User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1376840
- Report Number(s):
- NREL/CP-5500-68037
- Resource Relation:
- Conference: Presented at the 2017 American Control Conference (ACC), 24-26 May 2017, Seattle, Washington
- Country of Publication:
- United States
- Language:
- English
Similar Records
Foresee: A user-centric home energy management system for energy efficiency and demand response
Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint