R. Alberdi, E. Fernández, I. Albizu, V. Valverde, M. Bedialauneta, K. Sagastabeitia
{"title":"智能电网容量预测的统计方法与天气预报","authors":"R. Alberdi, E. Fernández, I. Albizu, V. Valverde, M. Bedialauneta, K. Sagastabeitia","doi":"10.1109/POWERAFRICA.2016.7556562","DOIUrl":null,"url":null,"abstract":"One of the purposes of smart grids is the efficient delivery of sustainable, economic and secure electricity supplies. One of the strategies used for this purpose is the control and improvement of overhead lines ampacity. A smart use of the actual ampacity requires the implementation of intelligent control devices. Research on ampacity is aimed not only to calculate it in the real time, but also to be able to forecast it several days or hours in advance. The aim of this paper is to compare different ampacity forecasting methods based on a bibliographic analysis. The different methods are classified in terms of the algorithms, the required data and the forecast length. Experiences that show the application of the methods in smart grids are described.","PeriodicalId":177444,"journal":{"name":"2016 IEEE PES PowerAfrica","volume":"251 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Statistical methods and weather prediction for ampacity forecasting in smart grids\",\"authors\":\"R. Alberdi, E. Fernández, I. Albizu, V. Valverde, M. Bedialauneta, K. Sagastabeitia\",\"doi\":\"10.1109/POWERAFRICA.2016.7556562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the purposes of smart grids is the efficient delivery of sustainable, economic and secure electricity supplies. One of the strategies used for this purpose is the control and improvement of overhead lines ampacity. A smart use of the actual ampacity requires the implementation of intelligent control devices. Research on ampacity is aimed not only to calculate it in the real time, but also to be able to forecast it several days or hours in advance. The aim of this paper is to compare different ampacity forecasting methods based on a bibliographic analysis. The different methods are classified in terms of the algorithms, the required data and the forecast length. Experiences that show the application of the methods in smart grids are described.\",\"PeriodicalId\":177444,\"journal\":{\"name\":\"2016 IEEE PES PowerAfrica\",\"volume\":\"251 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE PES PowerAfrica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERAFRICA.2016.7556562\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE PES PowerAfrica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERAFRICA.2016.7556562","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical methods and weather prediction for ampacity forecasting in smart grids
One of the purposes of smart grids is the efficient delivery of sustainable, economic and secure electricity supplies. One of the strategies used for this purpose is the control and improvement of overhead lines ampacity. A smart use of the actual ampacity requires the implementation of intelligent control devices. Research on ampacity is aimed not only to calculate it in the real time, but also to be able to forecast it several days or hours in advance. The aim of this paper is to compare different ampacity forecasting methods based on a bibliographic analysis. The different methods are classified in terms of the algorithms, the required data and the forecast length. Experiences that show the application of the methods in smart grids are described.