Machine learning techniques can be applied to sensor data collected from smart homes to reveal activity patterns of the residents, which can then be correlated with measured energy consumption. By associating activities with energy use and costs, intelligent systems can be devised to automatically control home environments so as to improve energy efficiency and cut expenses.
Teasing Detailed Home Habits from Aggregate Energy Consumption Data
Posted: 12 Feb 2012
Authors:Diane J. Cook and Chao Chen
Primary Committee:IEEE Smart Grid Newsletters
Sponsoring Society Members: Free
IEEE Members: $5.00
Please click 'Sign In' at the top of the page and log in with your IEEE Username and password. If you do not have an IEEE account, click 'Create Account' to create a FREE account to make a purchase. Alternatively, you can join IEEE and/or become a society member which will enable access to all materials; most of which are complimentary or discounted.
IEEE Smart Grid's 2018 IEEE International Forum on Smart Grids for Smart Cities (SG4SC) - Session 7B
IEEE Smart Grid's 2018 IEEE International Forum on Smart Grids for Smart Cities (SG4SC) - Session 7A
IEEE Smart Grid's 2018 IEEE International Forum on Smart Grids for Smart Cities (SG4SC) - Session 6B
Interview with Rob D'Arienzo - Outthink Severe Weather by Exploring AI-Infused Outage Management Solutions