User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

Xin Jin, Kyri Baker, Dane Christensen, Steven Isley

Research output: Contribution to conferencePaper


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.
Original languageAmerican English
Number of pages8
StatePublished - 2017
Event2017 American Control Conference (ACC) - Seattle, Washington
Duration: 24 May 201726 May 2017


Conference2017 American Control Conference (ACC)
CitySeattle, Washington

NREL Publication Number

  • NREL/CP-5500-68037


  • battery
  • demand response
  • home energy management system
  • model predictive control
  • residential building


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