Littérature scientifique sur le sujet « Building thermal models »

10 Intermediate-level'o computer codes are advocated as being the most appropriate for meeting the requirements of dynamic building thermal models. Such codes may be developed via the .4 computer-generalizationA Of analytical solutions and data correlations, which are then verified using higher-level ccoputational procedures and/or experimental measurements. Two intermediate-level ccniputer codes are described: one to model the convective heat exchange at the external facades of a building (WIND-CHT program), and the other to calculate the hourly mean rates of air infiltration into buildings (FLOW program). These codes take into account most of the key parameters such as wind speed and direction, the change in shape and height of the atmospheric boundary-layer over different terrains, the relative dimensions of the building,, the indoor-outdoor temperature difference and the leakage characteristics of the building. Both the WIND-CHT and FLOW programs are carpared with field experimental data, and good agreement is shown. The sensitivity of two dynamic building thermal models to the external convection and air infiltration input data are then assessed. The NBSLD (National Bureau of Standards Load Determination) 'response factor' program (1981) and the BM (British Research Establishment) 'admittance procedure' program (1984) were chosen for this purpose. The sensitivity of these models to the internal convection input data was also assessed. In this case the ROOM-CHT program, developed by Alamdari and Hammond (1982) was employed. Both models displayed a considerable variation in their results when the 'traditional' input data were replaced by the 'improved" values, although the extend of the impact of the convection and infiltration models is likely to depend on the conditions prevailing in and around the particular building being simulated.

The research activity carried out during the three years of the PhD course attended, at the Engineering Department of the University of Palermo, was aimed at the identification of an alternative predictive model able to solve the traditional building thermal balance in a simple but reliable way, speeding up any first phase of energy planning. Nowadays, worldwide directives aimed at reducing energy consumptions and environmental impacts have focused the attention of the scientific community on improving energy efficiency in the building sector. The reduction of energy consumption and CO2 emissions for heating and cooling needs of buildings is an important challenge for the European Union, because the buildings sector contributes up to 36% of the global CO2 emissions [1] and up to 40% of total primary energy consumptions [2]. Despite the ambitious goals set by the Energy Performance of Buildings Directive (EPBD) at the European level [1], which states that, by 2020, all new buildings and existing buildings undergoing major refurbishments will have to be Nearly Zero Energy Buildings (NZEB) [3,4], the critical challenge remains the improvement of the efficiency when upgrading the existing building stock to standards of the NZEB level [5]. The improvement of the energy efficiency of buildings and their operational energy usage should be estimated early in the design phase to guarantee a reduction in energy consumption, so buildings can be as sustainable as possible [6]. While a newly constructed NZEB can employ the “state of the art” of available efficient technologies and design practices, the optimization of existing buildings requires better efforts [7]. One way or the other, the identification of the best energy retrofit actions or the choice of a better technological solution to plan a building is not so simple. It has become one of the main objectives of several research studies, which require deep knowledge in the field of the building energy balance. The building thermal balance includes all sources and sinks of energy, as well as all energy that flows through its envelope. More in detail, the energy demand in buildings depends on the combination of several parameters, such as climate, envelope features, occupant behaviour and intended use. Indeed, the assessment of building energy performance requires substantial input data describing structures, environmental conditions [8], thermo-physical properties of the envelope, geometry, control strategies, and several other parameters. From the first design phases designers and researchers, which are trying to respect the prescriptions of the EPBD directive and to simultaneously ensure the thermal comfort of the occupants, must optimize all possible aspects that represent the key points in the building energy balance. As will be shown in Chapter A, the literature offers highly numerous complex and simplified resolution approaches [9]. Some are based on knowledge of the building thermal balance and on the resolution of physical equations; others are based on cumulated building data and on implementations of forecast models developed by machine-learning techniques [10]. Several numerical approaches are most widespread; these have undergone testing and implementing in specialised software tools such as DOE-2 [11], Energy Plus [12], TRNSYS [13] and ESP-r [14]. Such building modelling software can be employed