基于人工智能辅助的铁路供电系统功耗仿真

2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) Pub Date : 2017-05-01 DOI:10.1109/ICIEAM.2017.8076242

V. Cheremisin, A. Komyakov, V. V. Erbes

{"title":"基于人工智能辅助的铁路供电系统功耗仿真","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":"{\"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}","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

摘要

本文考虑了铁路设施用电的仿真问题。本文揭示了提高功耗仿真精度的重要性和紧迫性。给出了铁路运输对象电耗分布规律的分析结果。该研究是基于运行中的各个铁路分部的样本。选取工艺和气候因素作为影响因素。并对不同方法的电耗仿真结果进行了比较。人工智能辅助工具(人工神经网络、模糊神经网络、支持向量机)可以大大提高数学模型的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Simulation of power consumption in railway power supply systems with of artificial intelligence aids

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

自引率

0.00%

发文量