文献类型：期刊文献

英文题名：DEMAND FORECASTING FOR RUSH REPAIR SPARE PARTS OF POWER EQUIPMENT USING FUZZY C-MEANS CLUSTERING AND THE FUZZY DECISION TREE

作者：Hao, Yuanyuan[1,2];Tian, Mingheng[1,2];Wang, Yanxin[3];Huang, Minfang[1,2]

第一作者：Hao, Yuanyuan

通讯作者：Wang, YX[1]

机构：[1]North China Elect Power Univ, Sch Econ & Management, 2 Beinong Rd, Beijing 102206, Peoples R China;[2]North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, 2 Beinong Rd, Beijing 102206, Peoples R China;[3]Beijing Union Univ, Business Coll, 3 Yanjing Dongli, Beijing 100025, Peoples R China

第一机构：North China Elect Power Univ, Sch Econ & Management, 2 Beinong Rd, Beijing 102206, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Business Coll, 3 Yanjing Dongli, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

年份：2023

卷号：19

期号：4

起止页码：1007-1021

外文期刊名：INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL

收录：EI(收录号：20233114468149);Scopus(收录号：2-s2.0-85166370774);WOS:【ESCI(收录号:WOS:001024592600002)】；

基金：This work was supported by the program of the National Natural Science Foundation of China (72071078). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

语种：英文

外文关键词：Rush repair spare parts; Demand forecasting; Fuzzy clustering; Fuzzy de-cision tree

摘要：Rush repair spare parts of power equipment are necessary for ensuring the normal operation of the critical equipment of power grid. Once the power equipment is damaged due to disasters and the rush repair spare parts are out of stock, the reper-cussions of the accident will be worsened, causing further harm to the power grid and potentially large losses to production and life. On the other hand, the excessive inventory of rush repair spare parts will cause a waste of cost. Therefore, accurate prediction of rush repair spare parts demand is very important. It is particularly difficult to forecast the demand for rush repair spare parts of power equipment due to the nature of their demand, which is usually highly uncertain, random, and with a small amount of historic data. Aiming to improve the scientificity and practicality, this paper proposes a demand forecasting method of rush repair spare parts by using the Fuzzy C-Means (FCM) clus-tering and the Fuzzy Decision Tree (FDT). At first, we analyze the characteristics of emergency events causing spare parts demand and the attributes of spare parts' demand data. Secondly, FCM is applied to dividing the historic demand data into clusters. Then, FDT is used to mine the correlation between the spare parts demand and the emergency events and forecast the demand for rush repair spare parts according to the best cluster of data, and thus realize to predict the demand even with a small data set. Finally, we verify the proposed method by a data set of emergency demand for rush repair spare parts during the last three years from a power company.