Learning-based Model Predictive Control and User Feedback in Home Automation

Air conditioning systems are generally the largest systems in a home, both physically and energetically, and thus central to home automation efforts. Finding a balance between user comfort and efficiency is a complex problem given the considerable variation present. This paper focuses on comfort, as opposed to absolute temperature, as gauged using simple, sparse user inputs. A thermal model of the house is learned which accounts for weather data and exogenous factors such as occupancy. By incorporating user feedback, a Learning-Based Model Predictive Controller (LBMPC) is able adapt to home conditions and more efficiently operate the system. In contrast to previous efforts which operate in office spaces and to a set point, this work is adapted and tested in a typical home environment and closes a control loop on user comfort. The controller considers that the user's comfort levels may change during the day, for example when the user is in bed, or not at home. It shows that complex systems may be automated without extensive tweaking by the user and in a manner that considers user comfort, time of day, and related factors to reduce energy consumption.