skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Foresee: A user-centric home energy management system for energy efficiency and demand response

Abstract

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of themore » battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States); Univ. of Colorado, Boulder, 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), Energy Efficiency Office. Building Technologies Office
OSTI Identifier:
1395097
Alternate Identifier(s):
OSTI ID: 1549761
Report Number(s):
NREL/JA-5500-69073
Journal ID: ISSN 0306-2619
Grant/Contract Number:  
AC36-08GO28308; TIP-337
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 205; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; home energy management system; model predictive control; user preference; smart grid; energy efficiency; demand response

Citation Formats

Jin, Xin, Baker, Kyri A., Christensen, Dane T., and Isley, Steven. Foresee: A user-centric home energy management system for energy efficiency and demand response. United States: N. p., 2017. Web. doi:10.1016/j.apenergy.2017.08.166.
Jin, Xin, Baker, Kyri A., Christensen, Dane T., & Isley, Steven. Foresee: A user-centric home energy management system for energy efficiency and demand response. United States. https://doi.org/10.1016/j.apenergy.2017.08.166
Jin, Xin, Baker, Kyri A., Christensen, Dane T., and Isley, Steven. 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response". United States. https://doi.org/10.1016/j.apenergy.2017.08.166. https://www.osti.gov/servlets/purl/1395097.
@article{osti_1395097,
title = {Foresee: A user-centric home energy management system for energy efficiency and demand response},
author = {Jin, Xin and Baker, Kyri A. and Christensen, Dane T. and Isley, Steven},
abstractNote = {This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.},
doi = {10.1016/j.apenergy.2017.08.166},
url = {https://www.osti.gov/biblio/1395097}, journal = {Applied Energy},
issn = {0306-2619},
number = ,
volume = 205,
place = {United States},
year = {Wed Aug 23 00:00:00 EDT 2017},
month = {Wed Aug 23 00:00:00 EDT 2017}
}

Journal Article:

Citation Metrics:
Cited by: 93 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Smart home energy management systems: Concept, configurations, and scheduling strategies
journal, August 2016


An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid
journal, March 2017


Demand response implementation in smart households
journal, May 2017


Optimal Scheduling of Domestic Appliances via MILP
journal, December 2014


MPC-Based Appliance Scheduling for Residential Building Energy Management Controller
journal, September 2013


Residential Demand Response Scheduling with Consideration of Consumer Preferences
journal, January 2016


Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery
journal, March 2017


Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing
journal, July 2016


Load commitment in a smart home
journal, August 2012


A Review on Demand Response: Pricing, Optimization, and Appliance Scheduling
journal, January 2015


Distributed generation: Residential storage comes at a cost
journal, January 2017


SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement
journal, December 1994


Transactive Home Energy Management Systems: The Impact of Their Proliferation on the Electric Grid
journal, December 2016


Works referencing / citing this record:

Distributed Optimal Coordinated Operation for Distribution System with the Integration of Residential Microgrids
journal, May 2019


Optimal energy management for the residential MES
journal, May 2019