Learning Individual Preferences for Energy-Efficiency and Comfortable Living

Abstract

Automatic control of HVAC and artificial lights has been one of the popular methods for achieving energy-efficient buildings. The operating set-points are decided based on predefined values to ensure comfort level to most of the occupants based on prior studies. However, a person can feel comfortable beyond the traditional set-point ranges used in the energy management systems of buildings. In this work, we develop a smartphone application that learns individual preferences about thermal and visual comfort with minimal user intervention. These functions provide the flexibility to operate the controllers in an aggressively lower energy consuming state while maintaining the comfort level of the occupants. Using a HVAC energy consumption model, we show that individual comfort preference based set-point can attain lesser energy consumption as compared to fixed set-point.