Tesi sul tema "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
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.
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.
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.
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.
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.

The South African economy, which is largely based on heavy industry such as minerals extraction and processing, is by nature very energy intensive. Based on the abundance of coal resources, electricity in South Africa remains amongst the cheapest in the world. Whilst the low electricity price has contributed towards a competitive position, it has also meant that our existing electricity supply is often taken for granted. The economic and environmental benefits of energy efficiency have been well documented. Worldwide, nations are beginning to face up to the challenge of sustainable energy - in other words to alter the way that energy is utilised so that social, environmental and economic aims of sustainable development are supported. South Africa as a developing nation recognises the need for energy efficiency, as it is the most cost effective way of meeting the demands of sustainable development. South Africa, with its unique economic, environmental and social challenges, stands to benefit the most from implementing energy efficiency practices. The Energy Efficiency Strategy for South Africa takes its mandate from the South African White Paper on Energy Policy. It is the first consolidated governmental effort geared towards energy efficiency practices throughout South Africa. The strategy allows for the immediate implementation of low-cost and no-cost interventions, as well as those higher-cost measures with short payback periods. An initial target has been set for an across sector energy efficiency improvement of 12% by 2014. Thermal and energy system simulation is globally recognised as one of the most effective and powerful tools to improve overall energy efficiency. However, because of the usual extreme mathematical nature of most simulation algorithms, coupled with the historically academic environment in which most simulation software is developed, valid perceptions exist that system simulation is too time consuming and cumbersome. It is also commonly known that system simulation is only effective in the hands of highly skilled operators, which are specialists in their prospective fields. Through previous work done in the field, and the design of a dynamic thermal and energy system simulation scheme for cross industry applications, it was shown that system simulation has evolved to such an extent that these perceptions are not valid any more. The South African mining and commercial building industries are two of the major consumers of electricity within South Africa. By improving energy efficiency practices within the building and mining industry, large savings can be realised. An extensive investigation of the literature showed that no general suitable computer simulation software for cross industry mining and building thermal and energy system simulation could be found. Because the heating, ventilation and air conditioning (HVAC) of buildings, closely relate to the ventilation and cooling systems of mines, valuable knowledge from this field was used to identify the requirements and specifications for the design of a new single cross industry dynamic integrated thermal and energy system simulation tool. VISUALQEC was designed and implemented to comply with the needs and requirements identified. A new explicit system component model and explicit system simulation engine, combined with a new improved simulation of mass flow through a system procedure, suggested a marked improvement on overall simulation stability, efficiency and speed. The commercial usability of the new simulation tool was verified for building applications by doing an extensive building energy savings audit. The new simulation tool was further verified by simulating the ventilation and cooling (VC) and underground pumping system of a typical South African gold mine. Initial results proved satisfactory but, more case studies to further verify the accuracy of the implemented cross industry thermal and energy system simulation tool are needed. Because of the stable nature of the new VISUALQEC simulation engine, the power of the simulation process can be further extended to the mathematical optimisation of various system variables. In conclusion, this study highlighted the need for new simulation procedures and system designs for the successful implementation and creation of a single dynamic thermal and energy system simulation tool for cross industry applications. South Africa should take full advantage of the power of thermal and energy system simulation towards creating a more energy efficient society.
Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2005.

The internal thermal climatic condition of a house is directly affected by how the building envelope (walls, windows and roof) is designed to suit the environment it is exposed to. The way in which the building envelope is constructed has a great affect on the energy required for heating and cooling to maintain human thermal comfort. Understanding how the internal climatic conditions react to the building envelope construction is therefore of great value. This study investigates how the thermal behaviour inside of a simple house reacts to changes made to the building envelope with the objective to predict how these changes will affect human thermal comfort when optimising the design of the house. A three-dimensional numerical model was created using computational fluid dynamic code (Ansys Fluent) to solve the governing equations that describe the thermal properties inside of a simple house. The geometries and thermophysical properties of the model were altered to simulate changes in the building envelope design to determine how these changes affect the internal thermal climate for both summer and winter environmental conditions. Changes that were made to the building envelope geometry and thermophysical properties include: thickness of the exterior walls, size of the window, and the walls and window glazing constant of emissivity. Results showed that there is a substantial difference in indoor temperatures, and heating and cooling patterns, between summer and winter environmental conditions. The thickness of the walls and size of the windows had a minimal effect on internal climate. It was found that the emissivity of the walls and window glazing had a significant effect on the internal climate conditions, where lowering the constant of emissivity allowed for more stable thermal conditions within the human comfort range.

Face à la nécessité de réduire les consommations énergétiques ainsi que les émissions de CO2 dans le secteur du bâtiment, nous voyons se succéder des réglementations thermiques de plus en plus strictes. Ainsi, en 2020, tous les bâtiments neufs devront être à énergie positive. Le recours à des stratégies passives, exploitant les ressources de l'environnement, est un point clé pour atteindre cet objectif.En climat méditerranéen, caractérisé des étés chauds et secs, la ventilation naturelle peut apporter un confort thermique acceptable si celle-ci est utilisée intelligemment. Son efficacité est cependant très dépendante des conditions météorologiques locales et peut varier grandement d'un site à l'autre. Malgré la simplicité de ce type de système, sa gestion peut également s'avérer complexe si l'utilisateur ne dispose pas d'informations suffisantes et n'est pas présent en permanence dans le bâtiment. Cela met en avant l'intérêt de disposer d'outils adaptés à son étude, ainsi que de proposer un pilotage simple et optimisé du bâtiment, basé sur le confort de l'occupant.Afin d'évaluer le potentiel de la ventilation naturelle sans avoir recours à une lourde campagne expérimentale ou à une phase de modélisation complexe, nous proposons tout d'abord des indicateurs climatiques permettant d'obtenir une première vue du site étudié.À partir d'une approche expérimentale et numérique en conditions réelles, nous nous intéressons ensuite à la problématique de la mesure dans les bâtiments ventilés naturellement et notamment à celle du débit d'air. L'instrumentation d'un bâtiment résidentiel de l'IESC, situé sur le site de l'Université de Corse et du CNRS, permet le développement et le test de différents modèles simplifiés et adaptés au cas d'étude. La partie aéraulique est traitée à l'aide d'outils statistiques tandis la partie thermique repose sur une modélisation par analogie électrique. Un cas d'application du modèle thermo-aéraulique ainsi développé est finalement proposé pour illustrer ses possibilités d'utilisation sur différents modes de gestion de la ventilation naturelle
The need to reduce energy consumption and CO2 emissions in buildings leads to more and more stringent thermal regulations succeeding one another. In 2020, all new buildings should be positive energy buildings producing more energy than they use. Passive strategies, exploiting the resources of the environment, are a key point to meet this objective.In Mediterranean climate, characterized by hot and dry summers, natural ventilation can provide thermal comfort when used wisely. However, its efficiency is highly dependent on local weather conditions and can vary greatly from one site to another. Despite the simplicity of this type of system, its operation can be complex if the user does not have sufficient information and is not always present in the building. This shows the interest of developing appropriate tools for its study and implementing a simple and optimized control on the building, based on occupant comfort.To assess the potential of natural ventilation without the need of complex experimental measurement or modelling, we propose first of all several climate indicators which can give a first view of a site.Then, based on full-scale experimentations and numerical studies, we focus on the problem of measurement in naturally ventilated buildings with particular attention to the airflow rate. The instrumentation of a residential building at IESC (University of Corsica and CNRS) allows to develop and to test simplified models adapted to the case study. The airflow rate is obtained by statistical tools and the thermal model is based on an electrical analogy. Finally, an application of the coupled thermal and airflow model is proposed to highlight its possibilities on different natural ventilation control modes

A smart home energy management system has been used to reshape the electricity demand of the residential buildings widely. It normally requires understanding the capability of residential buildings’ thermal mass which revisits to the temperature flatirons and providing enough energy buffers. In this project, phase change material (PCM) was used as the virtual thermal energy storage. Basically, two parts were included: thermal modelling of residential building with PCM layer. Secondly thermal behaviour of models under different conditions (heating, ventilation and air conditioning system, fenestration, solar radiation) is discussed. Some numerical methods for thermal modelling with EnergyPlus are also presented. A conduction finite difference algorithm in EnergyPlus are applied to calculate heat transfer between ambient and zone. The results indicate that PCM layers shift and decreased the indoor temperature during peak period. Also, solar radiation and fenestration can influence its performance. A model that is easily scalable in one thermal zone and convex as a function of the control inputs is derived based on energy balance equations. The indoor temperatures are treated as control inputs together with the cooling energy exchange with the virtual thermal storage. This simplifies the enforcement of comfort, which can be imposed through appropriate constraints on the control inputs. A convex constrained optimization program was formulated to address the optimal energy management, in order to minimize the electricity cost caused by Heating, Ventilation and Air Conditioning unit.

