy.These were then combined with input features such as air temperature to form multivariate time series datasets at different frequencies.The optimal delay time and embedding dimension of the series were determined using the C-C method,thereby obtaining the high-dimensional phase space of each dataset.The heat load components were predicted using a bidirectional long short-term memory neural network model with optimized parameters.The final heat load prediction value was obtained by summing the prediction results.The case study results showed that the proposed method achieved good prediction performance compared to other models.关键词
模态分解/多变量相空间重构/热负荷预测/双向长短时记忆神经网络/园区综合能源系统Key words
mode decomposition/multivariate phase space reconstruction/heat load prediction/bidirectional long short-term memory neural network/integrated energy systems in parks分类
能源科技引用本文复制引用
薛东,徐静静,江婷,王晓海,徐聪..基于双重特征处理的园区综合能源系统供热负荷预测研究[J].综合智慧能源,2026,48(1):59-66,8.基金项目
新疆维吾尔自治区重大科技专项(2022A1001-3)Major Science and Technology Projects of Xinjiang Uygur Autonomous Region(2022A1001-3) （2022A1001-3）
