For the energy-related issues that the world is facing nowadays, the renovation of the building stock is one of the major challenges. The objective of this study is to present an approach that helps to develop a multi-criteria decision support tool dedicated to the rehabilitation of sustainable and passive energy housing by integrating heating energy needs, economic, social and environmental criteria throughout the life cycle stages of the building. The methodology consists of developing metamodels to predict heating energy needs from polynomial regression, design of experiments method and thermo-aeraulic simulations of building behavior. This metamodel is used to carry out a combinatorial study of real technical solutions. The methodology was applied to a real-life existing building located in La Rochelle city (France) based on an in-situ energy diagnosis. Three multicriteria analysis methods were studied and compared: weighted sum, Min-Max and Pareto concept. Furthermore, technical constraints as well as owner preferences and performance constraints have been studied. Optimal technical solutions have been obtained in order to meet the various criteria studied. In addition, window shading and natural ventilation have been proposed to reduce the thermal discomfort rate in summer. This study was extended to all French regions. Finally, this method can be transformed into a decision support tool which will be useful for architects, engineers and stockholders.

The commercial and public services sectors including shopping centers, worship buildings, theatres, and other types, account for more than 20% of the electricity consumption in the world. These building typologies are characterized by large spaces and high and temporary occupation. Besides, the horizontal temperature distribution in these buildings becomes one of the important parameters on occupant's comfort and energy efficiency. In the present study, a thermo-aeraulic zonal model using TRNSYS and CONTAM simulation tools is developed to analyze the spatial temperature distribution in a large building. Parametric studies relating to mesh discretization of building volume are performed to optimize the computational time and convergence. Extensive computational simulation is carried out to analyze the impact of building height, internal loads, natural ventilation and climatic conditions on the spatial temperature distribution, building energy performance, and thermal comfort. The developed simulation model in this study is effective to predict the horizontal temperature distribution with reasonable computation time compared to CFD simulations. The results show that the internal heat gains lead to an increase in the horizontal temperature gradient which should not be negligible especially in the case of large buildings. On the other side, natural night ventilation can reduce the peak tempearture in building by 3 ℃ for normal occupation building with limited internal gains. Furthermore, good spatial temperature distribution can decrease annual building energy needs about 32%. It can be helpful for architects and building developers to make an optimal choice regarding to building envelope and HVAC design.