a b s t r a c tThe objective of this paper is to present a method to optimize the equivalent thermophysical properties of the external walls (thermal conductivity k wall and volumetric specific heat (c) wall ) of a dwelling in order to improve its thermal efficiency. Classical optimization involves several dynamic yearly thermal simulations, which are commonly quite time consuming. To reduce the computational requirements, we have adopted a methodology that couples an artificial neural network and the genetic algorithm NSGA-II. This optimization technique has been applied to a dwelling for two French climates, Nancy (continental) and Nice (Mediterranean). We have chosen to characterize the energy performance of the dwelling with two criteria, which are the optimization targets: the annual energy consumption Q TOT and the summer comfort degree I sum . First, using a design of experiments, we have quantified and analyzed the impact of the variables k wall and (c) wall on the objectives Q TOT and I sum , depending on the climate. Then, the optimal Pareto fronts obtained from the optimization are presented and analyzed. The optimal solutions are compared to those from mono-objective optimization by using an aggregative method and a constraint problem in GenOpt. The comparison clearly shows the importance of performing multiobjective optimization.

In this study, the impact of behavioral actions of a building occupant on energy performance and thermal sensation are investigated. The study focuses on the six following actions: use of blinds, lighting system, windows, fan, thermostat and clothing adjustments. Eight types of buildings, classified among three criteria (air-conditioning, thermal inertia and climate), are studied. Simulation of the occupant's actions, building performance and thermal sensation have been carried out by using TRNSYS 17. Impact on energy demand and thermal sensation of each action has been investigated with a Design Of Experiments methodology coupled with the use of Yate's algorithm. This study shows that for a given building, the occupant's actions have a significant impact on energy demand. Building simulation in literature typically does not model human activity in energy consumption, yet our study demonstrate a strong correlation. Results from the design of experiments methodology are compared to conventional French design strategy. It appears that conventional French design strategy, which does not take into account occupants' actions, tends to strongly underestimate building energy demand.

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Building simulation usually focuses on the study of physical indoor parameters, but we must not forget the main aim of a house: to provide comfort to the occupants. This study was undertaken in order to build a complete tool to model thermal behaviour that will enable the prediction of thermal sensations of humans in a real environment. A human thermoregulation model was added to TRNSYS, a building simulation program. For our purposes, improvements had to be made to the original physiological model, by refining the calculation of all heat exchanges with the environment and adding a representation of clothes. This paper briefly describes the program, its modifications, and compares its results with experimental ones. An example of potential use is given, which points out the usefulness of such models in seeking the best solutions to reach optimal environmental conditions for global, and specially local comfort, of building occupants.