The thermal behaviour was investigated of three offices positioned in three buildings built in different periods, one academic institute built in 1920 and two modem commercial buildings in London. The buildings chosen for this study are the Rockefeller Building, which is part of University College London (UCL), Portman House in Oxford Street and the Visa Building in Paddington. Due to the lack of specific information related to the structure of the buildings such as windows, doors, building dimensions and other information that would allow the use of physical models, in this project black-box linear and non-linear mathematical models were used. Data relating to room temperature, hot and chilled water temperature, air flow and temperature from air handling units and outside temperature were collected for one year, from the actual building management systems (BMSs) installed in these buildings. The main assumption of the model development in the three buildings was that although occupancy, computers, printers etc cause an additional internal heat gain, their impact is in part indirectly included in the model. The primary objective of the analysis was to identify the inputs (independent variables) that gave good models for the prediction of room temperature for a certain period. Consequently, the process of input selection and period of validity in obtaining models that give good thermal prediction (within the same period) were the key points in season subdivision. The first part of the analysis applied the following linear parametric mathematical models to the three office buildings selected: Box Jenkins (BJ), autoregressivem oving averagew ith exogenousi nput (ARMAX) and output error (OE) structure. The project then deals with non-linear mathematical models. The same inputs selected and assumptions made with linear analysis were used to build, in turn, models with feedforward backpropagation (FFBP), non-linear autoregressive mathematical models with parallel arrangement (NARX) and series-parallel arrangement (NARXSP). The research presented in this project is related to developing models for three real offices positioned in three different buildings whereas previous researchers have applied these models mainly to experimental rooms and HVAC plants, with the purpose of fault detection and diagnostics. Furthermore, in the past, research on thermal model development has been related to one office or HVAC plant, and for a limited period of time (a few weeks or months). In contrast, this study undertakes an overall analysis of thermal model development for three offices and for a period of one year, where the process of input selection is given priority to obtain good models. Thus, previous studies have not utilized these two types of models for such a long period of data collection nor related them to three different buildings. Finally, model development and then validation were pursued utilizing the same week, different weeks and different days (where the first part of the data in each case was used for model estimation and the following part for model validation). This was one within the period that the models gave good results for the prediction of room temperature. The best mathematical models (linear and non-linear) that predict the room temperature, in terms of the inputs selected, has been determined for each season. The procedures for how to choose the best models are based on the following techniques: final prediction error (FPE for linear models), mean squared error (mse for non-linear models), and model fits and errors between measurements and simulated model output. Overall, the results related for the prediction of room temperature with non-linear models, are better than those obtained with linear models, as a result of comparison between models' errors, FPE and mse obtained with linear and non-linear models.

NANDRAD is a dynamic building energy simulation program. It calulates heating/cooling requirements and electric power consumption with respect to realistic climatic conditions and dynamic room usage. The model includes one-dimensional spatially resolved heat transport through multi-layered walls and thermal storage of solid components (room furniture/building walls). Consequently, massive constructions forms in the European area are very well represented. Further, NANDRAD calculates geometrical long radiation heat exchange inside the room. Heating systems may be modeled with a high level of geometrical detail, i.e. surface heating systems as part of the wall constructions and radiant heaters inside the room. NANDRAD can be applied for passive building simulation, energy optimization and thermal comfort analysis with respect to a very detailed building representation. In this terms, the model supports the simulation of a large number of zones and walls without need for subgrouping or other model reduction strategies.

Smart controls for heat pumps are required to harness the full energy flexibility potential of building thermal loads. A literature review revealed that most strategies used for this purpose can be classified in two categories: simpler rule-based control (RBC), and model predictive control (MPC), a more complex strategy based on optimization and requiring a prior model of the systems. Both RBC and MPC can use external penalty signals to prompt their actions. The price of electricity is most often used for this purpose, leading to strategies of cost reduction. As an alternative penalty signal, a novel marginal CO2 emissions signals was also conceived. In this thesis, both an RBC and an MPC controllers were developed as supervisory controls for an air-to-water heat pump supplying the heating and cooling needs of a residential building type from the Mediterranean area of Spain. The RBC strategy modulates the temperature set-points, while the MPC strategy minimizes the overall summed penalties (costs or emissions) due to the heat pump use, while balancing with comfort constraints and a proper operation of the systems. The MPC controller in particular required the development of a simplified model of the building envelope and of the heat pump performance, both adjusted differently for heating or cooling. The MPC included several novelties, such as the mixed-integer formulation, the heat pump simplified model based on experimental data and the consideration of its computational delay. The developed controllers were then tested, firstly in an experimental “hardware-in-the-loop” setup, with a real heat pump installed in the laboratory facilities, and connected to thermal benches that emulated the loads from a building model. Implementing the control strategies on a real heat pump enabled to highlight some practical challenges such as model mismatch in the MPC, communication issues, interfacing and control conflicts with the heat pump local controller. Secondly, a simulation-only framework was developed to test other configurations of the controllers, with TRNSYS as the main dynamic building simulation tool, coupled with MATLAB for the MPC controller. In that case, the real heat pump was replaced by a detailed model which was specially developed for this purpose. It is based on static tests performed in the laboratory, and therefore reproduces the dynamic behavior of the heat pump with high fidelity. The results from experimental and simulation studies revealed the ability of both types of controllers to shift the building loads towards periods of cheaper or less CO2-emitting electricity, these two objectives being in fact contradictory. In the cases where the reference control presented a large margin for improvements, the RBC and MPC controllers performed equally and provided important savings: around 15% emissions savings in heating mode, and 30% cost savings in cooling mode. In the cases where the reference control already performed close to optimally, the RBC controller failed to provide improvements, while the MPC benefitted from its stronger optimization and prediction features, reaching 5% cost savings in heating mode and 10% emissions savings in cooling mode. The research carried out in this thesis covered many aspects of energy flexibility in buildings: creation of input penalty signals, graphical representation of flexibility, development of controllers, performance in realistic experimental setup, fitting of appropriate models and compared performance in heating and cooling. The development efforts and barriers hindering the deployment of MPC controllers at large scale for building climate control have additionally been discussed. The performance of the developed controllers was evidenced in the thesis, proving their potential for load-shifting incentivized by different penalty signals: they could become a strong asset to unlock demand-side flexibility and in fine, help integrating a larger share of RES in the grid.
Para aprovechar todo el potencial de flexibilidad energética de las cargas térmicas en los edificios equipados con bombas de calor se requiere de sistemas de control inteligente. Una revisión bibliográfica ha revelado que la mayoría de las estrategias de gestión utilizadas para esta finalidad pueden ser clasificadas en dos categorías: control en base a reglas (RBC en inglés) o predictivo (MPC en inglés), basado en optimización y en el uso de modelos. Tanto RBC como MPC pueden utilizar señales externas de penalización para fundamentar sus decisiones. El precio de la electricidad es utilizado a este fin de forma habitual en estrategias de reducción de coste. Una nueva señal de emisiones marginales de CO2 fue también creada como alternativa. Se han desarrollado un controlador RBC y un MPC para sistemas de bombas de calor aire-agua que cubren las demandas de climatización y agua caliente en el ámbito residencial. El RBC modula las consignas de temperatura, y el MPC minimiza las penalizaciones totales del sistema, al mismo tiempo que se consideran restricciones operativas y de confort. En particular, el MPC ha requerido el desarrollo de nuevos modelos simplificados, para predecir la demanda del edificio y el rendimiento de la bomba de calor, tanto en modo calefacción como en modo refrigeración. Otras novedades añadidas en la configuración del MPC son la formulación entera mixta, y la consideración del retraso debido al tiempo de cómputo. Los controladores fueron testeados, primeramente, en un entorno experimental -hardware-in-the-loop-, con una bomba de calor real instalada en el laboratorio y conectada a unos bancos térmicos que emulan las cargas térmicas del edificio. El entorno experimental ha permitido poner de manifiesto algunos retos prácticos tales como la discrepancia en el modelo del MPC y conflictos de conexión con el controlador local de la bomba de calor. En segundo lugar, un entorno de simulación ha sido creado para testear diversas configuraciones, usando TRNSYS acoplado con MATLAB. Para ello, se ha desarrollado un modelo detallado de la bomba de calor, basado en ensayos realizados en laboratorio, que reproduce el comportamiento dinámico de la bomba de calor con alta fidelidad. Tanto los resultados experimentales como los simulados han revelado la capacidad de los dos tipos de control de desplazar las cargas del edificio hacia periodos donde la electricidad era más barata o había menos emisiones de CO2, estos dos objetivos presentando de hecho impactos contradictorios. En los casos donde el control de referencia presentaba un amplio margen de mejora, los controladores RBC y MPC han demostrado la capacidad de actuar eficientemente y proveer ahorros importantes: alrededor de un 15% de emisiones en modo calefacción, y de un 30% de coste en modo frío. En aquellos casos en el que el control de referencia actuaba de forma cercana a la óptima, los controladores RBC no han sido capaces de aportar mejoras significativas, mientras que el MPC ha demostrado la capacidad de conseguir ahorros de un 5% de coste en modo calefacción y de un 10% de emisiones en modo frío. La investigación realizada en esta tesis ha abarcado amplios aspectos de la flexibilidad energética en los edificios: la generación de señales de penalización, la representación gráfica del potencial de flexibilidad, el ajuste de modelos simplificados, el desarrollo de controladores, el ensayo en entorno experimental y de simulación, con la consecuente evaluación de su rendimiento comparado en periodos de invierno y de verano, así como una discusión de las barreras que dificultan la implementación de controladores MPC y RBC a gran escala. Finalmente, la tesis ha evidenciado el rendimiento de los controladores desarrollados si se formulan de forma adecuada, demostrando su potencial para el desplazamiento del consumo eléctrico en la edificación residencial con sistemas de bomba de calor respondiendo a diferentes señales de penalización. En conclusión, los sistemas propuestos pueden ser elementos muy valiosos para favorecer la necesaria flexibilidad de la demanda térmica en la edificación y posibilitar la integración de sistemas de generación renovables en la red

The world is constantly striving towards a more sustainable living, where every part of contribution is greatly appreciated. When it comes to heating of buildings, district heating is often the main source of heat. During specific times, peak demands are created by the tenants who are demanding a lot of heat at the same time. This demand peak puts a high load on the piping system as well as the need for certain peak boilers that run on non-environmental friendly peak fuel. One solution that is presented in this degree project that solves the time difference between production and demand is by utilizing thermal storage solutions. A dynamic district heated building model is developed with proper heat propagation in the pipelines, thermal inertia in the building and heat losses through the walls of the building. This is all done utilizing 4th generation district heating temperatures. Modelica is the tool that was used to simulate different scenarios, where the preheating of indoor temperature is done to mitigate the possibility for demand peaks. Using an already existing model, implementation and adjustments are done to simulate thermal storage and investigate its effectiveness in a 4th generation district heating system. The results show that short-term energy storage is a viable solution in concrete buildings due to high building mass. However, combining both 4th generation district heating with storage in thermal mass is shown not to be suitable due to low temperatures of supply water, which is not able to increase the temperature of the building’s mass enough.

