À mesure que les performances intrinsèques des bâtiments s’améliorent, les usages énergétiques non réglementés, que nous associons à une notion d’intensité énergétique des usages, prennent de plus en plus d’importance dans le bilan des consommations des bâtiments. De plus, les bâtiments performants font apparaître des problématiques au niveau de l’exploitation des installations. Ces constats nous permettent d’affirmer qu’il est aujourd’hui important de proposer un cadre pour le suivi et l’optimisation de la sobriété énergétique des usages et l’exploitation performante pour la maîtrise des consommations énergétiques réelles des bâtiments. Cette thèse propose tout d’abord de développer des modèles polynomiaux de prédiction de la consommation énergétique tous usages en fonction des facteurs caractérisant l’intensité d’usage, la qualité d’usage et la qualité d’exploitation. Pour cela, nous utilisons le logiciel EnergyPlus afin de réaliser des simulations énergétiques dynamiques (SED) sur des valeurs de paramètres définis par la méthode des plans d’expérience D-optimaux. Le modèle polynomial créé permet alors d’effectuer, avec un faible temps de calcul, une propagation des incertitudes sur les consommations d’énergie calculées. Pour ce faire, nous utilisons les données mesurées en exploitation dans le cadre de la mesure et de la vérification de la performance énergétique, associées à une incertitude concernant leur valeur. Nous pouvons alors déterminer l’incertitude globale sur les consommations énergétiques et identifier les pistes pour la réduire, permettant ainsi un meilleur suivi et encadrement de la consommation énergétique réelle. / Since building envelope and MEP systems characteristics regularly improve, the weight of non-regulatory energy end-uses increases. These energy end-uses are typically associated with tenants or owners’ activities. In addition, high performance buildings show new issues related to HVAC systems operations. Therefore, it is important to evaluate and improve non-regulatory energy end-uses energy as well as HVAC systems operations efficiencies. We have developed polynomial energy models that can predict energy consumption as a function of building’s activities characteristics and HVAC systems operations factors. We used EnergyPlus software in order to build reliable energy models along with the D-optimum design of experiments method (DOE). Then, we used measurement and verification (M&V) data, associated with probability functions, to determine the associated uncertainty of the calculated energy consumption. Finally, we combine the latter with the polynomial modeling error to calculate the energy consumption global uncertainty, with the goal to identify strategies to reduce it.

The goal of this dissertation is to demonstrate that data from a remotely located building can be utilized for energy modeling of a similar type of building and to demonstrate how to use this remote data without physically moving the data from one server to another using Virtual Information Fabric Infrastructure (VIFI). In order to achieve this goal, firstly an EnergyPlus model was created for Greek Life Center, a campus building located at University of North Texas campus at Denton in Texas, USA. Three thermal comfort models of Fanger model, Pierce two-node model and KSU two-node model were compared in order to find which one of these three models is most accurate to predict occupant thermal comfort. This study shows that Fanger's model is most accurate in predicting thermal comfort. Secondly, an experimental data pertaining to lighting usage and occupancy in a single-occupancy office from Carnegie Mellon University (CMU) has been implemented in order to perform energy analysis of Greek Life Center assuming that occupants in this building's offices behave similarly as occupants in CMU. Thirdly, different data types, data formats and data sources were identified which are required in order to develop a city-scale urban building energy model (CS-UBEM). Two workflows were created, one for an individual scale building energy model and another one for CS-UBEM. A new innovative infrastructure called as Virtual Information Fabric Infrastructure (VIFI) has been introduced in this dissertation. The workflows proposed in this study will demonstrate in the future work that by using VIFI infrastructure to develop building energy models there is a potential of using data for remote servers without actually moving the data. It has been successfully demonstrated in this dissertation that data located at remote location can be used credibly to predict energy consumption of a newly built building. When the remote experimental data of both lighting and occupancy are implemented, 4.57% energy savings was achieved in the Greek Life Center energy model.