{"title":"通过激励设计和效用学习提高能源效率","authors":"L. Ratliff, Roy Dong, Henrik Ohlsson, S. Sastry","doi":"10.1145/2566468.2576849","DOIUrl":null,"url":null,"abstract":"Utility companies have many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company-consumer interaction as a principal-agent problem and present an iterative algorithm for designing incentives while estimating the consumer's utility function.","PeriodicalId":339979,"journal":{"name":"Proceedings of the 3rd international conference on High confidence networked systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Energy efficiency via incentive design and utility learning\",\"authors\":\"L. Ratliff, Roy Dong, Henrik Ohlsson, S. Sastry\",\"doi\":\"10.1145/2566468.2576849\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Utility companies have many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company-consumer interaction as a principal-agent problem and present an iterative algorithm for designing incentives while estimating the consumer's utility function.\",\"PeriodicalId\":339979,\"journal\":{\"name\":\"Proceedings of the 3rd international conference on High confidence networked systems\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd international conference on High confidence networked systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2566468.2576849\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd international conference on High confidence networked systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2566468.2576849","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy efficiency via incentive design and utility learning
Utility companies have many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company-consumer interaction as a principal-agent problem and present an iterative algorithm for designing incentives while estimating the consumer's utility function.
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