1. Confronto fra i componenti termici ed elettrici sulla base delle conoscenze dei circuiti elettrici. Svilluppo di modelli termici tradizionali per le finestre e le pareti, realizzando la quantizzazione della perdita di calore di un edificio. Per quanto riguarda le teorie, i dati edi risultati di ricerche esistenti relativi al tempo, materiali di costruzione, termotecnica, astronomia e meteorologia, viene proposta la metodologia sulla stima oraria dell'energia solare e della radiazione atmosferica laddove l'approssimazione matematica risulta la più adatta al problema . 2. Definizione del concetto di "unit wall " basato sul miglioramento innovativo del modello di edificio tradizionale grazie all'ausilio della termocamera ad infrarossi e del quadricottero che consente di ottenere direttamente le informazioni sulla distribuzione della temperatura anziché dover ricorrere al calcolocome nel modello tradizionale, con il rischio di errori aggiuntivi. 3. Costruzione sistematica di un modello matematico per una termocamera ad infrarossi, basata sulla teoria dell'infrarosso termico e della radiazione, realizzando la conversione di file RAW originali a 14 bit. 4. Esecuzione della calibrazione dell'immagine, distorsione dell’obiettivo e la rettifica prospettica comprese, tramite la conoscenza della visione artificiale e dell'elaborazione delle immagini. Le finestre possono essere selezionate e rimosse con precisione dalle pareti usando il cursore in un'interfaccia utente grafica. La misurazione manuale delle dimensioni degli edifici può essereevitatagrazie all’utilizzo dei parametri dell'obiettivo e della distanza dell'oggetto. Infine, per convalidare l'applicabilità e l'accuratezza del modello, viene presentato il framework applicativo costituito dalla termocamera ad infrarossi Flir VUE Pro 640 trasportata da un quadricottero SAGA D600 alimentato dal Pixhawk firmware open source, seguito da esperimenti e test di moduli singoli.

The building sector is responsible for almost a quarter of the total carbon dioxide emissions. The urgency to reduce the emissions is reflected in the stricter guidelines which have been set all over the world. To reduce the building sector’s emissions the energy consumption need to be reduced, which can be done in two ways: building new energy efficient buildings or retrofitting of current buildings. Due to the life expectancy of current building stock the largest savings before 2030 will be made through retrofits. For this reliable computational tools are required, and currently there is a gap between the predicted and actual performance of retrofitted buildings. This thesis is going to look into how the computational method is contributing to the performance gap. A building at the RMIT campus in Melbourne, Australia, which is going to be retrofitted through retrofits designed by Siemens, is used. A thermal simulation model of the building was built, and tuned to reflect the pre-retrofit building, and compared against the measured energy performance of the building. The retrofits were then implemented in the simulation model and the gap in the predictions between the simpler computational method used by Siemens in designing the retrofits, and the extensive simulation model was compared. The gap between the computational methods were analysed in order to see how Siemens calculation method contribute to the performance gap. The conclusions which have been drawn are that the simulation model is reflecting the energy use of the building well considering the access of data available during the study. Especially the electricity use is reflected well both in the total annual use, approximately 4 % gap to measured value, and the monthly variation over the year. The total natural gas use is under predicting the annual use, approximately 40 % gap to the measured value, but shows a good correlation to the monthly variation. The electricity use is relatively stable in the simulation model, where the natural gas was sensitive for direct changes to the heating system. The input parameters which have the largest impact in the electricity use are internal gain profiles and the electrical internal gains energy use. Siemens calculation method are contributing to the performance gap through the lack of interaction between the different retrofits, the light retrofit have a noticeable impact on the heating and cooling system of the building. To only use one single period in the regression models can also easily lead to incorrect predictions. The strength of the simulation model is its ability to see the retrofits influence on each other and the possibility for scenario analysis.
Byggnadssektorn är ansvarig för nästan en fjärdedel av de totala globala koldioxidutsläppen. Viljan att minska utsläppen kan ses i de allt striktare riktlinjer som sätts över hela världen. För att reducera utsläppen finns det två sätt: bygga nya energieffektiva byggnader eller ombyggnation av nuvarande byggnader. Livslängden på nuvarande byggnadsbestånd innebär att de största besparingarna innan 2030 kommer att ske inom ombyggnationer. För detta krävs tillförlitliga verktyg, och i nuläget finns det ett gap mellan byggnaders förutspådda och verkliga energiprestanda. I denna examensuppsatts kommer beräkningsmetodens inflytande över detta gap att undersökas. En byggnad på RMIT:s campus i Melbourne, Australien, som kommer att undergå en ombyggnation som designats av Siemens har använts. En termisk simuleringsmodell av byggnaden skapades och avstämdes mot den verkliga byggnaden, och jämfördes mot uppmätta värden av byggnadens energiprestanda. Ombyggnationerna var sedan implementerade och skillnaden mellan den förutspådda prestandan av byggnaden, genom den omfattande simuleringsmodellen och den enklare beräkningsmetoden som användes av Siemens, jämfördes. Genom att analysera gapet mellan de olika beräkningsmetoderna kunde slutsatser dras angående hur de kan bidra till gapet i energiprestanda. Slutsatserna från arbetet är att simuleringsmodellen ger en bra bild av energianvändningen av byggnaden, med hänsyn till informationen som varit tillänglig. Byggnadens totala uppmätta elektricitetsanvändning är speciellt väl överrensstämmande med simuleringsmodellens resultat både i den årliga användningen, ca 4 % skillnad från uppmätta värden, och variationen över ett år. Den totala användningen av naturgas enligt simuleringsmodellen är under de uppmätta värdena med en skillnad på ca 40 %, men med en god överrensstämmelse med den årliga variationen. Användningen av elektricitet i modellen är relativt stabil, användningen av naturgas är känslig för direkta ändringar till uppvärmningssystemet. Inputparametrarna som har störst inverkan på elanvändningen är interna, energiproducerande och konsumerande, enheters användningsprofil (PC, personer, ljus m.m.), el konsumtion, och latenta samt sensibla värme. Siemens beräkningsmetod bidrar till gapet mellan förutspådda och verkliga energiprestanda genom brist på samverkan mellan de olika delarna i ombyggnationen. Ombyggnationen som innebär uppgradering av byggnadens belysning innebär exempelvis märkbara skillnader i byggnadens uppvärmnings- och kylsystem. Användningen av endast en period i skapandet av regressionsmodeller för att förutspå vattenkokarnas och kylarnas användning leder även till en missledande framtida energiproduktion. Styrkan i simuleringsmodellen är möjligheten till samverkan mellan olika ombyggnationer påverkan på varandra samt möjligheten till scenarioanalys.

At the present day, the need for the reduction of energy consumption is one of the main issues, from the technical, economic and environmental point of view. Buildings are responsible for more than 40% of energy utilization in European countries in 2017 [1]. Thus, actions that increase building energy efficiency are mandatory. Some interventions on the envelope and the internal operating conditions are addressed to the reduction of the heating and cooling loads of the building (i.e. the energy needs). Others pertain directly to the plants that must be properly selected and sized considering, if possible, also the use of renewable energies. In this framework, the present study is devoted to the analysis of energy-efficient buildings, with features aimed to reduce the loads and equipped with efficient plant solutions including innovative ground coupled water-to-water heat pumps and high efficiency air to air heat pump with energy recovery. The first part of the study is devoted to the ground heat exchangers and in particular to the modeling of energy geopiles in which the geothermal heat exchangers are integrated into the foundations of the building. To correctly size a ground heat exchanger (HE) field, in terms of total length, the number of HE and spacing, the ground response is needed and is provided in terms of g–function. A new semi-analytical method is proposed, based on the spatial superposition of a basic analytical solution, namely the single point source solution. This method allows generating ground response function (g-functions) for shapes of the heat exchanger different from classical linear one, as for the case of helix. The method has been validated by comparison with literature analytical solutions and with FEM simulations with Comsol Multiphysics. The second part of the research is devoted to developing a comprehensive model for dynamical energy simulations of a Nearly-Zero-Emission-Building. The model, developed with three different software (Sketch-Up, Openstudio and Energy Plus), represents the Smart Energy Building (SEB) located in the Savona Campus of the University of Genoa. The SEB is a very innovative building for both the envelope (ventilated facades) and the energy systems (i.e. geothermal heat pump and high efficiency air-to-air heat pump with energy recovery). Moreover, it has a complete monitoring system with numerous sensors that provide in real-time numerous thermal and electrical data (temperature, mass flow rates, electrical power, current, etc). All the detailed features of the building have been analyzed: the geometry, the materials, and the internal operating conditions. The climatic conditions of the site where the building is located are considered through a proper weather file. That information allows evaluating, firstly, the heating and cooling loads, which means the energy needs of the building during winter and summer. Then, the thermal plants have been introduced into the model, namely the ground coupled water-to-water heat pump and the air handler associated to a high efficiency air-to-air heat pump with energy recovery. For both the heat pumps, the performance (COP and EER) depends on the load and source-side fluid temperatures. This feature has been carefully implemented in the Energyplus model. The main results from the simulations are zone temperatures and primary energy consumption from the heating and cooling plants. Finally, the PV modules located on the roof of the SEB have been included in the model. The PV field has been analyzed considering electrical power production, cell temperature and solar irradiance received. The SEB is included in the complex and complete monitoring system of the Smart Polygeneration Microgrid of the Savona Campus The validation process of the model with real measurements from the SEB monitoring system would represent an important and original contribution of this study. Unfortunately, a complete analysis is not possible at the moment due to the unavailability of data series about the ventilation system. However, a preliminary comparison between model and measured data has been realized for the electrical production from the PV modules of the roof of the building. In particular, the EnergyPlus model has been updated by inserting a properly modified weather file with the measured values of outdoor air temperature and solar irradiance (global horizontal value). The calculation is done for two sample months (i.e. January and June 2018). The comparison shows a quite good agreement between simulated data trends and measured values, with a discrepancy at peak values. It is not clear if this disagreement is imputable to poor simulation parameter choice or errors in measures acquisition. Future work will be aimed towards completing the validation of the model using the huge amount of data from the monitoring system. Moreover, the model will be used to study the SEB thermal flexibility to different control strategies.

