{"title":"基于人工神经网络的非线性负荷监测与分类","authors":"M. A. Stosovic, D. Stevanović, M. Dimitrijevic","doi":"10.1109/TELSKS.2017.8246320","DOIUrl":null,"url":null,"abstract":"In this paper artificial neural networks are used for identification of nonlinear loads on the network, as one of the methods for non-intrusive load monitoring. Specific parameters of a group of the nonlinear loads are first measured, and then a neural network is used in order to identify which load, or group of loads, is active on the network. It is shown within an example that the task of loads identification can be done successfully.","PeriodicalId":206778,"journal":{"name":"2017 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Monitoring and classification of nonlinear loads based on artificial neural networks\",\"authors\":\"M. A. Stosovic, D. Stevanović, M. Dimitrijevic\",\"doi\":\"10.1109/TELSKS.2017.8246320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper artificial neural networks are used for identification of nonlinear loads on the network, as one of the methods for non-intrusive load monitoring. Specific parameters of a group of the nonlinear loads are first measured, and then a neural network is used in order to identify which load, or group of loads, is active on the network. It is shown within an example that the task of loads identification can be done successfully.\",\"PeriodicalId\":206778,\"journal\":{\"name\":\"2017 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TELSKS.2017.8246320\",\"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 International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TELSKS.2017.8246320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monitoring and classification of nonlinear loads based on artificial neural networks
In this paper artificial neural networks are used for identification of nonlinear loads on the network, as one of the methods for non-intrusive load monitoring. Specific parameters of a group of the nonlinear loads are first measured, and then a neural network is used in order to identify which load, or group of loads, is active on the network. It is shown within an example that the task of loads identification can be done successfully.
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