Influence Of Building Zoning On Annual Energy Demand

Simulation tools are widely used to assess the energy consumption of a building. In the modeling process, some choices should be made by the simulation tool user such as the division of the building into thermal zones. The zoning process is user dependent, which results in some difference in energy consumption results and model set-up and computational times. The aim of this work is to assess the influence of building zoning on the results of the dynamic thermal simulation including airflow and thermal transfers between zones For this purpose, several different building zonings are applied to the same office building, and then the results of the dynamic thermal simulations are compared in terms of energy consumption (heating, cooling, and auxiliaries) and computational and set-up times. To assess the impact of thermal zoning, five cases are studied (from the most to the least complex): - 1) *49-zone model* : each zone gathers the premises with the same air handling system, the same occupancy profile, at each floor and building orientation. - 2) *44-zone model* : the premises containing the same air handling system are gathered at every floor, even though their occupancy profile is different. - 3) *26-zone model*: all floors are merged, except for the first and the top floors (under-roof). - 4) *21-zone model* : the first and the under-roof floors are merged with the others if the premises have the same occupancy profile and handling system. - 5) *11-zone model* : the premises with a different orientation but with the same occupancy profile and handling system are gathered. The importance of airflow coupling is evaluated by using the most detailed model (49 zones) and comparing the cases with or without considering air transfer from offices to corridors and toilets (from which air is extracted). Then, to study the impact of thermally connecting juxtaposed zones, the “21-zone model” with and without thermal transfer are compared. Finally, the impact of merging the floors is analyzed by considering different roof and floor insulations and the impact of merging the orientations is studied by using different glazed surface ratio

Energy performance guarantee in new buildings : Uncertainty assessment methods associated to the thermal dynamic simulation in the process of design and construction

La garantie de performance énergétique des bâtiments neufs demande d'estimer, avant construction, l'énergie requise pour assurer le confort de ses utilisateurs. Pour cela, il est nécessaire de définir une consommation contractuelle raisonnablement probable et d'identifier les paramètres clés responsables d'un éventuel dépassement afin de les maitriser. Dans les bâtiments neufs, ce calcul est effectué en phase de conception, alors que beaucoup de données sont encore incertaines. Ainsi, la simulation thermique dynamique doit être effectuée avec des données de conception hypothétiques, sans possibilité de calibration sur des mesures.La thèse a pour but de développer une méthode de quantification des incertitudes lors de la conception et la réalisation d'un bâtiment neuf. Ces incertitudes sont classées en trois catégories : celles liées au modèle physique du bâtiment, celles provenant du manque de connaissance des paramètres à renseigner et celles dues aux sollicitations du bâtiment (usage et météo).Dans une première partie, les incertitudes liées aux méthodes de calcul sont abordées, afin de définir des pratiques permettant d'arbitrer entre finesse de modèle et paramètres inconnus à entrer. Puis, on définit une méthodologie permettant de choisir les paramètres incertains critiques qui seront inclus dans l'étude probabiliste et de leur associer une densité de probabilité, selon la connaissance dont on dispose. La partie centrale de la thèse est consacrée à une comparaison exhaustive des méthodes visant la sélection d'une méthode rapide de propagation des incertitudes et d'analyse de sensibilité. Enfin, après avoir illustré la démarche globale d'engagement et discuté de la prise en compte des risques financiers, la méthode est mise en œuvre sur un cas réel, en ajoutant une formule d'ajustement pour prendre en compte les sollicitations.Before the construction of a building, an energy performance guarantee consists in predicting the energy required for user comfort. To do that, it is necessary to state a contractual consumption and identify the key parameters to pay special attention to. Thus, for new buildings, consumption is calculated under design phase, when several data are uncertain. Thus, the dynamic thermal simulation is carried out with hypothetical data, without having the possibility to calibrate with measures.This PhD thesis aims to develop a method of uncertainty quantification during the design step and construction process of a new building. These uncertainties are classified into three categories: those associated with the calculation methods used for building and system modeling, those related to the lack of knowledge of model parameters and those due to the real use conditions of the building (occupancy and weather).To achieve this goal, uncertainties associated with the calculation methods are addressed, to identify some practices limiting the number of errors and the associated uncertainties. Then, a methodology is defined to choose the critical parameters used for the probabilistic study and to associate them with a distribution according to the available knowledge. The central part of this PhD thesis is dedicated to an exhaustive comparison of methods to select a fast uncertainty propagation and sensitivity analysis method. Finally, after illustrating the overall contracting approach and discussing the inclusion of financial risks, the method is applied on a real case, on which an adjustment formula is added to take into account actual weather and usage