Today's energy production is changing from scheduled to intermittent generation due to the increasing energy injection from renewable sources. This alteration requires flexibility in energy generation and demand. Electric heat pumps and thermal storages were found to have a large potential to provide demand flexibility which is analysed in this work. A three-fold method is set up to generate thermal load profiles, to simulate heat pump pools and to assess heat pump flexibility. The thermal profile generation based on a combination of physical and behavioural models is successfully validated against measurement data. A randomised system sizing procedure was implemented for the simulation of heat pump pools. The parameter randomisation yields correct seasonal performance factors, full load hours and average operation cycles per day compared to 87 monitored systems. The flexibility assessment analysis the electric load deviation of representative heat pump pool in response to 5 different on / off signals. The flexibility is induced by the capacity of thermal storages and analysed by four parameters. Generally, on signals are more powerful than off signals. A generic assessment by the ambient temperature yield that the flexibility is highest for heating days and the activated additional space heating storage: Superheating of the storage to the maximal temperature provides a flexible energy of more than 400 kWh per 100 heat pumps in a temperature range between -10 and +13 °C.

Existing residential and small commercial buildings now represent the greatest opportunity to improve building energy efficiency. Building energy simulation analysis is becoming increasingly important because the analysis results can assist the decision makers to make decisions on improving building energy efficiency and reducing environmental impacts. However, manually measuring as-is conditions of building envelops including geometry and thermal value is still a labor-intensive, costly, and slow process. Thus, the primary objective of this research was to automatically collect and extract the as-is geometry and thermal data of the building envelope components and create a gbXML-based building geometry model. In the proposed methodology, a rapid and low-cost data collection hardware system was designed by integrating 3D laser scanners and an infrared (IR) camera. Secondly, several algorithms were created to automatically recognize various components of building envelope as objects from collected raw data. The extracted 3D semantic geometric model was then automatically saved as an industry standard file format for data interoperability. The feasibility of the proposed method was validated through three case studies. The contributions of this research include 1) a customized low-cost hybrid data collection system development to fuse various data into a thermal point cloud; 2) an automatic method of extracting building envelope components and its geometry data to generate gbXML-based building geometry model. The broader impacts of this research are that it could offer a new way to collect as is building data without impeding occupants’ daily life, and provide an easier way for laypeople to understand the energy performance of their buildings via 3D thermal point cloud visualization.

Comportement dynamique des batiments. La forme modale et les techniques de reduction du modele. Transformation de cette forme lorsque le modele du batiment est simplifie, en une expression analytique simple; cette propriete est utilisee pour rechercher une correlation entre un nombre restreint de parametres descriptifs d'un batiment dont un parametre continu d'inertie et ses besoins de chauffage

The aim of this study was to investigate the thermal performance of residential units in the Middle East Technical University (METU) Campus, Ankara. The study was conducted on the unoccupied residential units to eliminate the occupant interventions. There were only three unoccupied residential units in the study period, hence sample was considered as randomly selected. Case study units were triplex row houses and all physical characteristics were identical apart from their orientations. The thermal performance of these three residential units was assessed by compiling data on temperature and relative humidity from a number of their rooms on certain days in January and February. The study was conducted in winter months, because heating loads are more significant than cooling loads for energy consumption in Ankara
the measurement period was determined according to the coldest days of the year. In this context, the temperature and humidity charts were evaluated and one of the units was simulated using the software tool Ecotect v.5.20. The simulation temperature charts demonstrate similar behavior and trends as the measured temperature
although, it was approximately 4 0C lower than the measured temperature. The possible reason for such a difference may be the precision of the material properties. Six different calibrations were tested by changing the thermal properties of the envelope materials to obtain comparable results with the measured temperature readings. Based on the calibrated model, it was found that an increase in the U-value of the envelope materials did not have a significant effect on the simulated temperature charts.

Dans le contexte environnemental et économique actuel, il est primordial que chaque individu ait les clefs pour maitriser ses consommations de chauffage tout en maintenant un niveau de confort satisfaisant. Cependant, des freins peuvent limiter cette maitrise de l’énergie et du confort. Par exemple, dans les immeubles en chauffage collectif en France, la répartition des charges entre les logements se fait traditionnellement au prorata de la surface, ce qui peut décourager l’adoption et le maintien de comportements sobres en énergie. Pour y remédier, la loi Élan vise à individualiser les frais de chauffage pour inciter les foyers à économiser de l’énergie. Cependant, des études menées sur certains logements et bâtiments ont montré que la répartition des frais de chauffage n’est pas le seul frein à davantage de sobriété énergétique. Ainsi, cette thèse s’est d'abord attachée à identifier et à comprendre les freins à une meilleure maîtrise des consommations de chauffage et du confort dans les logements français équipés de chauffage collectif, représentant environ six millions de foyers. Pour y parvenir, une enquête a été réalisée. Les résultats ont montré que les factures de chauffage sont souvent difficiles à consulter et à comprendre, tout comme la répartition des frais de chauffage. De plus, les répondants ont exprimé le besoin d’informations supplémentaires, telles que des conseils personnalisés ou des données sur l’impact environnemental de leur consommation. Les résultats ont également révélé que l’absence de systèmes de programmation et de pilotage à distance des radiateurs dissuade les occupants d’adopter et de maintenir des comportements économes. Ensuite, les travaux de cette thèse se sont concentrés sur la résolution de ces freins. Pour cela, une méthodologie a été élaborée afin d’améliorer à la fois l’environnement technique et informatif des usagers. Traditionnellement, pour inciter les individus à économiser de l’énergie, une ou plusieurs interventions sont mises en place. Cependant, peu d’études proposent une approche permettant de cibler les interventions pertinentes en fonction des caractéristiques des individus. Pour déterminer les interventions à employer, un modèle de changement de comportement a été utilisé en raison de son adaptation à la gestion de l’énergie. L’évaluation des facteurs psychosociaux constituant ce modèle a permis d’identifier les interventions les plus appropriées pour chaque individu. De plus, les informations fournies dans le cadre de ces interventions ont été personnalisées en fonction des habitudes et des centres d’intérêt des occupants, afin de les encourager à les consulter. Pour améliorer la commodité d’usage de l’environnement technique, des robinets thermostatiques connectés ont été installés, permettant un pilotage et une programmation à distance des radiateurs à faible coût. Ces dispositifs transmettent des données sur la température ambiante et la consigne appliquée par les usagers, permettant ainsi de fournir des conseils personnalisés sur l’utilisation du chauffage. Ces conseils incluaient également une estimation des économies réalisables en suivant les recommandations. Pour calculer de manière fiable ces économies potentielles ainsi que celles déjà réalisées, des modèles de simulation thermique dynamique ont été développés. Enfin, la méthodologie a été expérimentée sur deux familles durant la période de chauffe 2023-2024. L’évaluation de cette expérimentation a pris en compte l’évolution des facteurs psychosociaux, les changements de comportement, les économies d’énergie réalisées, ainsi que l’évolution du confort des usagers
In the current environmental and economic context, it is crucial that each individual has the tools to manage their heating consumption while maintaining a satisfactory level of comfort. However, obstacles can limit this control over energy and comfort. For example, in buildings with collective heating in France, the distribution of charges between apartments is traditionally based on the proportional surface area, which may discourage the adoption and maintenance of energy-efficient behaviours. To address this, the Élan law aims to individualize heating costs to encourage households to save energy. However, studies conducted on certain dwellings and buildings have shown that the distribution of heating costs is not the only obstacle to greater energy efficiency.Thus, this thesis first aimed to identify and understand the barriers to better control of heating consumption and comfort in French dwellings equipped with collective heating, representing around six million households. To achieve this, a survey was conducted. The results showed that heating bills are often difficult to access and understand, as is the distribution of heating costs. Additionally, respondents expressed the need for additional information, such as personalized advice or data on the environmental impact of their consumption. The results also revealed that the absence of remote programming and control systems for radiators discourages occupants from adopting and maintaining energy-efficient behaviours.Next, the work of this thesis focused on overcoming these barriers. To this end, a methodology was developed to improve both the technical and informational environment for users. Traditionally, to encourage individuals to save energy, one or more interventions are implemented. However, few studies propose an approach that targets relevant interventions based on individual characteristics. To determine the interventions to be used, a behavior change model was employed due to its suitability for energy management. The evaluation of the psychosocial factors constituting this model helped identify the most appropriate interventions for each individual. Moreover, the information provided as part of these interventions was personalized according to the occupants' habits and interests, encouraging them to engage with it. To improve the ease of use of the technical environment, connected thermostatic valves were installed, allowing remote control and programming of radiators at a low cost. These devices transmit data on the ambient temperature and the setpoint applied by users, enabling personalized advice on heating usage. This advice also included an estimate of the savings achievable by following the recommendations. To reliably calculate these potential savings, as well as those already achieved, dynamic thermal simulation models were developed.Finally, the methodology was tested on two families during the 2023-2024 heating period. The evaluation of this experiment considered changes in psychosocial factors, behaviour, comfort and energy savings achieved

