Os avanços tecnológicos das últimas décadas têm motivado o desenvolvimento dos edifícios inteligentes, visando à criação de ambientes mais confortáveis e seguros para os ocupantes, economicamente vantajosos para os proprietários e ambientalmente corretos. Considerando-se que nestes ambientes emergem novas formas de interação entre os usuários e os sistemas prediais, as quais não são adequadamente tratadas por técnicas convencionais de modelagem, torna-se necessário o estudo de novas soluções que abordem essas interações. Assim, este trabalho apresenta a proposta de uma abordagem sistemática para modelar e simular os sistemas de controle de edifícios inteligentes. Considera-se o sistema de controle como um sistema orientado por eventos discretos, no qual a comunicação entre os dispositivos que o constituem é realizada por meio da troca assíncrona de mensagens. Nesta abordagem, é utilizada a rede de Petri colorida para especificar as funcionalidades do edifício inteligente e a interação entre os dispositivos que constituem o seu sistema de controle. Assim, fornece-se um procedimento estruturado para desenvolver modelos que facilita a especificação do algoritmo de controle dos subsistemas do edifício inteligente. Para avaliar as principais características do procedimento proposto, foi apresentado um exemplo de aplicação que aborda a integração das funcionalidades de um sistema de telefonia distribuído e um sistema de vigilância predial. A abordagem de modelagem possibilitou a identificação das funcionalidades dos dispositivos inteligentes que integram o sistema de controle em diferentes níveis de abstração e as interações que ocorrem durante o seu funcionamento. A realização deste trabalho contribui para o aprimoramento de novas abordagens para o desenvolvimento de sistemas de controle com arquiteturas heterárquicas. Estes sistemas são constituídos por dispositivos inteligentes colaborativos, que possuem um elevado grau de autonomia. / Technological advances in recent decades have motivated the development of intelligent buildings, aimed at creating environments more productive for the occupants, economically advantageous for the owners and environmentally correct. New ways of interaction between users and the buildings systems are emerging from these kinds of systems, which are not adequately treated by conventional modeling techniques. In this sense, it is necessary the study of new approaches which address these new functionalities. Thus, this work presents a proposal for a systematic approach to model and simulate the control system of the intelligent buildings. The control system is considered to be a discrete event system, where the communication between the devices that integrate it is oriented by means of asynchronous messages exchange. This approach uses the colored Petri nets in order to specify the functionalities of the building system and their devices interactions. The approach provides a structured procedure to develop models that facilitate the algorithm specification of the control system. In order to verify the main characteristics of the proposed procedure, it is presented an example that is a control system that integrates a distributed telephony system and a surveillance building system. The approach proposed enabled the identification of the main functionalities and interactions of the intelligent devices constituting the control system. The achievement of this thesis contributes to the development of new approaches to develop heterarchical control system architectures. This kind of system architectures is constituted by collaborative intelligent devices that have a high degree of autonomy.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

This research explores the barriers and limitations of the interaction between building development processes in an attempt of an integrated decision support approach to improve building design for effective maintenance in the field of office building automation. Extensive coverage of literature and practice in office building industry over the last two decades indicates a wide diffusion and application of the information and communication technologies (ICT). While this has resulted in the adoption of advanced system integration in buildings, system redundancy and excessive expenditures are causing a major impact on the overall efficiency and has burdened building owners and occupiers with escalating maintenance costs. This phenomenon stimulates and warrants the re-examination of integrated building development, not just on system integration but also on the interdisciplinary development process integration particularly linking design and maintenance. Studies in this field revealed existing problems such as the inherent professional fragmentation, lack of historical information and service data, the first cost mentality of owners and developers, difficulties in forecasting future conditions and changes early in the design stage. With extensive use of qualitative information, this situation presents a great potential for the development of a decision support system exploring the communication and integration of design and maintenance phases, which has been one of the primary objectives of this research. In addition to literature studies, a questionnaire survey and a case study to identify industry concerns, feasible solutions, and practical procedure oriented approaches through knowledge extractions were carried out. A set of guidelines, a checklist for its implementation and prototype system for computerized decision support to design and maintenance of building automation systems were also produced. These strategic approaches to balance design and maintenance will help facilitate appropriate decision making in the early design stage for sustainable maintenance of buildings.

