Short-term Load Forecasting of an Integrated Energy System Based on STL-CPLE with Multitask Learning  

作　　者：Suxun Zhu Hengrui Ma Laijun Chen Bo Wang Hongxia Wang Xiaozhu Li Wenzhong Gao 

机构地区：[1]the College of Energy and Electrical En-gineering,Qinghai University,Xining 810016,China [2]the School of Electrical and Automation,Wuhan University,Wuhan 430072,China [3]the Department of Electrical and Computer Engineering,University of Denver,Denver CO 80208,USA [4]Engineering Research Center for Renewa-ble Energy Generation and Grid Connection Technology,Ministry of Education,Xinjiang University,Urumchi 830046,China

出　　处：《Protection and Control of Modern Power Systems》2024年第6期71-92,共22页现代电力系统保护与控制(英文)

基　　金：supported by the National Natural Sci-ence Foundation of China Joint Fund Program(No.U22A20224).

摘　　要：Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among loads.In line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called STL-CPLE.First,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual components.Then,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature information.This approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.

关 键 词：Integrated energy system multienergy load forecasting convolutional progressive layer extrac-tion network seasonal-trend decomposition. 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]