Amr Alzouhri Alyafi - Génération d’explications pour la gestion énergétique dans les bâtiments

Energy is fundamental to maintain comfort and it shapes our modern life. With the excess demand for energy, home energy management systems are appearing with time. They aim at reducing or modulating energy consumption while keeping an acceptable level of comfort. Efficient home energy management systems should embed a behavioral representation of a home system, including inhabitants. It establishes relationships between different environmental variables and heterogeneous phenomena present in a home. Therefore, those systems are complex to build and to understand for inhabitants. For this reason, the designers did try to automatize as much as possible the HVAC systems, the lightings …etc. So, they promoted the concept of “doing instead”. This was justified as it was easier for them than implicating occupants and simpler than trying to create a relation between occupants and energy systems. This concept does create different problems as occupants are detached from the energy system and they don’t understand its functionality nor how it is working.
 
To overcome this difficulty this work promotes the concept of “doing with” as it tries to implicate the occupant in the loop with their energy management system. This is where the explanation is needed to allow occupants to discover the knowledge in the energy system and to develop their capacity of understanding how the system is working and why it is recommending various actions. The explanation is the way to discover new knowledge and consequently, to involve occupants. For humans, explanation plays an important role in life. It is one of the main tools for learning and understanding. It is even used in communication and social aspects. People tend to use it besides learning to show their knowledge about a subject to gain the confidence of others or to clarify a situation. But generating explanations is not an easy task. It is one of the ongoing scientific problems from several decades. Explanations have numerous forms, types, and level of clearness. This study is focusing on the causal explanations. As it is the most intuitive form of explanation to be understood by occupants and is adapted to transfer the knowledge from complex systems like energy models. The scientific challenge is how to construct causal explanations for the inhabitants from a flow of observed sensor data.