{"title":"电力系统机器学习研究进展","authors":"Zhibo Ma, Chi Zhang, Chen Qian","doi":"10.1109/ISGT-Asia.2019.8881330","DOIUrl":null,"url":null,"abstract":"The trend of decentralisation and decarbonisation have been developed over the years. This has brought certain challenges to the prediction and control of the energy system using conventional method. There are some recent technology breakthrough in Machine Learning which has made some of the objectives achievable in many different aspects especially for the non-linear tasks. This paper has focused on reviewing the available machine learning technologies applied on the fault forecasting and load forecasting in power system.","PeriodicalId":257974,"journal":{"name":"2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Review of Machine Learning in Power System\",\"authors\":\"Zhibo Ma, Chi Zhang, Chen Qian\",\"doi\":\"10.1109/ISGT-Asia.2019.8881330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The trend of decentralisation and decarbonisation have been developed over the years. This has brought certain challenges to the prediction and control of the energy system using conventional method. There are some recent technology breakthrough in Machine Learning which has made some of the objectives achievable in many different aspects especially for the non-linear tasks. This paper has focused on reviewing the available machine learning technologies applied on the fault forecasting and load forecasting in power system.\",\"PeriodicalId\":257974,\"journal\":{\"name\":\"2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISGT-Asia.2019.8881330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISGT-Asia.2019.8881330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The trend of decentralisation and decarbonisation have been developed over the years. This has brought certain challenges to the prediction and control of the energy system using conventional method. There are some recent technology breakthrough in Machine Learning which has made some of the objectives achievable in many different aspects especially for the non-linear tasks. This paper has focused on reviewing the available machine learning technologies applied on the fault forecasting and load forecasting in power system.