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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The objective of this article is to provide a methodology for optimizing the envelope of a building with respect to the triple objective of heating load, cooling load and daylight. The variables to optimize are the window to wall area ratio (WWR) and the window type characterized by its visual and thermal characteristics (visual and solar transmittance, and U-value). Energy load is computed using building performance simulation software (TRNSYS). A criterion of daylight is defined as the integrated time when the illuminance is below a threshold and artificial light is required. This criterion was calculated using the software Daysim. The variables have antagonistic effects on the objectives: WWR and window type may have opposite effects by increasing solar gain and daylight duration during winter, which would be beneficial, but could lead to overheating during summer. Therefore, an easy-to-set up methodology is proposed to find the optimal solutions of such a problem. A multi-objective optimization was performed in order to find the optimal variables leading to the minimization of the energy load and the maximization of the indoor daylight duration. The method was applied to a dormitory retrofitting. Optimal solutions are the best compromise among antagonistic objectives and would offer guidance to designers in making construction decisions.

A thin-film-heater method is setup to measure the thermal conductivity of super insulating materials such as silica aerogels. The experimental setup is purposely designed for insulating materials and allows direct measurement of the thermal conductivity. Few experimental data are available in the literature concerning thermal conductivity of aerogels even though these materials are of major interest in thermal insulation. More data are necessary in order to understand thermal transport and to validate existing models. Monolithic and granular silica aerogels are investigated. Our experimental technique enables to quantify the influence of important parameters, such as air pressure and distribution of grain sizes, on the insulating performance of this material.