{"title":"基于时间序列距离测量的电动汽车充电站短期负荷预测","authors":"Shi Xin, Q. Lei, Tian Li, Li Miaozhu, Yi Lizheng","doi":"10.1109/ICEMI.2017.8265838","DOIUrl":null,"url":null,"abstract":"To improve the efficiency and the precision of short-term load forecasting of the electric vehicle charging stations, this paper proposed a new algorithm for short — term load forecasting for electric vehicle charging stations based on time series distance measuring. Applying the editing distance to load the load sequence into the load segment and the time segment respectively to measure, so as to measure the distance between the load sequences accurately, And simplify the complexity of model which can improve the efficiency of prediction. Based on the actual historical load data of electric vehicle charging stations, the simulation analysis is given to verify that the proposed method has high prediction efficiency and accuracy.","PeriodicalId":275568,"journal":{"name":"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Short-term load forecasting for electric vehicle charging stations based on time series distance measuring\",\"authors\":\"Shi Xin, Q. Lei, Tian Li, Li Miaozhu, Yi Lizheng\",\"doi\":\"10.1109/ICEMI.2017.8265838\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve the efficiency and the precision of short-term load forecasting of the electric vehicle charging stations, this paper proposed a new algorithm for short — term load forecasting for electric vehicle charging stations based on time series distance measuring. Applying the editing distance to load the load sequence into the load segment and the time segment respectively to measure, so as to measure the distance between the load sequences accurately, And simplify the complexity of model which can improve the efficiency of prediction. Based on the actual historical load data of electric vehicle charging stations, the simulation analysis is given to verify that the proposed method has high prediction efficiency and accuracy.\",\"PeriodicalId\":275568,\"journal\":{\"name\":\"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEMI.2017.8265838\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMI.2017.8265838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short-term load forecasting for electric vehicle charging stations based on time series distance measuring
To improve the efficiency and the precision of short-term load forecasting of the electric vehicle charging stations, this paper proposed a new algorithm for short — term load forecasting for electric vehicle charging stations based on time series distance measuring. Applying the editing distance to load the load sequence into the load segment and the time segment respectively to measure, so as to measure the distance between the load sequences accurately, And simplify the complexity of model which can improve the efficiency of prediction. Based on the actual historical load data of electric vehicle charging stations, the simulation analysis is given to verify that the proposed method has high prediction efficiency and accuracy.