User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response [electronic resource] : Preprint
- Published:
- Washington, D.C. : United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy - Physical Description:
- 1.4 MB : digital, PDF file
- Additional Creators:
- National Renewable Energy Laboratory (U.S.), United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Summary:
- This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.
- Report Numbers:
- E 1.99:nrel/cp-5500-68037
nrel/cp-5500-68037 - Subject(s):
- Other Subject(s):
- Note:
- Published through SciTech Connect.
08/21/2017.
"nrel/cp-5500-68037"
Presented at the 2017 American Control Conference (ACC), 24-26 May 2017, Seattle, Washington.
Jin, Xin; Baker, Kyri; Christensen, Dane; Isley, Steven. - Funding Information:
- AC36-08GO28308
View MARC record | catkey: 23761132