Thèse de doctorat en Energétique et tranferts
Soutenue en 2014
à Toulouse 3 .
Dans le contexte de crise énergétique actuel, les concepteurs de bâtiments tentent de concevoir des habitats très performants d'un point de vue énergétique. Toutefois, de plus en plus de retours d'expérience montrent que les performances énergétiques réelles sont très différentes de celles prévues à l'origine lors des calculs de conception. Une des raisons principales avancées par la communauté scientifique est la mauvaise ou la non prise en compte du rôle actif des occupants. Les travaux présentés ici ont pour objectif de caractériser le comportement de l'occupant et son influence sur la performance énergétique. Différentes méthodologies sont utilisées : le plan d'expériences, une étude expérimentale in situ et la modélisation du comportement par intelligence artificielle dans le logiciel TRNSys.
Impact of occupant behavior on building energy performance : artificial intelligence modeling and post occupancy evaluation
Building sector plays a major role in global warming. In France, it is responsible of about 40% of energy consumption et about 33% of carbon emissions. In this context, building designers try to improve building energy performance. To do so, they often use building energy modeling (BEM) software to predict future energy use. For several years now, researchers have observed a difference between actual and predicted energy performance. Some reasons are pointed out such as uncertainties on physical properties of building materials and lack of precision of fluid dynamics models. One of the main causes could come from bad assessments in the modeling of occupant behavior. Occupant is often considered as passive in building simulation hypothesis. However, numerous of papers show that he act on the building he is in, and on personal characteristics. The work presented here intend to characterize occupant behavior and its influence on energy use. In the first part of the manuscript we assess the individual impact of several actions using design of experiments (DOE) methodology. Actions like operations on windows, blind or thermostat are investigated separately. We show that two opposite extreme behaviors (economic and wasteful) could lead to significant difference in building energy use. Moreover, a factor two-to-one in total energy use is observed between passive and active behaviors. In the second part we focused on an experimental approach. Thermal and visual environment of 4 offices have been monitored during a year and online questionnaires about comfort and behavior have been submitted to office occupants. Tank to a statistical analysis we estimates probabilities of acting on windows, blinds and clothing insulation against physical variables or thermal sensation. Final part of the thesis deals with the development of an occupant behavior model called OASys (Occupant Actions System) and running under TRNSys software. The model is based on an artificial intelligence algorithm and is intended to predict occupant interactions with thermostat, clothing insulation, windows, blinds and lighting system based on thermal and visual sensation. Results from OASys are compared to results from literature through various case studies for partial validation. They also confirm the significant impact of occupant behavior on building energy performance.