{"title":"Simulation of power consumption in railway power supply systems with of artificial intelligence aids","authors":"V. Cheremisin, A. Komyakov, V. V. Erbes","doi":"10.1109/ICIEAM.2017.8076242","DOIUrl":null,"url":null,"abstract":"The paper considers simulation of power consumption at railway facilities. The text reveals the importance and urgency of improving the accuracy of the simulation of power consumption. The results of the analysis of the laws of distribution of electric power consumption of railway transport objects are presented. The study is based on samples of various railway subdivisions in operation. Process and climatic factors are selected as influences. The results of comparison of the simulation of electric power consumption by different methods are presented. Artificial intelligence aids (artificial neural network, fuzzy neural network, support vector machine) may considerably increase the accuracy of mathematical models.","PeriodicalId":428982,"journal":{"name":"2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEAM.2017.8076242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The paper considers simulation of power consumption at railway facilities. The text reveals the importance and urgency of improving the accuracy of the simulation of power consumption. The results of the analysis of the laws of distribution of electric power consumption of railway transport objects are presented. The study is based on samples of various railway subdivisions in operation. Process and climatic factors are selected as influences. The results of comparison of the simulation of electric power consumption by different methods are presented. Artificial intelligence aids (artificial neural network, fuzzy neural network, support vector machine) may considerably increase the accuracy of mathematical models.
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