Smart Room-by-Room HVAC Scheduling for Residential Savings and Comfort

HVAC systems account for a significant amount of the energy spent in residences with forced-air systems. Moreover, HVAC is a major contributor to greenhouse gas and fine particulate matter emissions. Recently, the use of sensors, wireless, microprocessors, and the Internet of Things (IoT) have been proposed to increase energy efficiency in residential buildings by controlling the duty cycle of furnaces and air-conditioners. In this paper, we propose ILPSS (Integer Linear Programming for Smart Scheduling), a novel solution for optimizing the duty cycle of the HVAC equipment and improving users' comfort by allowing users to specify comfort levels at specific times in each room of a residence. Our proposal builds on multiple-variable, linear regression model and integer linear programming and can be run on a home IoT hub. Our experimental evaluation on a real dataset shows that our proposed solution saves energy (up to 45%) and meets users' comfort needs, compared to commodity and current smart HVAC systems.