The surge of smart devices and IoT creates opportunities to learn about the condition of the building, and act upon it to create an 'optimal' condition. The large amount of available sensors and actuators prohibits their direct use by people, and demands for an automated approach.
In this thesis we will aim at modelling a building from a 'system' perspective, i.e. by considering the inputs (controllable and uncontrollable) and the outputs:
|weather conditions (outside temperature, wind, sun,...)
preferences of the habitants
Simulation tools of buildings and their envelopes are available, such as to allow for virtual prototyping of realistic buildings, and for obtaining years worth of usage data. The latter will be used to construct simplifying meta models, which are required in MPC or reinforcement learning types of control strategies.
The main goal is to create people-centric habitats which support their users in creating a healthy, comfortable and energy efficient indoor environment. This will be achieved by providing its users with contextualized feedback. Well-being and empowered user-control are the main drivers to reach this goal. At the same time, to assure that climate goals can be met, we should quantify the impact of human-centric buildings on the energy consumption.
As comfort control and energy efficient behavior are far less intuitive in nearly-zero energy buildings than in traditional homes, efficient control actions are far from straightforward. We will create control algorithms based on artificial intelligence technology to create efficient and robust feedback to the users (engineering aspect). To speed up the learning curve of the algorithm, it is fed with simulated data from physical modelling (including building geometry, heat transfer, impact of the sun and wind, ...). Feedback actions can be automated (through motors f.i.) or manual (human action). The feedback transfer method and the human interface should be designed for high user acceptance (behavioral aspect). Nudging will be used to optimize user acceptance.
ARCH, ETEC, TONA