in several ways on different scales; they can be simplified [15,16] or detailed comprehensively by different methods and numerical approaches [17]. Nevertheless, they are often characterised by a lack of a common language, which constitutes an obstacle for making a suitable choice. It is often more convenient to accelerate the building thermal needs evaluation and use the simplified methods and models. For example, a steady state approach for the evaluation of thermal loads is characterised by a good level of accuracy and low computational costs. However, its main limitation is that some phenomenon, such as the thermal inertia of the building envelope/structure, may be completely neglected. On the other hand, the choice of a more complex solution, such as the dynamic approach, uses very elaborate physical functions to evaluate the energy consumption of buildings. Although these dynamic simulation tools are effective and accurate, they have some practical difficulties such as collecting detailed building data and/or evaluating the proper boundary conditions. The use of these tools normally requires an expert user and a careful calibration of the model and do not provide a generalised response for a group of buildings with the same simulation, because they support a specific answer to a specific problem. Meanwhile the lack of precise input can lead to low-accuracy simulation. Anyway, in all cases it is necessary to be an expert user to implement, solve and evaluate the results, and these phases are not fast and not always immediately provide the correct evaluation, conducting the user to restart the entire procedure. In the field of energy planning, in order to identify energy efficiency actions aimed at a particular context, could be more convenient to speed up the preliminary assessment phase resorting to a simplified model that allows the evaluation of thermal energy demand with a good level of accuracy and without excessive computational cost or user expertise. The aim of this research, conducted during the three years of the PhD studies, is based on the idea of overcoming the limits previously indicated developing a reliable and a simple building energy tool or an evaluation model capable of helping an unskilled user at least in the first evaluation phase. To achieve this purpose, the first part of the research was characterised of an in-depth study of the sector bibliography with the analysis of the most widespread and used methods aimed at solving the thermal balance of buildings. After a brief distinction of the analysed methods in White, Black and Grey Box category, it was possible to highlight the strengths and weaknesses of each one [9]. Based on the analysis of this study, some alternative methods have been investigated. In detail, the idea was to investigate several Black-Box approaches; mainly used to deduce prediction models from a relevant database. This category does not require any information about physical phenomena but are based on a function deduced only by means of sample data connected to each other and which describes the behaviour of a specific system. Therefore, it is fundamental the presence of a suitable and well-set database that characterise the problem, so that the output data are strongly related to one or more input data. The completely absence of this information and the great difficulty in finding data, has led to the creation of a basic energy database which, under certain hypotheses, is representative of a specific building stock. For this reason, in the first step of this research was developed a generic building energy database that in a reliable way, and underlining the main features of the thermal balance, issues information about the energy performances. In detail, two energy building databases representative of a non-residential building-stock located in the European and Italian territory have been created. Starting from a well-known and calibrated Base-Case dynamic model, which simulates the actual behaviour of a non-residential building located in Palermo, it was created an Ideal Building representative of a new non-residential building designed with high energy performances in accordance whit the highest standard requirements of the European Community. Taking into consideration the differences existing in the regulations and technical standards about the building energy performance of various European countries, several detailed dynamic simulation models were developed. Moreover, to consider different climatic characteristics, different locations were evaluated for each country or thermal zone which represent the hottest, the coolest and the mildest climate. The shape factor of buildings, which represents the ratio between the total of the loss surfaces to the gross heated volume of a building, was varied from 0.24 to 0.90. To develop a representative database where the data that identify the building conditions are the inputs of the model linked to an output that describes the energy performances it was decided to develop a parametric simulation. In detail different transmittance values, boundary conditions, construction materials, and energy carriers were chosen and employed to model representative building stocks of European and Italian cities for different climatic zones, weather conditions, and shape factor; all details and the main features are described in Chapter B.   