Automated Learning and Decision : Making of a Smart Home System

Abstract: Smart homes are custom-fitted systems for users to manage their home environments. Smart homes consist of devices which has the possibility to communicate between each other. In a smart home system, the communication is used by a central control unit to manage the environment and the devices in it. Setting up a smart home today involves a lot of manual customizations to make it function as the user wishes. What smart homes lack is the possibility to learn from users behaviour and habits in order to provide a customized environment for the user autonomously. The purpose of this thesis is to examine whether environmental data can be collected and used in a small smart home system to learn about the users behaviour. To collect data and attempt this learning process, a system is set up. The system uses a central control unit for mediation between wireless electrical outlets and sensors. The sensors track motion, light, temperature as well as humidity. The devices and sensors along with user interactions in the environment make up the collected data. Through studying the collected data, the system is able to create rules. These rules are used for the system to make decisions within its environment to suit the users’ needs. The performance of the system varies depending on how the data collection is handled. Results find that collecting data in intervals as well as when an action is made from the user is important.