Approximate reasoning for an efficient, scalable and simple thermal control enhancement

Afef Denguir (1, 2) , François Trousset (1) , Jacky Montmain (1)

1 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production
2 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

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 scalable, efficient and simple thermal control approach based on imprecise knowledge of buildings’ specificities. Its main principle is a weak data-dependency which ensures the scalability and simplicity of our thermal enhancement approach. For this, an extended thermal qualitative model is proposed. It is based on a qualitative description of influences that actions’ parameters may have on buildings’ thermal performances. Our thermal qualitative model is enriched by collecting and assessing previous thermal control performances. Thus, an approximate reasoning for a smart thermal control becomes effective based on our extended thermal qualitative model.

Mots clés: Qualitative modeling; approximate reasoning; smart thermal control; online learning; preference based learning

Domaines: Intelligence artificielle [cs.AI]; Automatique

Afef Denguir, François Trousset, Jacky Montmain. Approximate reasoning for an efficient, scalable and simple thermal control enhancement. 15th International Conference on Information processing and Management of uncertainty in Knowledge-Based Systems (IPMU 2014), Jul 2014, Montpellier, France. ⟨10.1007/978-3-319-08795-5_43⟩. ⟨hal-01933894⟩