These two databases were used to investigated three alternative methods to solve the building thermal balance; these are: • Multi Linear Regression (MLR): identification of some simple correlations that uses well known parameters in every energy diagnosis [18–20]; • Buckingham Method (BM): definition of dimensionless numbers that synthetically describe the relationships between the main characteristic parameters of the thermal balance [21]; and • Artificial Neural Network (ANN): Application of a specific Artificial Intelligence (AI) to determine the thermal needs of a [22] building. These methods, belonging to the Black-Box category, permit solving a complex problem easier with respect to the White-Box methods because they do not require any information about physical phenomena and expert user skills. Only a small amount of data on well-known parameters that represent the thermal balance of a building is required. The first analysed alternative method was the MLR, described in Chapter C. This approach allowed to develop a simple model that guarantees a quick evaluation of building energy needs [19] and is often used as a predictive tool. It is reliable and, at the same time, easy to use even for a non-expert user since an in-depth knowledge in the use phase is not needed, and computational costs are low. Moreover, the presence of an accurate input analysis guarantees greater speed and simplicity in the data collection phase [23]. The basis for this model is the linear regression among the variables to forecast and two or more explanatory variables. The feasibility and reliability of MLR models is demonstrated by the publication of the main achieved results in international journals. At first, the MLR method was applied on a dataset that considered heating energy consumptions for three configurations of non-residential buildings located in seven European countries. In this way, it was developed a specific equation for each country and three equations that describe each climatic region identified by a cluster analysis; these results were published in [19]. In a second work [18], it was applied the same methodology to a set of data referring to buildings located in the Italian peninsula. In this case, three building analysed configurations, in accordance to Italian legislative requirements regarding the construction of high energy performance buildings, have been employed. The achievement of the generalised results along with a high level of reliability it was achieved by diversifying each individual model according to its climate zone. It was provided an equation for each climate zone along with a unique equation applicable to the entire peninsula, obviously with different degrees of reliability. An improved version of the latest work concerning the Italian case study appeared in the paper published in [20]. The revised model provided an ability to predict the energy needs for both heating and cooling. Furthermore, to simplify the data retrieval phase that is required for the use of the developed MLR tool, an input selection analysis based on the Pearson coefficient has been performed. In this way the explanatory variables, needful for an optimal identification of thermal loads, have been identified. Finally, a comprehensive statistical analysis of errors ensured high reliability. The second analysed alternative method represents an innovative approach in developing a flexible and efficient tool in the building energy forecast framework. This tool predicts the energy performance of a building based some dimensionless parameters implemented through the application of the Buckingham theorem. A detailed description of the methodology and results is discussed in the Chapter D and is also published in [21]. The Buckingham theorem represents a key theorem of the dimensional analysis since it is able to define the dimensionless parameters representing the building balance [24]. These parameters define the relationships between the descriptive variables and the fundamental dimensions. Such a dimensional analysis guarantees that the relationship between physical quantities remains valid, even if there is a variation of the magnitudes of the base units of measurement [25]. The dimensional analysis represents a good model to simplify a problem by means of the dimensional homogeneity and, therefore, the consequent reduction in the number of variables. Therefore, this model works well with different applications such as forecasting, planning, control, diagnostics and monitoring in different sectors. The application of the BM for predicting the energy performance of buildings determined nine ad hoc dimensionless numbers. The identification of a set of criteria and a critical analysis of the results allowed to immediately determine thought the dimensionless numbers and without using any software tool, the heating energy demand with a reliability of over 90%. Furthermore, the validation of the proposed methodology was carried out by comparing the heating energy demand that was calculated by a detailed and accurate dynamic simulation. The last Black-Box examined model was the application of Artificial Neural Networks. The ANNs are the most widely used data mining models, characterised by one of