标题:Combination load forecasting method for CCHP system based on IOWA operator
作者:Sun, Yunxin ;Li, Ke ;Yan, Yi ;Wei, Xinguo ;Zhang, Chenghui
作者机构:[Sun, Yunxin ;Li, Ke ;Yan, Yi ;Wei, Xinguo ;Zhang, Chenghui ] School of Control Science and Engineering, Shandong University, Jinan, China
会议名称:2017 Chinese Automation Congress, CAC 2017
会议日期:20 October 2017 through 22 October 2017
来源:Proceedings - 2017 Chinese Automation Congress, CAC 2017
出版年:2017
卷:2017-January
页码:4193-4197
DOI:10.1109/CAC.2017.8243515
关键词:combination; combined cooling heating and power; gray relational; IOWA operator; load forecasting
摘要:Load forecasting is the basis of the design and implementation of the control strategy of the combined cooling heating and power (CCHP) system, and the precision affects the comprehensive energy efficiency of the system directly. In this paper, the gray relational analysis method is used to indicate the strong coupling relationship among the loads of heating, cooling and electricity in the system. Furthermore, a load forecasting method with the least squares support vector regression (LS-SVR) prediction and the radial basis function neural network (RBF Neural Network) prediction combined based on induced ordered weighted averaging (IOWA) operator is proposed by establishing the optimal model based on the minimum sum of error squares. The simulation results based on the historical load data of a CCHP system show that the accuracy of the multivariate combination forecasting method proposed in this paper is higher than that of single variable prediction method and the single prediction method, and the feasibility and effectiveness of the combination load forecasting method based on IOWA operator are verified. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050450717&doi=10.1109%2fCAC.2017.8243515&partnerID=40&md5=0e74e08e092424d3f0d0f8c650b09cec
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