{"title":"使用机器学习算法的电气系统数据分析","authors":"Pillalamarri Madhavi, S. Satyanarayana","doi":"10.1109/ICAITPR51569.2022.9844178","DOIUrl":null,"url":null,"abstract":"Electrical Systems are designed in the amalgamation of various types of electrical equipment at the generation, transmission, and distribution verge to furnish uninterrupted power to the consumers. To communicate this process effectively, Machine learning techniques provide productive prediction and decision for any real-time application by using a set of instructions with proper statical data from the system. This paper gives a comprehensive study of Electrical Generation and Consumption through various sectors over the period of 2019-2020 and analyzes the prediction with regards to accuracy using various supervised Machine learning algorithms and conventional analysis of insulators for different voltage ratings.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data Analysis of Electrical Systems Using Machine Learning Algorithms\",\"authors\":\"Pillalamarri Madhavi, S. Satyanarayana\",\"doi\":\"10.1109/ICAITPR51569.2022.9844178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electrical Systems are designed in the amalgamation of various types of electrical equipment at the generation, transmission, and distribution verge to furnish uninterrupted power to the consumers. To communicate this process effectively, Machine learning techniques provide productive prediction and decision for any real-time application by using a set of instructions with proper statical data from the system. This paper gives a comprehensive study of Electrical Generation and Consumption through various sectors over the period of 2019-2020 and analyzes the prediction with regards to accuracy using various supervised Machine learning algorithms and conventional analysis of insulators for different voltage ratings.\",\"PeriodicalId\":262409,\"journal\":{\"name\":\"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAITPR51569.2022.9844178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAITPR51569.2022.9844178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data Analysis of Electrical Systems Using Machine Learning Algorithms
Electrical Systems are designed in the amalgamation of various types of electrical equipment at the generation, transmission, and distribution verge to furnish uninterrupted power to the consumers. To communicate this process effectively, Machine learning techniques provide productive prediction and decision for any real-time application by using a set of instructions with proper statical data from the system. This paper gives a comprehensive study of Electrical Generation and Consumption through various sectors over the period of 2019-2020 and analyzes the prediction with regards to accuracy using various supervised Machine learning algorithms and conventional analysis of insulators for different voltage ratings.