the highest levels of accuracy with respect to other methods but generally have higher computational costs in the developing phase [26]. The design of a neural network, inspired by the behaviour of the human brain, involves the large number of suitably connected nodes (neurons) that, upon applications of simple mathematical operations, influence the learning ability of the network itself [27]. Also in this case, as described in Chapter E, this methodology was applied at the two different energy databases. In [22], the ANN was used to predict the demand for thermal energy linked to the winter climatization of non-residential buildings located in European context, while in another work under review, the ANN was used to determine the heating and cooling energy demand of a representative Italian building stock. The validation of the ANNs was carried out by using a set of data corresponding to 15% of the initial set which were not used to train the ANNs. The obtained good results (determination coefficient values higher than 0.95 and Mean Absolute Percentage Error lower than 10%) show the suitability of the calculation model based on the use of adaptive systems for the evaluation of energy performance of buildings. Simultaneously, a deep analysis of the investigated problem, underlines how to determine the thermal behaviour of a building trough Black-Box models, particular attention must be paid to the choice of an accurate climate database that along with thermophysical characteristics, strongly influence the thermal behaviour of a building [9]. In detail, to develop a predictive model of thermal needs, it is also necessary to pay close attention to the climate aspects. In the literature, many studies use the degree day (DD) to predict building energy demand, but this assessment, through the use of a climatic index, is correct only if its determination is a function of the same weather data used for the model implementation. Otherwise, the predictive model is generally affected by a greater evaluation error; all these aspects are deeply discussed analysing a specific Italian case study in Chapter F, and the main results are published in [8]. The results achieved during the three years of PhD research, make it possible to affirm that each model can be used to solve thermal building balance by knowing merely a few parameters representative of the analysed problem. Nonetheless, some questions may be asked: Which of these models can be identified as the most efficient solution? Is it possible to compare the performances of these models? Is it possible to choose the most efficient model based on some specific phase in the evaluation? To attempt to answer these questions, during the research period it was decided to compare the three selected alternative models by applying a Multi Criteria Analysis (MCA), that explicitly evaluates multiple criteria in decision-making. It is a useful decision support tool to apply to many complex decisions by choosing among several alternatives. The idea rising thanks to the scientific collaboration with the VGTU University of Vilnius, Lithuanian, in the person of Prof. A. Kaklauskas and Prof. L. Tupènaitè, experts in the field of multi-criteria analysis. At the first time a multi-criteria procedure was applied to determine the most efficient alternative model among some resolution procedures of a building’s energy balance. This application required extra effort in defining the criteria and identifying a team of experts. To apply the MCA, it was necessary to identify the salient phases of the evaluation procedure to explain the most sensitive criteria for acquiring conscious, truthful answers that only a pool of experts in the field can provide. Details of this work were carried out during the period of one-month research in Vilnius, from April to May 2019, where it was possible to improve the application of the Multiple Criteria Complex Proportional Evaluation (COPRAS) method for identifying the most efficient predictive tool to evaluate building thermal needs. These results are collected in Chapter G and the main results are explained in a paper under review in the Journal “Energy” from September. The identification of the most efficient alternative model to solve the building energy balance through the application of a specific MCA, allowed to deepen the identified methodology and improve research. In particular, the most efficient alternative resolution model was the subject of the research that took place during the research period at the RWTH in Aachen University, Germany with Prof. M. Traverso, Head of the INaB Department, from September 2018 to March 2019. The experience in the field of LCA and the possibility of identifying the environmental impacts linked to the building system, has led the research to investigate neural networks for a dual and simultaneous environmental-energy analysis. The results confirm that the application of ANNs is a good alternative model for solving the energy and environmental balance of a building and for ensuring the development of reliable decision support tools that can be used by non-expert users. ANNs can be improved by upgrading the training database and choosing the network structure and learning algorithm. The results of this research are collected in Chapter H and published in [28].

