Spatial load forecasting method based on rank set pair analysis

被引：0
|
作者：
Xiao B. [1 ]
Zhang J. [1 ]
Jiang Z. [2 ]
Shi Y. [3 ]
Jiao M. [4 ]
Wang Y. [4 ]
机构：
[1] School of Electrical Engineering, Northeast Electric Power University, Jilin
[2] School of Computer Science and Technology, Beihua University, Jilin
[3] Tonghua Power Supply Company of State Grid Jilin Electric Power Company Co., Ltd., Tonghua
[4] Changchun Power Supply Company of State Grid Jilin Electric Power Company Co., Ltd., Changchun
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2020年 / 40卷 / 04期
关键词：
Cell; Classified load density; GIS; Rank set pair analysis; SLF;
D O I：
10.16081/j.epae.202002033
中图分类号：
学科分类号：
摘要：
According to the advantages of R-SPA(Rank Set Pair Analysis) theory in dealing with uncertainties of system, a novel SLF(Spatial Load Forecasting) method is proposed. Firstly, Class Ⅰ cells are generated according to the power supply range of each 10 kV feeder in the area to be forecasted in the power GIS(Geographic Information System), and several historical load sets and one target data set are generated from the historical load data of Class Ⅰ cells according to different set capacities. Secondly, the correspon-ding rank sets are obtained by rank transformation of the historical data sets, which are combined with the target data to form the set pairs. Then, the historical data set that is similar to the target data set is searched, and the forecasting values of set capacity with minimum relative errors are taken as the load fores-ting values of Class Ⅰ cells. Finally, Class Ⅱ cells are generated by equal size grid, and their load forecasting values are solved according to the load forecasting values of Class Ⅰ cells and land use information, thus the spatial load forecasting results after gridding are obtained. The practicability and effectiveness of the proposed method are verified by an engineering example. © 2020, Electric Power Automation Equipment Press. All right reserved.
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页码：153 / 158
页数：5
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