{"title":"智能家居的能源管理","authors":"Muhammad Umair, G. Shah","doi":"10.1109/SMARTCOMP50058.2020.00054","DOIUrl":null,"url":null,"abstract":"This paper presents a Markov chain based probabilistic model to get users' stochastic activity patterns and to predict the energy consumption of a smart home. These predictions are then incorporated in our prediction and feedback based proactive energy conservation (PF-PEC) algorithm, to reduce electricity cost without compromising human comfort. The experimental results show that the proposed algorithm minimizes the total energy consumption while also ensuring standard human comfort in a smart home environment.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy Management of Smart Homes\",\"authors\":\"Muhammad Umair, G. Shah\",\"doi\":\"10.1109/SMARTCOMP50058.2020.00054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a Markov chain based probabilistic model to get users' stochastic activity patterns and to predict the energy consumption of a smart home. These predictions are then incorporated in our prediction and feedback based proactive energy conservation (PF-PEC) algorithm, to reduce electricity cost without compromising human comfort. The experimental results show that the proposed algorithm minimizes the total energy consumption while also ensuring standard human comfort in a smart home environment.\",\"PeriodicalId\":346827,\"journal\":{\"name\":\"2020 IEEE International Conference on Smart Computing (SMARTCOMP)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Smart Computing (SMARTCOMP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMARTCOMP50058.2020.00054\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMARTCOMP50058.2020.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a Markov chain based probabilistic model to get users' stochastic activity patterns and to predict the energy consumption of a smart home. These predictions are then incorporated in our prediction and feedback based proactive energy conservation (PF-PEC) algorithm, to reduce electricity cost without compromising human comfort. The experimental results show that the proposed algorithm minimizes the total energy consumption while also ensuring standard human comfort in a smart home environment.