Vers une démarche de garantie des consommations énergétiques dans les bâtiments neufs : Méthodes d'évaluation des incertitudes associées à la simulation thermique dynamique dans le processus de conception et de réalisation.

Before the construction of a building, an energy performance guarantee consists in predicting the energy required for user comfort. To do that, it is necessary to state a contractual consumption and identify the key parameters to pay special attention to. Thus, for new buildings, consumption is calculated under design phase, when several data are uncertain. Thus, the dynamic thermal simulation is carried out with hypothetical data, without having the possibility to calibrate with measures.This PhD thesis aims to develop a method of uncertainty quantification during the design step and construction process of a new building. These uncertainties are classified into three categories: those associated with the calculation methods used for building and system modeling, those related to the lack of knowledge of model parameters and those due to the real use conditions of the building (occupancy and weather).To achieve this goal, uncertainties associated with the calculation methods are addressed, to identify some practices limiting the number of errors and the associated uncertainties. Then, a methodology is defined to choose the critical parameters used for the probabilistic study and to associate them with a distribution according to the available knowledge. The central part of this PhD thesis is dedicated to an exhaustive comparison of methods to select a fast uncertainty propagation and sensitivity analysis method. Finally, after illustrating the overall contracting approach and discussing the inclusion of financial risks, the method is applied on a real case, on which an adjustment formula is added to take into account actual weather and usage.La garantie de performance énergétique des bâtiments neufs demande d'estimer, avant construction, l'énergie requise pour assurer le confort de ses utilisateurs. Pour cela, il est nécessaire de définir une consommation contractuelle raisonnablement probable et d'identifier les paramètres clés responsables d'un éventuel dépassement afin de les maitriser. Dans les bâtiments neufs, ce calcul est effectué en phase de conception, alors que beaucoup de données sont encore incertaines. Ainsi, la simulation thermique dynamique doit être effectuée avec des données de conception hypothétiques, sans possibilité de calibration sur des mesures.La thèse a pour but de développer une méthode de quantification des incertitudes lors de la conception et la réalisation d'un bâtiment neuf. Ces incertitudes sont classées en trois catégories : celles liées au modèle physique du bâtiment, celles provenant du manque de connaissance des paramètres à renseigner et celles dues aux sollicitations du bâtiment (usage et météo).Dans une première partie, les incertitudes liées aux méthodes de calcul sont abordées, afin de définir des pratiques permettant d'arbitrer entre finesse de modèle et paramètres inconnus à entrer. Puis, on définit une méthodologie permettant de choisir les paramètres incertains critiques qui seront inclus dans l'étude probabiliste et de leur associer une densité de probabilité, selon la connaissance dont on dispose. La partie centrale de la thèse est consacrée à une comparaison exhaustive des méthodes visant la sélection d'une méthode rapide de propagation des incertitudes et d'analyse de sensibilité. Enfin, après avoir illustré la démarche globale d'engagement et discuté de la prise en compte des risques financiers, la méthode est mise en œuvre sur un cas réel, en ajoutant une formule d'ajustement pour prendre en compte les sollicitations

Adaptation of fan motor and VFD efficiency correlations using Bayesian inference

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Influence du découpage en zones sur les besoins énergétiques annuels

