Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System  

作　　者：Leijiao Ge Yuanliang Li Jan Yan Yuanliang Li Jiaan Zhang Xiaohui Li 

机构地区：[1]IEEE [2]School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China [3]Concordia Institute for Information Systems Engineering,Concordia University,Montreal,QC H3G 1M8,Canada [4]School of Electrical and Engineering,Hebei University of Technology,Tianjin 300401,China [5]Marketing Service Center,State Grid Tianjin Electric Power Company,Tianjin 300302,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2023年第5期1462-1479,共18页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by Science and Technology Project of the Headquarters of State Grid Corporation of China (No. 5100-202155018A-0-0-00);the National Natural Science Foundation of China (No. 51807134);the State Key Laboratory of Power System and Generation Equipment (No. SKLD21KM10);the Natural Science and Engineering Research Council of Canada (NSERC)(No. RGPIN-2018-06724)。

摘　　要：To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

关 键 词：Collaborative atomic chaotic search(CACS) multivariate two-stage adaptive-stacking prediction(M2ASP)framework prediction error correction regional integrated energy system(RIES) 

分 类 号：TM73[电气工程—电力系统及自动化] TK01[动力工程及工程热物理]