Foresee: A user-centric home energy management system for energy efficiency and demand response. (1st November 2017)

• Record Type: Journal Article
• Authors: Jin, Xin; Baker, Kyri; Christensen, Dane; Isley, Steven
• Is Part Of: Applied energy. Volume 205(2017)
• Page Start: 1583
• Page End: 1595
• Publication Date: 2017-11-01
• Subjects: Home energy management system -- Model predictive control -- User preference -- Smart grid -- Energy efficiency -- Demand response; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.08.166
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A user-preference-driven home energy management system calledforesee is proposed. Foresee learns the preferences and needs of the occupants and acts on their behalf. Foresee predicts future comfort needs, energy costs and grid service availability. Foresee optimizes how a home operates to concurrently meet the occupants' needs. Foresee is built upon lightweight algorithms for deployment on embedded platforms. 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 thatforesee 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. These benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.