International audienceRÉSUMÉ. Les outils de simulation sont couramment employés pour évaluer la consommation énergétique d'un bâtiment. Lors du processus de modélisation, des choix doivent être réalisés par l'utilisateur de l'outil, tels que la division du bâtiment en zones thermiques. Le but de ce travail est d'évaluer l'influence du découpage en zones sur les résultats de la simulation thermique dynamique en incluant ou non les flux d'air et les transferts thermiques entre les zones. À cette fin, cinq découpages en zones (modèles de 49 à 11 zones) sont appliqués au même immeuble de bureau. L'impact de la fusion des étages est analysé en considérant différents isolants de plancher et toiture et celui de l'union des orientations est étudié à l'aide de différents taux de surface vitrée. Les résultats des simulations thermiques dynamiques sont comparés en termes de besoins énergétiques (chauffage et refroidissement) ainsi qu'en temps de calcul et de paramétrage du modèle.ABSTRACT. Simulation tools are widely used to assess the energy consumption of a building. In the modeling process, some choices should be made by the simulation tool user such as the division of the building into thermal zones. The aim of this work is to assess the influence of building zoning on the results of the dynamic thermal simulation including or not airflow and thermal transfers between zones. For this purpose, several different building zonings (49-zone to 11-zone models) are applied to the same office building. The impact of merging the floors is analyzed by considering different roof and floor insulations and the impact of merging the orientations is studied by using different glazed surface ratio. The results of the dynamic thermal simulations are compared in terms of energy demand (heating and cooling) and computational and setup times

A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings

© 2018 Elsevier B.V. An Energy Performance Contracting (EPC) is a financing agreement offered by general contractors that enables cost savings from reduced energy consumption to building owners. To create such an offer, the contractor has to provide an energy consumption threshold and a measurement plan. This article aims to draw some recommendations to choose an appropriate approach to provide the information necessary to create the contract, regarding computation time budget, expected accuracy and type of information provided. To get these results, we couple thermal simulations to various uncertainty and sensitivity methods. We first compare screening and differential sensitivity to reduce the number of inputs of the statistical study. Then, we analyze various uncertainty analysis methods to set an appropriate energy consumption threshold, considering the input uncertainties and the study context (Quadratic combination, directional and importance sampling and reliability methods). Sensitivity analyses in various input spaces are then carried out to identify the most critical contributors to energy levels to create the measurement plan. Finally, two metamodeling approaches are tested to reduce the overall computational time: Kriging and sparse polynomial chaos. These methods are tested and compared on a 4000 m² office building in Nantes, France. The resulting recommendations can be applied to any building, depending on the model regularity, the number of uncertain parameters and the objective of the study

A Comparison of Methods for Uncertainty and Sensitivity Analysis applied to the Energy Performance of New Commercial Buildings.

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Modelica-json: Transforming energy models to digitize the control delivery process

Building simulation models are typically not used to generate the documentation required for bidding and project delivery of commercial building systems, or for their semantic modeling and commissioning. This paper presents a software tool that aids in digitizing the control delivery process, spanning simulation during design to implementation and formal verification during commissioning. The tool can generate from Modelica models digital documentation of control sequences. This digital documentation, along with other project drawings and specifications can be used for project bidding. It can also be used for implementation of control sequences through machineto-machine translation to commercial legacy control products, for which we are currently developing the proposed ASHRAE Standard 231P based on the presented work. Moreover, as-installed control sequences can be formally verified against the design specification, and a semantic model in Brick can be exported to aid in configuration of building analytics and fault detection. The paper presents what we believe is the first translation of a Modelica-implemented control sequence to a native implementation on a commercial control platform, using the webCTRL product line from Automated Logic. The paper also shows how a webCTRL implementation can be formally verified against its Modelica specification. These use cases have all been demonstrated with a prototype implementation that is now being further developed

Prototyping The BOPTEST Framework For Simulation-Based Testing Of Advanced Control Strategies In Buildings

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