Cited By
View all- Nalmpantis CVrakas D(2019)Machine learning approaches for non-intrusive load monitoringArtificial Intelligence Review10.1007/s10462-018-9613-752:1(217-243)Online publication date: 6-Aug-2019
Energy efficiency via incentive design and utility learning
Pages 57 - 58
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.
References
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J. Eom. Shareholder incentives for utility-delivered energy efficiency programs in California. In Proceedings of the 28th USAEE/IAEE North American Conference, 2008.
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Index Terms
- Energy efficiency via incentive design and utility learning
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Information & Contributors
Information
Published In
April 2014
162 pages
ISBN:9781450326520
DOI:10.1145/2566468
- General Chairs:
- Linda Bushnell,
- Larry Rohrbough,
- Program Chairs:
- Saurabh Amin,
- Xenofon Koutsoukos
Copyright © 2014 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 April 2014
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HiCoNS '14
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HiCoNS '14: 3rd International Conference on High Confidence Networked Systems
April 15 - 17, 2014
Berlin, Germany
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HiCoNS '14 Paper Acceptance Rate 12 of 18 submissions, 67%;
Overall Acceptance Rate 30 of 55 submissions, 55%
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View all- Nalmpantis CVrakas D(2019)Machine learning approaches for non-intrusive load monitoringArtificial Intelligence Review10.1007/s10462-018-9613-752:1(217-243)Online publication date: 6-Aug-2019
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