Predicting the Temperature Dynamics of Scaled Model and Real-World IoT-Enabled Smart Homes

Recent advances in IoT sensors and actuators and smart home controllers allow us to collect real-time information about the state of the home and take intelligent actions that maximize the user's goals with respect to comfort, convenience, environmental awareness and cost. While thermal comfort is one of the primary concerns of many users, many homes use a very simple, energy inefficient approach that blankets the home with constant temperature air conditioning. Such systems do not take advantage of more energy efficient and environment friendly natural ways to manage the temperature, such as opening and closing windows, window shades and interior doors. In this paper we develop a deep neural network based model that predicts the temperature in various rooms of the home function of the state of the actuators. We also describe a scaled model of a four room home which allows us to control the doors and windows and collect data using IoT devices. We train and validate our temperature models on both data collected from the scaled model as well as from publicly available datasets from two real-world smart homes.