Aggregated performance and qualitative modeling based smart thermal control

In order to ensure thermal energy efficiency and follow government's thermal guidance, more flexible and efficient buildings' thermal controls are required. This paper focuses on proposing an efficient, scalable, reusable, and data weak dependent smart thermal control approach based on an aggregated performance and imprecise knowledge of buildings' thermal specificities. Its main principle is to bypass data unavailability and quantitative models identification issues and to ensure an immediate thermal enhancement. For this, we propose, first, an aggregated performance based smart thermal control in order to identify relevant thermal setpoints. An extended thermal qualitative model is then introduced to guarantee an efficient achievement of the identified thermal setpoints. Uncertainty about how relevant a thermal control is for a given thermal situation is thus reduced using online and preference based learnings.