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

Abstract

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.

Authors:
 [1]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1378882
Report Number(s):
NREL/CP-5500-69116
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 American Control Conference (ACC), 24-26 May 2017, Seattle, Washington
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; demand response; home energy management system; model predictive control; residential building; battery

Citation Formats

Jin, Xin, Baker, Kyri A, Isley, Steven C, and Christensen, Dane T. User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response. United States: N. p., 2017. Web. doi:10.23919/ACC.2017.7963592.
Jin, Xin, Baker, Kyri A, Isley, Steven C, & Christensen, Dane T. User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response. United States. doi:10.23919/ACC.2017.7963592.
Jin, Xin, Baker, Kyri A, Isley, Steven C, and Christensen, Dane T. Mon . "User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response". United States. doi:10.23919/ACC.2017.7963592.
@article{osti_1378882,
title = {User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response},
author = {Jin, Xin and Baker, Kyri A and Isley, Steven C and Christensen, Dane T},
abstractNote = {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.},
doi = {10.23919/ACC.2017.7963592},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jul 03 00:00:00 EDT 2017},
month = {Mon Jul 03 00:00:00 EDT 2017}
}

Conference:
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