Po Hu, Yunjia Wang, Ning Pang, Zeya Zhang, Yifan Huang, Haoran Guo
{"title":"基于多模型并行积分算法的住宅负荷分类方法研究","authors":"Po Hu, Yunjia Wang, Ning Pang, Zeya Zhang, Yifan Huang, Haoran Guo","doi":"10.1109/ICCSI53130.2021.9736211","DOIUrl":null,"url":null,"abstract":"With the gradual upgrading of power grid to energy Internet, machine learning and artificial intelligence technology are booming in the field of load classification. In this paper, combined with the theory of artificial intelligence, aiming at the problem of residential user load type classification, a residential load classification algorithm based on multi model parallel integration(MMPI) is proposed. The algorithm constructs several base classifiers with different performance, embeds the ensemble learning load classification model, and processes them in parallel on MATLAB platform. An example is given to verify the effectiveness of the algorithm by using Irish electricity consumption data. The classification results show that, compared with the traditional single model load classification, the load classification method based on multi model parallel integration has higher classification accuracy and efficiency.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on residential load classification method based on Multi-model parallel integration algorithm\",\"authors\":\"Po Hu, Yunjia Wang, Ning Pang, Zeya Zhang, Yifan Huang, Haoran Guo\",\"doi\":\"10.1109/ICCSI53130.2021.9736211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the gradual upgrading of power grid to energy Internet, machine learning and artificial intelligence technology are booming in the field of load classification. In this paper, combined with the theory of artificial intelligence, aiming at the problem of residential user load type classification, a residential load classification algorithm based on multi model parallel integration(MMPI) is proposed. The algorithm constructs several base classifiers with different performance, embeds the ensemble learning load classification model, and processes them in parallel on MATLAB platform. An example is given to verify the effectiveness of the algorithm by using Irish electricity consumption data. The classification results show that, compared with the traditional single model load classification, the load classification method based on multi model parallel integration has higher classification accuracy and efficiency.\",\"PeriodicalId\":175851,\"journal\":{\"name\":\"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSI53130.2021.9736211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSI53130.2021.9736211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on residential load classification method based on Multi-model parallel integration algorithm
With the gradual upgrading of power grid to energy Internet, machine learning and artificial intelligence technology are booming in the field of load classification. In this paper, combined with the theory of artificial intelligence, aiming at the problem of residential user load type classification, a residential load classification algorithm based on multi model parallel integration(MMPI) is proposed. The algorithm constructs several base classifiers with different performance, embeds the ensemble learning load classification model, and processes them in parallel on MATLAB platform. An example is given to verify the effectiveness of the algorithm by using Irish electricity consumption data. The classification results show that, compared with the traditional single model load classification, the load classification method based on multi model parallel integration has higher classification accuracy and efficiency.