We study the consistency, robustness and efficiency of parameter estimation in different but related models via semiparametric approach. First, we revisit the second- order least squares estimator proposed in Wang and Leblanc (2008) and show that the estimator reaches the semiparametric efficiency. We further extend the method to the heteroscedastic error models and propose a semiparametric efficient estimator in this more general setting. Second, we study a class of semiparametric skewed distributions arising when the sample selection process causes sampling bias for the observations. We begin by assuming the anti-symmetric property to the skewing function. Taking into account the symmetric nature of the population distribution, we propose consistent estimators for the center of the symmetric population. These estimators are robust to model misspecification and reach the minimum possible estimation variance. Next, we extend the model to permit a more flexible skewing structure. Without assuming a particular form of the skewing function, we propose both consistent and efficient estimators for the center of the symmetric population using a semiparametric method. We also analyze the asymptotic properties and derive the corresponding inference procedures. Numerical results are provided to support the results and illustrate the finite sample performance of the proposed estimators.

The challenge of climate change is twofold: to mitigate (prevent) the causes of climate change and to prepare (adapt) to the inevitable effects and consequences. Building and construction are key sectors for decarbonisation (mitigation). The increase in intensity, frequency and duration of heat waves threatens indoor comfort and constitutes a health and comfort risk (adaptation).Therefore, regulations are being changed to take into account related emissions and extreme episodes through new indicators. However, up to now, past climate observations are still used in the calculation of these indicators. This raises the question of how to integrate future climate predictions into regulations. This work aims at characterising the vulnerability of buildings to climate change and aimsat taking into account future climate predictions in building design. It establishes a method for constructing standard weather data based on climate projections and for identifying vulnerable building typologies that are at risk. This project stands out for the use of a large number of building and meteorological data. 77 residential buildings from the Centre Scientifique et Technique du Bâtiment (CSTB) database and 78 years (1981-2058) of weather data for 9 climate models (RCP8.5 scenario) are crossed for Dynamic Thermal Simulations (DTS) on COMETh. The study first highlights the relevance of using reference and extreme years, representative of the climate data, to reduce the number of simulations. The reference year makes it possible to observe the average needs over a period. The extreme year estimates the range of values around this mean.The report then raises the issue of cooling systems as one of the major challenges for energy needs. Under the effect of climate change, heating requirements are decreasing and largely compensate the increase of cooling needs. But few buildings in France are already equipped with cooling systems and the creation of a need exceeding a threshold leads to the purchase of new units. This raises a problem of social equity in access to thermal comfort. Moreover, the environmentalimpact of these systems is more related to refrigerants necessary for the manufacturethan to energy consumption.The research finally proposes a method to classify passive or active buildings (in the sense of cooling needs), that are adapted or not adapted to future extreme weather conditions. This involves applying a clustering algorithm (k-means) to group similar buildings together in terms of energy requirements for different climate models. This method already makes it possible to identify the buildings at risk and to prioritise the measures to be taken (energy renovation). This classification also opens up the possibility of extending this work to newer, larger and more diversified samples. Similar encouraging results were obtained from 2470 offices. They could helpidentify technical and architectural characteristics and assist in the design of efficient passive buildings.

Les normes de construction pour des bâtiments de plus en plus économes en énergie (BBC) nécessitent une attention particulière. Ces normes reposent sur l’amélioration des performances thermiques de l’enveloppe du bâtiment associé à un effet capacitif des murs augmentant la constante de temps du bâtiment. La prévision de la demande en énergie de bâtiments BBC est plutôt complexe. Ce travail aborde cette question par la mise en œuvre d’intelligence artificielle(IA). Deux approches de mise en œuvre ont été proposées : « all data » et « relevant data ». L’approche « all data » utilise la totalité de la base de données. L’approche « relevant data » consiste à extraire de la base de données un jeu de données représentant le mieux possible les prévisions météorologiques en incluant les phénomènes inertiels. Pour cette extraction, quatre modes de sélection ont été étudiés : le degré jour (HDD), une modification du degré jour (mHDD) et des techniques de reconnaissance de chemin : distance de Fréchet (FD) et déformation temporelle dynamique (DTW). Quatre techniques IA sont mises en œuvre : réseau de neurones (ANN), machine à support de vecteurs (SVM), arbre de décision (DT) et technique de forêt aléatoire (RF). Dans un premier temps, six bâtiments ont été numériquement simulés (de consommation entre 86 kWh/m².an à 25 kWh/m².an) : l’approche « relevant data » reposant sur le couple (DTW, SVM) donne les prévisions avec le moins d’erreur. L’approche « relevant data » (DTW, SVM) sur les mesures du bâtiment de l’Ecole des Mines de Nantes reste performante. / High-energy efficiency building standards (as Low energy building LEB) to improve building consumption have drawn significant attention. Building standards is basically focused on improving thermal performance of envelope and high heat capacity thus creating a higher thermal inertia. However, LEB concept introduces alarge time constant as well as large heat capacity resulting in a slower rate of heat transfer between interior of building and outdoor environment. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs. This work focuses on artificial intelligence (AI) models to predict energy consumptionof LEBs. We consider two kinds of AI modeling approaches: “all data” and “relevant data”. The “all data” uses all available data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on either simple physical understanding: Heating Degree Day (HDD), modified HDD or pattern recognition methods: Frechet Distance and Dynamic Time Warping (DTW). Four AI techniques have been considered: Artificial Neural Network (ANN), Support Vector Machine (SVM), Boosted Ensemble Decision Tree (BEDT) and Random forest (RF). In a first part, numerical simulations for six buildings (heat demand in the range [25 – 85 kWh/m².yr]) have been performed. The approach “relevant data” with (DTW, SVM) shows the best results. Real data of the building “Ecole des Mines de Nantes” proves the approach is still relevant.