Le secteur du bâtiment est un consommateur énergétique majeur, par conséquent, un cadre d’actions a été décidé au niveau international dans le but de limiter son impact. Afin de mettre en œuvre ces mesures, il est nécessaire d’avoir à disposition des modèles offrants une description fiable du comportement thermique des bâtiments. A cet effet, cette thèse propose l’application d’une nouvelle technique guidée par les données pour la modélisation thermique des bâtiments en se basant sur l’approche des systèmes hybrides, caractérisés par des dynamiques continues et événementielles. Ce choix est motivé par le fait qu’un bâtiment est un système complexe caractérisé par des phénomènes non-linéaires et l’apparition de différents événements. On utilise les modèles affines par morceaux ou PWARX pour l’identification de systèmes hybrides. C’est une collection de sous-modèles affines représentant chacun une configuration caractérisée par une dynamique particulière. Le manuscrit commence par un état de l’art sur les principales techniques de modélisation thermique des bâtiments. Ensuite, le choix d’une approche hybride est motivé par une interprétation mathématique basée sur les équations d’un circuit thermique. Ceci est suivi par une brève présentation des modèles hybrides et une description détaillée de la méthodologie utilisée. On montre ensuite comment utiliser la technique SVM pour classifier les nouvelles données. Enfin, l’intégration des modèles PWARX dans une boucle de contrôle hybride afin d’estimer le gain en performance énergétique d’un bâtiment après rénovation est présentée. La méthodologie est validée en utilisant des données issues de cas d’études variés<br>The building sector is a major energy consumer, therefore, a framework of actions has been decided on by countries worldwide to limit its impact. For implementing such actions, the availability of models providing an accurate description of the thermal behavior of buildings is essential. For this purpose, this thesis proposes the application of a new data-driven technique for modeling the thermal behavior of buildings based on a hybrid system approach. Hybrid systems exhibit both continuous and discrete dynamics. This choice is motivated by the fact that a building is a complex system characterized by nonlinear phenomena and the occurrence of different events. We use a PieceWise AutoRegressive eXogeneous inputs (PWARX) model for the identification of hybrid systems. It is a collection of sub-models where each sub-model is an ARX equation representing a certain configuration in the building characterized by its own dynamics. This thesis starts with a state-of-the-art on building thermal modeling. Then, the choice of a hybrid system approach is motivated by a mathematical interpretation based on the equations derived from an RC thermal circuit of a building zone. This is followed by a brief background about hybrid system identification and a detailed description of the PWARX methodology. For the prediction phase, it is shown how to use the Support Vector Machine (SVM) technique to classify new data to the right sub-model. Then, it is shown how to integrate these models in a hybrid control loop to estimate the gain in the energy performance for a building after insulation work. The performance of the proposed technique is validated using data collected from various test cases

Combining computer fluid dynamic, CFD, models with finite element, FE, models to calculate temperature in fire exposed structures can reduce design temperatures in structures while still obtaining the level of structural fire safety stipulated by society. A better understanding of heat transfer and the concept of adiabatic surface temperatures, AST, the transition of data between models can be simplified and more accurate temperature predictions can be made.The thesis focuses on heat transfer calculations by employing AST in particular, and how this can be used as a means of coupling any CFD and FE-analysis code. The thesis presents a method for performing FE-analysis of the thermal response with input data calculated with the computer code FDS, Fire Dynamics Simulator. Parallel to this, the heat balance equation in FDS is tested and an alternate numerical algorithm is developed and tested.Firstly, a verification model is developed to test the radiative and convective part of the existing heat balance equation in FDS. An alternate numerical algorithm for calculation of the heat transfer at surfaces is developed as a more homogenous alternative for CFD codes.Secondly is a study on how to extract AST from an arbitrary point with direction in a CFD calculation using an infinitesimal surface. Instead of modeling numerous small surfaces for extracting AST, a post processor is developed to calculate AST independent of any modeled surface. For CFD codes, such as FDS that depend on a rectilinear grid, this enables calculation of AST in any direction, not only directions normal to the Cartesian planes.Finally, a comparison is made between different methods for calculating temperatures in steel with AST from numerical fire dynamics/modeling calculations. In this thesis there is a comparison between simplified Eurocode techniques, simple finite element analysis and advanced finite element analysis. This study shows the benefit of understanding heat transfer in numerical codes and to implement the concept of AST in a proper way.This way, the concept of combining numerical fire dynamics calculation with numerical (or simplified) thermal calculations can be better understood and implemented.<br>Godkänd; 2013; 20131010 (joasan); Tillkännagivande licentiatseminarium 2013-11-15 Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Joakim Sandström Ämne: Stålbyggnad/Steel Structures Uppsats: Thermal Boundary Conditions Based on Field Modelling of Fires Heat Transfer Calculations in CFD and FE Models With Special Regards to Fire Exposure Represented With Adiabatic Surface Temperatures Examinator: Professor Ulf Wickström, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet Diskutant: Teknologie doktor, Lektor Stephen Welch, the University of Edinburgh, UK Tid: Torsdag den 5 december 2013 kl 13.00 Plats: F1031, Luleå tekniska universitet

