foresee™ (SWR-17-15)
foresee™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, foresee™ intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, foresee™ provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. foresee™ improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highly reliable demand response services are likely to be incentivized by utility companies, making foresee™ economically viable for most homes.
- Short Name / Acronym:
- foresee™
- Project Type:
- Closed Source
- Site Accession Number:
- 7590; SWR 17-15
- Software Type:
- Scientific
- Programming Language(s):
- M Script, Python
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B); USDOE Bonneville Power Administration (BPA)Primary Award/Contract Number:AC36-08GO28308
- DOE Contract Number:
- AC36-08GO28308
- Code ID:
- 120154
- OSTI ID:
- code-120154
- Country of Origin:
- United States
Similar Records
Foresee: A user-centric home energy management system for energy efficiency and demand response
Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings: Preprint
User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response
Journal Article
·
Tue Aug 22 20:00:00 EDT 2017
· Applied Energy
·
OSTI ID:1395097
Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings: Preprint
Conference
·
Mon Nov 08 19:00:00 EST 2021
·
OSTI ID:1832222
User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response
Conference
·
Mon Jul 03 00:00:00 EDT 2017
·
OSTI ID:1378882