It is widely recognised that one's ability of adaptation is remarkable and thermal comfort is significantly related to such adaptations. This study proposes an alternative method of predicting adaptive thermal comfort based on the availability of adaptations, in particular behavioural adaptations, which needs quantifications of individual adaptation processes and of interactions between them. The fundamental argument of this method is that exercising an adaptive behaviour leads to an increase in comfort temperature, which is termed adaptive increment in this study. Apart from adaptive increments, this method also determines a baseline thermal comfort temperature (the thermal comfort temperature without adaptations) and a correction factor that considers the factors affecting adaptive behaviours, based on which, the highest operative temperature at which people may still feel thermally comfortable. This may be applied in mixed mode (MM) buildings to achieve a higher air-conditioning (AC) setpoint which may lead to a significant reduction in cooling energy. This method is believed to be flexible in dealing with different environments with various levels of adaptations and likely to be advantageous over the steady-state and adaptive models in predicting thermal comfort temperature of an environment with abundant adaptive opportunities. This study also evaluates ways of promoting the use of adaptive opportunities. It explores how adaptive thermal comfort theories may be used for behaviour modelling and in turn be applied to enhance the energy performances and comfort levels of real buildings. To improve the feasibility of this method key effective adaptive behaviours are studied in detail through lab experiments and field studies. The lab experiment has found the adaptive increment of taking cold water to be 1.5°C which is more significant than the previous literature suggests. When all the studied adaptive behaviours are exercised, the overall adaptive increment is as high as 4.7°C. However, the research has identified some issues associated with the adaptive opportunities studied. These include the existence of constraints on the use of adaptive behaviours, the low availability of some effective adaptive opportunities, the low operation frequency of desk fans and the misuse of windows and AC systems. Despite this, the availability of more adaptive opportunities has been verified to be capable of increasing the highest operative temperature at which people may still feel thermally comfortable: the lab experiment shows that over 80% of the participants can still find it thermally comfortable at an operative temperature of 30°C on the condition that adequate adaptive opportunities are provided; the field study shows that the thermal comfort temperature of occupants increases by at least 1°C when desk fans and cool mats are available. Based on these analyses, it proposes an MM system which encourages occupants to exercise adaptive opportunities and improves both comfort levels and energy efficiency. Building performance simulation results show that the proposed MM system is effective in reducing the reliance on AC systems and promotes effective uses of windows and AC systems. By applying the MM system and the associated passive energy-saving strategies, an office can cut cooling energy by about 90% and the peak cooling load by over 80% during transitional seasons.

Questa tesi tratta della progettazione dei parametri e dell'ottimizzazione del modello di consumo energetico degli edifici sulla base di immagini ad infrarossi. I contenuti della ricerca si possono riassumere come segue: 1. Definizione ed implementazione di un nuovo modello semplificato di consumo di energia. In base alle conoscenze teoriche esistenti, il modello complesso di consumo energetico viene semplificato come funzione di correlazione con il consumo di energia delle pareti e con tutti gli altri consumi energetici. 2. Progettazione dei parametri del modello. Prendendo in considerazione la quantità dei dati, i requisiti di velocità di calcolo, nonché l'analisi empirica esistente del consumo di energia, viene selezionato il nonlinear least square method per risolvere il problema di progettazione del parametro. 3. Ottimizzazione del valore del parametro nel modello di consumo energetico. Il problema di ottimizzazione del modello di edificio è considerato come “knapsack problem” e risolto dalla programmazione dinamica. 4. Progettazione del sistema di acquisizione dati per il modello di consumo energetico. Le temperature delle pareti sono ottenute dalle telecamere ad infrarossi FLIR Vue Pro e FLIR One. Considerando le difficoltà nel misurare la temperatura delle pareti esterne del grattacielo, si prevede di collegare la telecamera a infrarossi al velivolo SAGA D600 come piattaforma di test. 5. Proposizione di un modello confermato in base a test sul campo. La sperimentazioneè suddivisa in due parti. La prima parte ha l’obiettivo di stimare il consumo di energia di un closed foam box per verificare il modello di parete. La seconda calcola il consumo energetico di un edificio situato nel sud-est di Shanghai utilizzandoil sistema di acquisizione dati sviluppatodalla tesi. E’ stato così dimostrato che il modello propostoè in grado di simulare l'andamento del consumo energetico dell'edificio e che i valori dei parametri sono ragionevoli.

District cooling companies enforce a large penalty based on peak demands, which current cooling methods do not address properly. Building developers are exploring alternatives methods to reduce the said peak demands. The use of Ice Thermal Storage is an nontraditional method within the Scandinavian countries, but has shown to be a method to peak shave as well as load shifting in other regions of the worlds. The goal of the thesis was to "investigate the potential of ice thermal storage for cooling demand and peak shaving for Beridarebanan 4, 11, 77". The energy simulation was accomplished using the building performance simulator software IES VE. As inputs to the simulation, building data from the renovation project and corresponding weather data were used. The resulting simulation model was validated against renovated data with differences of 3,3% and 41,9% for the heating and cooling loads, respectively. The large discrepancy within cooling was determined to be weighted heavily by cooling strategy implemented within the building. When similar cooling strategies were implemented results were consistent with one another. This validation was investigated on a building, zone, and room level to look for consistency. The resulting simulated heating and cooling demands from IES VE were input into a then created ice thermal storage controller within MS Excel. In all, with the stable electrical and district cooling prices, a payback of 12 years was calculated for a 4,5 MWh, 6 hour storage ITS system. Results also show that for a 6 hour storage capacity,the controller exceeded the 1 000 kW price tier 4 hours out of the entire year, making it an ideal storage size. Current Swedish Electrical Market incentivize peak shaving rather than energy saving, accounting for nearly 80% of the yearly savings. The margin for earning more for the energy savings has negative consequences for potentially exceeding the 1 000 kW cooling threshold.

Neste trabalho é analisado, por via numérica e experimental, o comportamento térmico e eléctrico de um sistema fotovoltaico/térmico integrado em edifício, recorrendo a material de mudança de fase para regularização da diferença de temperatura entre interior e exterior e para a estabilização da temperatura do módulo fotovoltaico. Foi realizado uma revisão da literatura sobre o tema. Um modelo de cálculo dos fenómenos de transferência de calor e massa foi desenvolvido, assim como da produção de energia eléctrica, e implementado em software de cálculo Matlab/Simulink®. Paralelamente foram conduzidos ensaios experimentais a fim de analisar o comportamento térmico do sistema e respectiva validação do modelo numérico. De modo a melhorar a eficiência total do sistema, foi aplicado um processo de optimização com o método dos algoritmos genéticos. Do estudo, conclui-se que o sistema pode alcançar uma eficiência máxima total de 64% na configuração de inverno e de 32% na configuração de verão; ABSTRACT: This work presents a numerical and experimental analysis of the thermal and electrical performance of a building integrated photovoltaic/thermal system (BIPV/T), with the use of phase change material for stabilize the temperature difference between indoors and outdoors and a rapid stabilization of the PV modules’ temperature. A literature review was conducted on the topic. A calculation model was developed of the heat and mass transfer phenomena, as well as a model of a photovoltaic module, which were implemented in Matlab/Simulink®. Experimental tests were performed to analyze the thermal performance of the system and the validation of the numerical model. To improve overall system efficiency, an optimization process with the method of genetic algorithms was applied. From the study, it is concluded that the system can achieve a maximum total efficiency of 64% with winter configuration and 32% with summer configuration.

Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units� coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants� thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants� thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB.
PHD
For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand.

The representation of the thermal behaviour of the building is achieved through a relatively simple dynamic model that takes into account the effects due to the thermal mass of the building components. The model of a intra-floor apartment has been built in the Matlab-Simulink environment and considers the heat transmission through the external envelope, wall and windows, the internal thermal masses, (i.e. furniture, internal wall and floor slabs) and the sun gain due to opaque and see-through surfaces of the external envelope. The simulations results for the entire year have been compared and the model validated, with the one obtained with the dynamic building simulation software Energyplus.

Urban energy system planning can play a pivotal role in the transition of urban areas towards energy efficiency and carbon neutrality. With the building sector being one of the main components of the urban energy system, there is a great opportunity for improving energy efficiency in cities if the spatio-temporal patterns of energy use in the building sector are accurately identified. A bottom-up engineering energy model of buildings, known as urban building energy model (UBEM), is an analytical tool for modeling buildings on city-levels and evaluating scenarios for an energy-efficient built environment, not only on the building-level but also on the district and city-level. Methods for developing an UBEM vary, yet, the majority of existing models use the same approach to incorporating already established building energy simulation software into the main core of the model. Due to difficulties in accessing building-specific information on the one hand, and the computational cost of UBEMs on the other hand, simplified building modeling is the most common method to make the modeling procedure more efficient. This thesis contributes to the state-of-the-art and advancement of the field of urban building energy modeling by analyzing the capabilities of conventional building simulation tools to handle an UBEM and suggesting modeling guidelines on the zoning configuration and levels of detail of the building models. According to the results from this thesis, it is concluded that with 16% relative difference from the annual measurements, EnergyPlus is the most suitable software that can handle large-scale building energy models efficiently. The results also show that on the individual building-level, a simplified single-zone model results in 6% mean absolute percentage deviation (MAPD) from a detailed multi-zone model. This thesis proposes that on the aggregated levels, simplified building models could contribute to the development of a fast but still accurate UBEM.