This study examines the potential to conduct building thermal performance simulation (BPS) of unconditioned low-cost housing during the early design stages. By creating a set of regression models (meta-models) based on EnergyPlus simulations, this research aims to promote and simplify BPS in the building envelope design process. The meta-models can be used as tools adapted for three Brazilian cities: Curitiba, São Paulo and Manaus, providing decision support to designers by enabling rapid feedback that links early design decisions to the buildings thermal performance. The low-cost housing unit studied is a detached onestory house with an area of approximately 51m2, which includes two bedrooms, a combined kitchen and living room, and one bathroom. This representative configuration is based on collected data about the most common residence options in some Brazilian cities. This naturally ventilated residence is simulated in the Airflow Network module in EnergyPlus, which utilizes the average wind pressure coefficients provided by the software. The parametric simulations vary the house orientation, U-value, heat capacity and absorptance of external walls and the roof, the heat capacity of internal walls, the window-to-wall ratio, type of window (slider or casement), and the existence of horizontal and/or vertical shading devices with varying dimensions. The models predict the resulting total degree-hours of discomfort in a year due to heat and cold, based on comfort limits defined by the adaptive method for naturally ventilated residences according to ANSI ASHRAE Standard 55. The methodology consists of (a) analyzing a set of Brazilian low-cost housing projects and defining a geometric model that can represent it; (b) determining a list of design parameters relevant to thermal comfort and defining value ranges to be considered; (c) defining the input data for the 10.000 parametric simulations used to create and test the meta-models for each analyzed climate; (d) simulating thermal performance using Energy Plus; (e) using 60% of the simulated cases to develop the regression models; and (f) using the remaining 40% data to validate the meta-models. Except by Heat discomfort regression models for the cities of Curitiba and São Paulo the meta-models show R2 values superior to 0.9 indicating accurate predictions when compared to the discomfort predicted with the output data from EnergyPlus, the original simulation software. Meta-models application tests are performed and the meta-models show great potential to guide designers decisions during the early design.<br>Esta pesquisa avalia as potencialidades do uso de simulações do desempenho térmico (SDT) nas etapas iniciais de projetos de habitações de interesse social (HIS) não condicionadas artificialmente. Busca-se promover e simplificar o uso de SDT no processo de projeto da envolvente de edificações através da criação de modelos de regressão baseados em simulações robustas através do software EnergyPlus. Os meta-modelos são adaptados ao clima de três cidades brasileiras: Curitiba, São Paulo e Manaus, e permitem uma rápida verificação do desconforto térmico nas edificações podendo ser usados como ferramentas de suporte às decisões de projeto nas etapas iniciais. A HIS considerada corresponde a uma unidade térrea com aproximadamente 51m2, composta por dois quartos, um banheiro e cozinha integrada à sala de jantar. Esta configuração é baseada em um conjunto de projetos representativos coletados em algumas cidades brasileiras (como São Paulo, Curitiba e Manaus). Estas habitações naturalmente ventiladas são simuladas pelo módulo Airflow Network utilizando o coeficiente médio de pressão fornecido pelo EnergyPlus. As simulações consideram a parametrização da orientação da edificação, transmitância térmica (U), capacidade térmica (Ct) e absortância () das paredes externas e cobertura; Ct e U das paredes internas; relação entre área de janela e área da parede; tipo da janela (basculante ou de correr); existência e dimensão de dispositivos verticais e horizontais de sombreamento. Os meta-modelos desenvolvidos fornecem a predição anual dos graus-hora de desconforto por frio e calor, calculados com base nos limites de conforto definidos pelo método adaptativo para residências naturalmente ventiladas (ANSI ASHRAE, 2013). A metodologia aplicada consiste em: (a) análise de um grupo de projetos de HIS brasileiras e definição de um modelo geométrico que os represente; (b) definição dos parâmetros relevantes ao conforto térmico, assim como seus intervalos de variação; (c) definição dos dados de entrada para as 10.000 simulações paramétricas utilizadas na criação e teste de confiabilidade dos meta-modelos para cada clima analisado; (d) simulação do desempenho térmico por meio do software EnergyPlus; (e) utilização de 60% dos casos simulados para o desenvolvimento dos modelos de regressão; e (f) uso dos 40% dos dados restantes para testar a confiabilidade do modelo. Exceto pelos modelos para predição do desconforto por calor para Curitiba e São Paulo, os demais meta-modelos apresentaram valores de R2 superiores a 0.9, indicando boa adequação das predições de desconforto dos modelos gerados ao desconforto calculado com base no resultado das simulações no EnergyPlus. Um teste de aplicação dos meta-modelos foi realizado, demonstrando seu grande potencial para guiar os projetistas nas decisões tomadas durante as etapas inicias de projeto.

Building performance simulations [BPS] tools are important in all the design stages, mainly in the early ones. However, some barriers such as time, resources and expertise do not contribute to their implementation in architecture offices. This research aimed to develop regression models (meta-models) to assess the thermal discomfort in a Brazilian low-cost house [LCH] during early design. They predicted the degree-hours of discomfort by heat and/or by cold as function of the design parameters changes for three Brazilian cities: Curitiba/PR, São Paulo/SP, and Manaus/AM. This work focused on using the meta-models to evaluate the impact of the parameters related to natural ventilation strategies on thermal performance in LCH. The analyzed Brazilian LCH consisted in a naturally ventilated representative unit developed based on the collected data. The most influential parameters in thermal performance, namely as key design parameters, were building orientation, shading devices positions and sizes, thermal material properties of the walls and roof constructive systems as well as window-to-wall ratios (WWR) and effective window ventilation areas (EWVA). The methodology was divided into: (a) collecting projects of Brazilian LCH, and based on that a base model that was able to represent them was proposed, (b) defining the key design parameters and their ranges, in order to compose the design space to be considered, (c) simulating thermal performance using EnergyPlus coupled with a Monte Carlo framework to randomly sample the design space considered, (d) using the greater part of the simulation results to develop the meta-models, (e)using the remaining portion to validate them, and (f) applying the meta-models in a simple design configuration in order to test their potential as a support design tool. Overall, the meta-models showed R2 values higher than 0.95 for all climates. Except for the regression models to predict discomfort by heat for Curitiba (R2 =0.61) and São Paulo (R2 =0.74). In their application, the models showed consistent predictions for WWR variations, but unexpected patterns for EWVA.<br>Simulações do desempenho de edificações são ferramentas importantes em todo processo de desenvolvimento do projeto, especialmente nas etapas iniciais. No entanto, barreiras como tempo, custo e conhecimento especializado impedem a implementação de tais ferramentas nos escritórios de arquitetura. A presente pesquisa se propôs a desenvolver modelos de regressão (meta-modelos) para avaliar o desconforto térmico em uma habitação de interesse social [HIS] brasileira. Estes meta - modelos predizem os graus-hora de desconforto por calor ou por frio em função de alterações nos parâmetros de projeto para três cidades brasileiras: Curitiba/PR, São Paulo/SP e Manaus/AM. O foco deste trabalho é o uso dos meta-modelos para avaliar o impacto de parâmetros relacionados com estratégias de ventilação natural no conforto térmico em HIS. A HIS brasileira analisada consistiu em uma unidade representativa, naturalmente ventilada e desenvolvida baseada em dados coletados. Os parâmetros que mais influenciam o conforto térmico, nomeados parâmetroschave de projeto foram: orientação da edificação, posição e tamanho das proteções solares, propriedades térmicas dos sistemas construtivos das paredes e do telhado, assim como, áreas de janela nas fachadas e áreas efetiva de abertura. A metodologia foi dividida em: (a) coleta de projetos de HIS brasileiras que embasaram a proposição de um modelobase que os representassem, (b) definição dos parâmetros chave de projeto e suas faixas de variação, a fim de compor o universo de projeto a ser explorado, (c) simulações térmicas usando o EnergyPlus acoplado com uma ferramenta de Monte Carlo para variar randomicamente o universo de projeto considerado, (d) uso da maior parte dos resultados das simulações para o desenvolvimento dos meta-modelos,(e) uso da porção remanescente para a validação dos meta-modelos e (f) aplicação dos meta-modelos em uma simples configuração de projeto, visando testar o seu potencial como ferramenta de suporte de projeto. De modo geral, os meta-modelos apresentaram R2 superiores a 0,95 para todos os climas, exceto os meta-modelos para predizer desconforto por calor para Curitiba (R2 =0,61) e São Paulo (R2 =0,74). Na fase de aplicação, os modelos mostraram predições consistentes para variações na área de janela na fachada, mas incoerências para variações nas áreas efetiva de abertura.