La stratégie de l’Union Européenne pour atteindre les objectifs climatiques, est d’augmenter progressivement la part d’énergies renouvelables dans le mix énergétique et d’utiliser l’énergie plus efficacement de la production à la consommation finale. Cela implique de mesurer les performances énergétiques du bâtiment et des systèmes associés, indépendamment des conditions climatiques et de l’usage, pour fournir des solutions efficaces et adaptées de rénovation. Cela implique également de connaître la demande énergétique pour anticiper la production et le stockage d’énergie (mécanismes de demande et réponse). L’estimation des besoins énergétiques et des performances énergétiques des bâtiments ont un verrou scientifique commun : l’identification expérimentale d’un modèle physique du comportement intrinsèque du bâtiment. Les modèles boîte grise, déterminés d’après des lois physiques et les modèles boîte noire, déterminés heuristiquement, peuvent représenter un même système physique. Des relations entre les paramètres physiques et heuristiques existent si la structure de la boîte noire est choisie de sorte qu’elle corresponde à la structure physique. Pour trouver la meilleure représentation, nous proposons d’utiliser, des simulations de Monte Carlo pour analyser la propagation des erreurs dans les différentes transformations de modèle et, une méthode de priorisation pour classer l’influence des paramètres. Les résultats obtenus indiquent qu’il est préférable d’identifier les paramètres physiques. Néanmoins, les informations physiques, déterminées depuis l’estimation des paramètres, sont fiables si la structure est inversible et si la quantité d’information dans les données est suffisante. Nous montrons comment une structure de modèle identifiable peut être choisie, notamment grâce au profil de vraisemblance. L’identification expérimentale comporte trois phases : la sélection, la calibration et la validation du modèle. Ces trois phases sont détaillées dans le cas d’une expérimentation d’une maison réelle en utilisant une approche fréquentiste et Bayésienne. Plus précisément, nous proposons une méthode efficace de calibration Bayésienne pour estimer la distribution postérieure des paramètres et ainsi réaliser des simulations en tenant compte de toute les incertitudes, ce qui représente un atout pour le contrôle prédictif. Nous avons également étudié les capacités des méthodes séquentielles de Monte Carlo pour estimer simultanément les états et les paramètres d’un système. Une adaptation de la méthode de prédiction d’erreur récursive, dans une stratégie séquentielle de Monte Carlo, est proposée et comparée à une méthode de la littérature. Les méthodes séquentielles peuvent être utilisées pour identifier un premier modèle et fournir des informations sur la structure du modèle sélectionnée pendant que les données sont collectées. Par la suite, le modèle peut être amélioré si besoin, en utilisant le jeu de données et une méthode itérative
The European Union strategy for achieving the climate targets, is to progressively increase the share of renewable energy in the energy mix and to use the energy more efficiently from production to final consumption. It requires to measure the energy performance of buildings and associated systems, independently of weather conditions and user behavior, to provide efficient and adapted retrofitting solutions. It also requires to known the energy demand to anticipate the energy production and storage (demand response). The estimation of building energy demand and the estimation of energy performance of buildings have a common scientific: the experimental identification of the physical model of the building’s intrinsic behavior. Grey box models, determined from first principles, and black box models, determined heuristically, can describe the same physical process. Relations between the physical and mathematical parameters exist if the black box structure is chosen such that it matches the physical ones. To find the best model representation, we propose to use, Monte Carlo simulations for analyzing the propagation of errors in the different model transformations, and factor prioritization, for ranking the parameters according to their influence. The obtained results show that identifying the parameters on the state-space representation is a better choice. Nonetheless, physical information determined from the estimated parameters, are reliable if the model structure is invertible and the data are informative enough. We show how an identifiable model structure can be chosen, especially thanks to profile likelihood. Experimental identification consists of three phases: model selection, identification and validation. These three phases are detailed on a real house experiment by using a frequentist and Bayesian framework. More specifically, we proposed an efficient Bayesian calibration to estimate the parameter posterior distributions, which allows to simulate by taking all the uncertainties into account, which is suitable for model predictive control. We have also studied the capabilities of sequential Monte Carlo methods for estimating simultaneously the states and parameters. An adaptation of the recursive prediction error method into a sequential Monte Carlo framework, is proposed and compared to a method from the literature. Sequential methods can be used to provide a first model fit and insights on the selected model structure while the data are collected. Afterwards, the first model fit can be refined if necessary, by using iterative methods with the batch of data

This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. HVAC on/off behaviors are simulated by using Mathematical Program with Complementary Constraints (MPCCs) and solved in near real-time with a nonlinear solver. Removing HVAC on/off as a discrete variable decreases potential solutions and consequently reduces solve time and increases the probability of reaching a more optimal solution. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day compared to a home with regular temperature control. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. Homes using this home energy optimization will contribute less to the grid peak load and therefore, improve grid stability and reduce the amplitude of load following cycles for utilities. This case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real-time, and mixed empirical / first principles machine learning methods to guide the model structure.

Ventilated hollow core slabs (VHCSs) have shown to reduce energy requirements in building heating and cooling applications, as they enhance the use of building thermal mass by increasing the contact between the ventilation air and the structure. This thesis aims to contribute towards the accurate modelling of VHCSs for evaluating their thermal performance for building cooling applications under hot climatic conditions. For this purpose, a numerical procedure is developed for the prediction of the thermal performance of VHCS units. A turbulence model suitable for this purpose is identified first by assessing the ability of five different turbulence models to predict the dimensionless velocity and temperature profiles as well as the Nusselt numbers in a horizontal pipe subjected to turbulent mixed convection conditions typical of VHCSs. The Standard k-ε model showed the best performance, and as such, it is adopted to model the thermal performance of a VHCS geometry for which experimental thermal responses are reported in the literature. The numerical predictions of local temperatures within the VHCS agreed well with the experimental measurements, and hence the Standard k-ε model is adopted here for the modelling of VHCSs. The validated numerical approach is firstly applied to evaluate the impact integrating various types of micro-encapsulated phase change materials in VHCSs on their daily thermal performance in terms of the slab’s cooling potential when ventilated at night using naturally cold air under two ideal room temperature conditions. Analysis is also carried out towards quantifying how the ‘inter-interconnections’ of the hollow cores contribute to the thermal behaviour of VHCSs. The geometry is then simplified to represent a segment of a standard VHCS to evaluate the ability of the slab’s geometric parameters and the inclusion of thin metal sheeting on its hollow core and bottom surfaces to improve the slab’s cooling potential in office building applications.

Past research has shown that natural ventilation can be used to satisfy upwards of 98% of the yearly cooling demand when utilized in the appropriate climate zone. Yet widespread implementation of natural ventilation has been limited in practice. This delay in market adoption is mainly due to lack of effective and reliable control. Historically, control of natural ventilation was left to the occupant (i.e. they are responsible for opening and closing their windows) because occupants are more readily satisfied when given control of the indoor environment. This strategy has been shown to be effective during summer months, but can lead to both over and under ventilation, as well as the associated unnecessary energy waste during the winter months. This research presents the development and evaluation of a model-based control algorithm for natural ventilation. The proposed controller is designed to modulate the operable windows based on ambient temperature, wind speed, wind direction, solar radiation, indoor temperature and other building characteristics to ensure adequate ventilation and thermal comfort throughout the year without the use of mechanical ventilation and cooling systems. A midrise student dormitory building, located in Portland OR, has been used to demonstrate the performance of the proposed controller. Simulation results show that the model-based controller is able to reduce under-ventilated hours to 6.2% of the summer season (June - September) and 2.5% of the winter (October - May) while preventing over-heating during 99% of the year. In addition, the model-based-controller reduces the yearly energy cost by 33% when compared to a conventional heat pump system. As a proactive control, model-based control has been used in a wide range of building control applications. This research serves as proof-of-concept that it can be used to control operable windows to provide adequate ventilation year-round without significantly affecting thermal comfort. The resulting control algorithm significantly improves the reliability of natural ventilation design and could lead to a wider adoption of natural ventilation in appropriate climate zones.

The energy crisis together with greater environmental awareness, has increased interest in the construction of low energy buildings. Fabric thermal storage systems provide a promising approach for reducing building energy use and cost, and consequently, the emission of environmental pollutants. Hollow core ventilated slab systems are a form of fabric thermal storage system that, through the coupling of the ventilation air with the mass of the slab, are effective in utilizing the building fabric as a thermal store. However, the benefit of such systems can only be realized through the effective control of the thermal storage. This thesis investigates an optimum control strategy for the hollow core ventilated slab systems, that reduces the energy cost of the system without prejudicing the building occupants thermal comfort. The controller uses the predicted ambient temperature and solar radiation, together with a model of the building, to predict the energy costs of the system and the thermal comfort conditions in the occupied space. The optimum control strategy is identified by exercising the model with a numerical optimization method, such that the energy costs are minimized without violating the building occupant's thermal comfort. The thesis describes the use of an Auto Regressive Moving Average model to predict the ambient conditions for the next 24 hours. A building dynamic lumped parameter thermal network model, is also described, together with its validation. The implementation of a Genetic Algorithm search method for optimizing the control strategy is described, and its performance in finding an optimum solution analysed. The characteristics of the optimum schedule of control setpoints are investigated for each season, from which a simplified time-stage control strategy is derived. The effects of weather prediction errors on the optimum control strategy are investigated and the performance of the optimum controller is analysed and compared to a conventional rule-based control strategy. The on-line implementation of the optimal predictive controller would require the accurate estimation of parameters for modelling the building, which could form part of future work.

Ce travail propose un modèle thermique dynamique des appareils électriques dans les bâtiments basse consommation. L'objectif de ce travail est d'étudier l'influence des gains thermiques de ces appareils sur le bâtiment. Cette étude insiste sur la nécessité d'établir un modèle thermique dynamique des appareils électriques pour une meilleure gestion de l'énergie du bâtiment et le confort thermique de ses habitants.Comme il existe des interactions thermiques entre le bâtiment et les appareils électriques, sources de chaleur internes au bâtiment, il est nécessaire de modéliser le bâtiment. Le bâtiment basse consommation est modélisé dans un premier temps par un modèle simple reposantl'étude d'une pièce quasi-adiabatique. Ensuite, dans le but d'établir le modèle des appareils électriques, ceux-ci sont classés en quatre catégories selon leurs propriétés thermiques et électriques. A partir de cette classification et du premier principe de la thermodynamique, un modèle physique générique est établi. Le protocole expérimental et la procédure d'identification des paramètres thermiques des appareils sont ensuite présentés. Afin d'analyser la pertinence du modèle générique appliqué à des cas pratiques, plusieurs appareils électriques utilisés fréquemment dans les résidences – un écran, un ordinateur, un réfrigérateur, un radiateur électrique à convection et un micro-onde – sont choisis pour étudier et valider ce modèle ainsi que les protocoles d'expérimentation et d'identification. Enfin, le modèle proposé est intégré dans le modèle d'un bâtiment résidentiel développé et validé par le CSTB. Ce modèle couplé des appareils et du bâtiment est implémenté dans SIMBAD, un outil de simulation du bâtiment. A travers cette simulation, le comportement thermique du bâtiment et la quantité d'énergie nécessaire à son chauffage sur une période hivernale, ainsi que l'inconfort thermique dû aux appareils électriques durant l'été, sont observés.Ce travail fournit des résultats quantitatifs de l'effet thermique de différents appareils électriques caractérisés dans un bâtiment basse consommation et permet d'observer leur dynamique thermique et leurs interactions. Finalement, cette étude apporte une contribution aux études de gestion de l'énergie des bâtiments à basse consommation énergétique et du confort thermique des habitants
This work proposes a dynamic thermal model of electrical appliances within low energy buildings. It aims to evaluate the influence of thermal gains of these appliances on the buildings and persuades the necessity of dynamic thermal modeling of electrical appliances for the energy management of low energy buildings and the thermal comfort of inhabitants.Since electrical appliances are one of the free internal heat sources of a building, the building which thermally interact with the appliances has to be modeled. Accordingly, a test room which represents a small scale laboratory set-up of a low energy building is first modeled based on the first thermodynamics principle and the thermal-electrical analogy. Then, in order to establish the thermal modeling of electrical appliances, the appliances are classified into four categories from thermal and electrical points of view. After that, a generic physically driven thermal model of the appliances is derived. It is established based also on the first thermodynamics principle. Along with this modeling, the used experimental protocol and the used identification procedure are presented to estimate the thermal parameters of the appliances. In order to analyze the relevance of the proposed generic model applied to practical cases, several electrical appliances which are widely used in residential buildings, namely a monitor, a computer, a refrigerator, a portable electric convection heater, and microwave are chosen to study and validate the proposed generic model and the measurement and identification protocols. Finally, the proposed dynamic thermal model of electrical appliances is integrated into a residential building model which was developed and validated by the French Technical Research Center for Building (CSTB) on a real building. This coupled model of the appliances and the building is implemented in a building energy simulation tool SIMBAD, which is a specific toolbox of Matlab/Simulink®. Through the simulation, thermal behavior and heating energy use of the building are observed during a winter period. In addition, thermal discomfort owing to usages of electrical appliances during a summer period is also studied and quantified.This work therefore provides the quantitative results of thermal effect of differently characterized electrical appliances within a low energy building and leads to observe their thermal dynamics and interactions. Consequently, it permits the energy management of low energy buildings and the thermal comfort of inhabitants in accordance with the usages of electrical appliances