Building performance simulation (BPS) tools are significant and helpful during all design stages, especially during the early ones. However, there are obstacles to the full implementation and use of such tools, causing them not to become an effective part of the design process. In order to overcome this barrier, this research is presented, with the creation of regression models (meta-models) that allow to predict the discomfort by heat and/or by cold in a Brazilian low-cost house (LCH) in three distinct bioclimatic zones in Brazil, represented by the cities of Curitiba/PR, São Paulo/SP and Manaus/AM. The focus of this work was to analyze the impact of solar incidence and shading devices on thermal comfort by applying the meta-models. The method consisted in a) collecting data from projects referring to the type of building aforementioned to aid in the creation of the base model; b) definition of the key parameters and their ranges to be varied; c) simulations run on EnergyPlus using the Monte Carlo method to randomly create parameters combinations within their defined ranges; d) regression analysis and metamodels elaboration, followed by their validation with reliability tests; and lastly, e) a case study, consisting in applying the meta-models to a standard LCH to verify the impact of shading devices in a unit in regards to thermal comfort and the their potential as support tool in the design process. In general, all R2 values for the meta-models were above 0.95, except for the ones for São Paulo and Curitiba for discomfort by heat, 0.74 and 0.61, respectively. In regards to the case study, the meta-models predicted a decrease of approximately 50% in discomfort by heat for Manaus when a given combination of orientation, quantity and size of the devices was used. For the remaining locations, the meta-models predicting discomfort by heat and by cold require further investigation to properly assess some unexpected predictions and the meta-models sensitivity to the parameters related to shading devices.<br>Ferramentas de simulação computacional são importantes e uteis durante todas as etapas de projeto, especialmente durante as iniciais. No entanto. Há obstáculos para a completa implementação e uso de tais ferramentas, fazendo com que não sejam uma parte efetiva do processo de projeto. Para superar esta barreira, esta pesquisa é apresentada, com a criação de modelos de regressão (meta-modelos) que permitem a predição do desconforto por frio e/ou por calor em uma habitação de interesse social (HIS) no Brasil em três zonas bioclimáticas, representadas pelas cidades de Curitiba/PR, São Paulo/SP e Manaus/AM. O foco deste trabalho foi analisar o impacto da incidência solar e das proteções solares no conforto térmico utilizando os meta-modelos. O método consistiu em a) coletar dados referentes ao tipo de edifício mencionado para auxiliar na criação do modelo de base; b) a definição dos parâmetros chave e suas faixas de variação; c) simulações no EnergyPlus usando o método de Monte Carlo para aleatoriamente combinar valores de parâmetros dentro de suas faixas; d) análise de regressão e elaboração dos meta-modelos, seguida da validação dos mesmos por testes de confiabilidade; e por fim, e) um estudo de caso, consistindo na aplicação dos meta-modelos a uma HIS padrão para verificar o impacto das proteções solares em uma unidade em relação ao conforto térmico da mesma, assim como o potencial dos meta-modelos em serem utilizados como uma ferramenta de auxílio nas fases iniciais de projeto. No geral, todos os valores de R2 foram acima de 0.95, exceto para os meta-modelos de São Paulo e Curitiba para desconforto por calor, com 0.74 e 0.61, respectivamente. Em relação ao estudo de caso, os meta-modelos previram uma queda de aproximadamente 50% no desconforto por calor para Manaus, dada uma combinação entre orientação, quantidade e dimensão das proteções. Para as demais localidades, os meta-modelos prevendo desconforto por frio e por calor requerem maiores estudos para avaliar predições inesperadas e a sensibilidade dos meta-modelos em relação aos parâmetros de proteções solares.

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.