Etude de l'optimisation de l'effet de diode thermique d'une structure pouvant servir de paroi d'un local. Definition d'une structure alveolaire a lamelles. Etude numerique approchee du comportement thermique journalier et annuel d'un local a structure a effet diode

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.

Le bâtiment doit s’adapter pour faire face aux conditions climatiques futures, notamment des périodes caniculaires plus fréquentes, plus longues et plus intenses. Afin de garantir le confort thermique des occupants, le rafraichissement naturel par ouverture de fenêtre constitue une solution bioclimatique, gratuite en carbone et en énergie. Or, son potentiel de réduction du besoin de rafraîchissement par climatisation dépend de la valeur du taux de renouvellement en air extérieur. Une estimation fiable de ce taux de renouvellement d’air pourrait promouvoir la prise en compte du bénéfice apporté par le rafraîchissement naturel, lors de la conception neuve, de la rénovation, ou pour le pilotage intelligent d’ouvrants.L’objectif de ces travaux de thèse est de développer une méthode de diagnostic in situ du taux de renouvellement d’air par ouverture de fenêtre, en bâtiments occupés, et sur la base d’une instrumentation non intrusive. Pour cela, nous implémentons la méthode des gaz traceurs, basée sur la production de CO2 métabolique par l’occupant. Une approche de résolution statistique, impliquant un filtre de Kalman, a été récemment introduite dans la littérature.Nous étudions le potentiel et les limites d’une telle méthode, à travers la réalisation d’une campagne expérimentale dans un bâtiment résidentiel test. Nous développons, en parallèle, un modèle de simulation thermique dynamique du cas d’étude, fournissant un banc de test numérique pour le développement de modèles statistiques. Une nouvelle formulation de modèle statistique est proposée et testée. Enfin, une fois le diagnostic pour différentes configurations d’ouvrants réalisé, nous calibrons un modèle prédictif du taux de renouvellement d’air, en proposant une approche originale de l’impact de la direction du vent
The building must adapt to face future climatic conditions, particularly more frequent, longer, and more intense heatwaves. To ensure the thermal comfort of occupants, natural cooling through window opening constitutes a bioclimatic solution that is carbon-free and energy-free. However, its potential to reduce the need for air conditioning depends on the value of the outdoor air change rate. A reliable estimation of this air change rate could promote the consideration of the benefits provided by natural cooling for new construction, for renovation, or for smart control of openings.The objective of this doctoral research is to develop an in-situ diagnostic method for the air change rate through window opening, in occupied buildings, based on non-intrusive instrumentation. To this end, we implement the tracer gas method, based on the metabolic CO2 production by the occupant. A statistical resolution approach, involving a Kalman filter, has been recently introduced in the literature.We investigate the potential and limitations of such a method through the execution of an experimental campaign in a test residential building. In parallel, we develop a building energy simulation model of the case study, providing a digital test bench for the development of statistical models. A new statistical model formulation is proposed and tested. Finally, once the diagnostics for different window configurations are completed, we calibrate a predictive model of the air change rate, proposing an original approach to account for the impact of wind direction

In terms of master’s thesis HAM-Tools library designed for MATLAB/Simulink was modified for the use in simulations of houses in the Czech Republic. Modified library and its parts were described in detail and tested by the simulation of the one-zone and two-zones models of the house. The simulations of models with same parameters were also realized in program TRNSYS. The corresponding results achieved in mentioned simulation tools were compared to each other. The one-zone model created by using HAM-Tools library is tested by the simulation of ventilating, heating, cooling, and sources of moisture. A demonstration of the practical use of the simulation is carried out in the thesis, namely by examining the influence of the insulation thickness on the thermal performance of the house (resp. its heat loss) on real atmospheric conditions. Among others, available resources of meteorological data are mentioned and compared to each other. The function for processing of the meteorological data to a file compatible with the HAM-Tools library was created. It was also created a material data file containing commonly used materials of building structures in the Czech Republic and their parameters.

La gestion de la demande en chauffage des bâtiments raccordés à des réseaux de chaleur s'effectue classiquement au moyen d’une courbe de chauffe : lorsque la température extérieure chute, la température de départ de l’eau alimentant le circuit de chauffage interne est relevée. Ce mode de contrôle, appelé régulation par loi d’eau, présente des atouts en termes de simplicité et de robustesse, mais ne tient pas compte de l'inertie thermique du bâtiment et ne permet donc pas une modulation de sa demande. La modulation de la demande en chauffage se définit comme l'action de contrôle consistant à modifier de manière stratégique les conditions de confort thermique dans le cadre d’une optimisation énergétique et/ou économique. Il s’agit d’une brique essentielle du contrôle flexible qui envisage le déplacement des charges et l’effacement des pics pour une meilleure efficacité de production favorisant la pénétration des énergies renouvelables et de récupération.Ces travaux de thèse visent à développer une stratégie de contrôle prédictif et flexible de la demande en chauffage, applicable à grande échelle dans les réseaux de chaleur.Tout d'abord, un simulateur thermique dynamique de bâtiment résidentiel, équipé de radiateurs hydrauliques connectés à une sous-station de réseau de chaleur, est développé. Il permet de définir plusieurs cas d’études de bâtiments représentatifs du parc résidentiel français et constitue l’environnement expérimental virtuel de nos travaux de recherche. Ensuite, une méthodologie permettant d’obtenir un modèle orienté-contrôle et d’ordre réduit de bâtiment avec son système de chauffage est proposée. Elle commence par la définition de la structure du modèle en se basant sur des connaissances physiques, puis consiste en l'identification des paramètres par optimisation méta-heuristique à l'aide des données générées par le simulateur. L'approche d'identification paramétrique évalue la possibilité de réaliser cette tâche en ne s’appuyant que sur des données disponibles au niveau de la sous-station, notamment en s’interdisant d’utiliser des mesures de température intérieure au bâtiment, donnée à caractère personnel présumée indisponible à grande échelle pour des raisons techniques, économiques et juridiques. Enfin, la stratégie de contrôle prédictif est implémentée. Elle permet la planification de la température de départ de l'eau de chauffage en fonction des prévisions météorologiques et des prix de l’énergie. Le contrôleur flexible s’appuie sur un problème d’optimisation linéaire sous contraintes, selon le principe de l’horizon fuyant. Il incorpore les équations linéarisées du modèle d’ordre réduit et calcule le compromis optimal entre coûts énergétiques et inconfort thermique, le degré de flexibilité de la demande en chauffage étant défini par l’intermédiaire de paramètres de réglage dédiés
In District Heating Systems (DHSs), buildings Space-Heating (SH) demand management conventionally relies on a heating curve: when the outdoor temperature drops, the internal SH system supply water temperature is raised. This control mode, referred to as Weather-Compensation Control (WCC), offers widely recognized assets in terms of simplicity and robustness. However, WCC does not account for the building thermal inertia, and consequently, it does not allow modulation of its demand. SH demand modulation is the control action of strategically altering the indoor thermal comfort conditions within an energetic and/or economic optimization framework. It is a key measure in flexible demand control strategies, which seek loads shifting and peaks shaving to allow sustainable commitment of energy resources in favour of renewable power penetration and waste heat recovery.The work presented in this thesis aims at developing a flexible Model Predictive Control (MPC) strategy for SH demand, applicable at large scale in DHSs.Firstly, a thermal dynamic simulator of a residential building with a radiator SH circuit connected to a DHS substation is developed. It allows the definition of multiple case study buildings, well-representative of the french residential stock, and constitutes the virtual experimental environment for our research. Then, a methodology to obtain a control-oriented Reduced-Order Model (ROM) for the building and its SH system is proposed. It starts by defining the ROM structure based on physical knowledge, and proceeds to parameters identification by meta-heuristic optimization using data generated by the simulator. The parametric identification approach evaluates the possibility of carrying out this task by relying solely on data available at the substation level, refraining from using indoor temperature measurements, personal data assumed to be unavailable at large scale for technical, economic and legal reasons. Finally, MPC is implemented to schedule the SH supply water temperature as function of weather forecasts and energy price variations. The flexible controller is designed to solve a constrained linear optimization problem according to the receding horizon principle. It embeds the linearized ROM equations within the problem formulation and makes an optimal trade-off between energy consumption costs and thermal discomfort, the degree of flexibility to modulate SH demand being defined through dedicated tuning parameters

Diversos delineamentos experimentais que são aplicados correntemente tomam como base experimentos agronômicos. Esses dados experimentais são, geralmente, analisados usando-se modelos que consideram uma variância residual constante (ou homogênea), como pressuposto inicial. Entretanto, esta pressuposição mostra-se relativamente forte quando se está diante de situações para as quais fatores ambientais ou externos exercem considerável influência nas medidas experimentais. Neste trabalho, são estudados modelos para a média e a variância, simultaneamente, com a variância estruturada de duas formas: (i) por meio de um preditor linear, que permite incorporar variáveis externas e fatores de ruído e (ii) por meio de efeitos aleatórios, que permitem acomodar tanto o efeito longitudinal quanto o efeito de superdispersão, no caso de medidas binárias repetidas no tempo. A classe de modelos lineares generalizados duplos (MLGD) foi aplicada a um estudo observacional que consistiu em medir a mortalidade de frangos de corte no fim da condição de espera pré-abate. Nesse problema, é forte a evidência de que alguns fatores influenciam a variabilidade, e consequentemente, diminuem a precisão das análises inferenciais. Outro problema agronômico relevante, associado à horticultura, são os experimentos de cultura de tecidos vegetais, em que o número de explantes que regeneram são contados. Como esse tipo de experimento apresenta um grande número de parâmetros a serem estimados, comparado ao tamanho da amostra, os modelos existente podem gerar estimativas questionáveis ou até levar a conclusões erroneas, uma vez esse que são baseados em grandes amostras para se fazer inferência estatística. Foi proposto um modelo linear generalizados duplo, para os dados de proporções, de uma perspectiva Bayesiana, visando a análise estatística sob pequenas amostras e a incorporação do conhecimento especialista no processo de estimação dos parâmetros. Um problema clínico, que envolve dados binários medidos repetidamente no tempo é apresentado e são propostos dois modelos que acomodam o efeito da superdispersão e a dependência longitudinal das medidas, utilizandos-se efeitos aleatórios. Foram obtidos resultados satisfatórios nos três problemas estudados. Os MLGD permitiram identificar os fatores associados à mortalidade das aves de corte, o que permitirá minimizar perdas e habilitar os processos de manejo, transporte e abate aos critérios de bem-estar animal e exigências da comunidade européia. O MLGD Bayesiano permitiu identificar o genótipo associado ao efeito de superdispersão, aumentando a precisão da inferência de seleção de variedades. Dois modelos combinados foram propostos logit-normal-Bernoulli-beta e o probit-normal-Bernoulli-beta, que acomodaram satisfatoriamente a superdispersão e a dependência longitudinal das medidas binárias. Esses resultados reforçam a importância de se modelar a média e a variância conjuntamente, o que aumenta a precisão na pesquisa agronômica, tanto em estudos experimentais quanto em estudos observacionais.
Several experimental designs that are currently applied are based on agricultural experiments. These experimental data are, usually, analised with statistical models that assume constant residual variance (or homogeneous), as basic assumption. However, this assumption shows hard to stand for, when environmental or external factors exert strong influence over the measurements. In this work, we study the joint modelling for the mean and the variance, the latter being structured on two ways: (i) through a linear predictor, which allows the incorporation of external variables and/or noise factors and (ii) by the use of random effects, that accommodate jointly the possible overdispersion effect and the dependence of longitudinal data in the case of binary measusurements taken over time. The class of double generalized linear models (DGLM) was applied to an observational study where the poultry mortality was measured in the preslaughter operations. With this situation, it can be observed that there is a strong influence from some environmental factors over the variability observed, and consequently, this reduces the precision of the inferential analysis. Another relevant agricultural problem, related to horticulture, is the tissue culture experiments, where the number of regenerated explants is counted. Usually, this kind of experiment use a large number of parameters to be estimated, when compared with the sample size. The current frequentist models are based on large samples for statistical inference and, under this experimental condition, can generate unreliable estimates or even lead to erroneous conclusions. A double generalized linear model was proposed to analyse proportion data, under the Bayesian perspective, which can be applied to small samples and can incorporate expert knowledge into the parameter estimation process. One clinical research, that measured binary data repeatedly through the time is presented and two models are proposed to fit the overdispersion effect and the dependence of longitudinal measurements, using random effects. It was obtained satisfactory results under these three problems studied. the DGLM allowed to identify factors associated with the poultry mortality, that will allow to minimize loss and improve the process, since the catching until lairage on slaughterhouse, agreeing with animal welfare criteria and the European community rules. The Bayesian DGLM allowed to identify the genotype associated with the overdispersion effect, increasing the precision on the inference about varieties selection. Two combined models were proposed, a logit-normal- Bernoulli-beta and a probit-normal-Bernoulli-beta, which have both addressed the overdispersion effect and the longitudinal dependence of the binary measurements. These results reinforce the importance to modelling mean and dispersion jointly, as a way to increase the precision of agricultural experimentation, be it on experimental studies or observational studies.

Cette thèse s’inscrit dans le contexte du développement de Bâtiments Basse Consommation. La conception de telles constructions les rend sensibles aux sollicitations internes. Aussi, les outils de thermique du bâtiment existants ne sont pas adaptés pour simuler assez fidèlement ce type de bâtiments, si bien qu’un modèle tridimensionnel et dynamique a été développé ici. Celui-ci présente plusieurs particularités : il s’appuie sur une discrétisation spatiale optimisée des parois, la tache solaire y est localisée et l’intégration des dynamiques des conditions environnementales est assurée par un solveur numérique à pas de temps adaptatif et un seul nœud d’air est considéré. La validation du modèle s’est suivant une confrontation avec des mesures en conditions réelles réalisées dans une cellule de BESTlab d’EDF R&D. Un suivi visuel de la tache solaire a permis de confirmer sa bonne localisation par notre modèle. Des mesures de température en surface complétées par des cartographies thermographiques ont été comparées aux champs de températures simulés, montrant une bonne concordance. Les comparaisons de températures d’air mesurées et simulées ont montré des résidus ne dépassant pas 1,5 ˚C, pour des erreurs moyennes de 0,5 ˚C. La pertinence des deux principales innovations du modèle a été ensuite démontrée : l’utilisation d’entrées échantillonnées à la minute associées à un solveur à pas de temps adaptatif permet de minimiser les erreurs de simulation : en mi-saison, les résidus maximaux sont respectivement de 1 ˚C et 2 ˚C pour des entrées à la minute et à l’heure. En hiver, les températures d’air simulées tendent à plus osciller autour de la consigne quand le pas d’échantillonnage des entrées s’allonge. Deux modèles unidimensionnels, représentatifs de modèles courants, M1D,sol diluant le rayonnement solaire sur le sol seul et M1D,parois le distribuant de façon homogène sur les parois au prorata de la taille de la tache solaire censée les frappées, ne dégradent que légèrement la précision des calculs de température d’air. Cependant, ces modèles 1D ne permettent pas de calcul des champs de températures sur les parois si bien qu’ils présentent des erreurs locales dépassant 20 ˚C aux endroits touchés par la tache solaire. Enfin en hiver, le modèle 3D permet de prédire des consommations de chauffage surestimées de 6,5 % quand M 1D,parois les surestime de 11 % et M1D,sol de 22 %. Les améliorations apportées par notre modèle ont été confirmées pour d’autres types de cellules. D’ailleurs des écarts plus importants entre M1D,sol et le modèle 3D ont été observés pour une cellule dont parois et sol ont des compositions très différentes, alors que l’orientation a aussi un impact. Ce travail confirme la nécessité de représenter plus finement les phénomènes physiques pour des locaux fortement isolés. Des améliorations sont à intégrer, comme la description de l’anisothermie de l’air
Low energy building constructions become sensitive to internal gains : any internal heating source has an impact on the envelope. Therefore, it is important to evaluate the performance of current transient thermal models when adapted to low energy buildings. This work describes a numerical model to simulate a single room, using a refined spatial three-dimensional description of heat conduction in the envelope but a single air node is considered. The model has been developed for environmental conditions that vary over short time-steps and has integrated the projection of solar radiation through a window onto interior walls : the sun patch. The validation of the model has been done through a detailed comparison between model and measurements. The in situ experiment has been carried out in one of the BESTlab cells (EDF R&D). The sun patch has been followed by a camera to validate its calculated position and surface. Temperature measurements by thermocouples and by thermal cameras have been compared to the models outputs. Differences between air and surface temperatures measured and simulated were never above 1.5 ˚C and mean errors reached 0.5 ˚C. The two innovations of the model have then be proven. Using minute wise weather data and inputs associated to an adaptative solver, enabled to pull down simulation errors : in May maximal differences rised from 1 ˚C to 2 ˚C for respectivelly one minute and hourly wise inputs. More important errors are seen in summer whereas in winter, air temperatures simulated tend to more fluctuate around the set up temperature when the sampling step gets longer. Two one dimensional models, close to traditional taken simulation tools, were used. Model M 1D,sol supposed the incoming radiation to reach only the floor. A 1D model with sun patch movement, called 1D,parois , was also used. These two models evaluated the air temperature with an acceptable error. However, their surface temperatures were still subject to important errors. Thus, for temperature surfaces evaluation, both 1D model presented differences up to 20 ˚C for surfaces touched by the sun patch. In winter, the 3D model can predict heating energy consumptions overestimated by 6.5 % when M 1D,parois overestimated them by 11 % and M1D,sol by 22 %. The improvements brought by our model have been proven also for other cells with different thermal masses. For these cells, differences between M1D,sol and the 3D model could reach 4.5 ˚C. Differences seemed to be more important for low thermal mass cells, and the orientation of the building had a strong impact. This work has confirmed the necessity of representing more accuratelly the descriptions of the enveloppe for strongly insulated rooms. To improve the model, the anisothermal hypotheses of the air should be considered