La simulation de systèmes d'énergie est un outil important pour la conception, le développement et l'évaluation de nouvelles architectures et des contrôles grille dans le concept de réseau intelligent pour les dernières décennies. Cet outil a évolué pour répondre aux questions proposées par les chercheurs et les ingénieurs dans les applications de l'industrie, et pour offrant des différentes alternatives pour couvrir plusieurs scénarios réalistes.Aujourd'hui, en raison des progrès récents dans le matériel informatique, la Simulation numérique en temps réel (DRTS) est utilisée pour concevoir des systèmes de puissance, afin de soutenir les décisions prises dans les systèmes de gestion de l'énergie automatisés (SME) et de réduire le délai de commercialisation de produits, entre des autres applications.Les simulations de réseaux électriques peuvent être classées dans les catégories suivantes: (1) la simulation analogique (2) hors simulation de ligne (3) de simulation entièrement numérique (4) la simulation rapide (5) Contrôleur Hardware-In-the-Loop (CHIL) et (6) Puissance Hardware-In-the-Loop (PHIL).Les dernière 3 sont axés sur la simulation Real-Time hardware-in-the-Loop (HIL RT-). Ces catégories portent sur les questions liées à Transitoires électromagnétiques (liste EMT), la simulation de phaseurs ou mixte (phaseur et EMT). Comme mentionné ci-dessus, ces progrès sont possibles en raison de l'évolution des architectures informatiques (matériels et logiciels); Cependant, pour le cas particulier de l'analyse des flux de puissance des réseaux de distribution (DS), il y a encore des défis à résoudre.Les architectures informatiques actuelles sont composées de plusieurs noyaux, laissant derrière lui le paradigme de la programmation séquentielle et conduisant les développeurs de systèmes numériques pour examiner des concepts comme le parallélisme, la concurrence et les événements asynchrones. D'autre part, les méthodes pour résoudre le flux de puissance dynamique des systèmes de distribution considérer le système comme un seul bloc; ainsi, ils utilisent une seule base pour l'analyse des flux de puissance, indépendamment de l'existence de plusieurs cœurs disponibles pour améliorer les performances de la simulation.Répartis dans des procédés en phase et de la séquence, ces procédés ont en caractéristiques communes telles que l'examen d'une seule matrice creuse pour décrire les DS et qu'ils peuvent résoudre simultanément une seule fréquence.Ces caractéristiques font dès les méthodes mentionné sont pas appropriées pour le traitement avec multiple noyaux. En conséquence, les architectures informatiques actuelles sont sous-utilisés, et dégrade la performance des simulateurs lors de la manipulation de grandes DS échelle, changer DS topologie et y compris les modèles avancés, entre autres des activités de la vie réelle.Pour relever ces défis Cette thèse propose une approche appelée A-Diakoptics, qui combine la puissance de Diakoptics et le modèle de l'acteur; le but est de faire toute méthode classique d'analyse de flux d'énergie appropriée pour le traitement multithread. En conséquence, la nature et la complexité du système d'alimentation peuvent être modélisées sans affecter le temps de calcul, même si plusieurs parties du système d'alimentation fonctionnent à une fréquence de base différente comme dans le cas de micro-réseaux à courant continu. Par conséquent, l'analyse des flux de charge dynamique de DS peut être effectuée pour couvrir les besoins de simulation différents tels que la simulation hors ligne, simulation rapide, CHIL et PHIL. Cette méthode est une stratégie avancée pour simuler les systèmes de distribution à grande échelle dans des conditions déséquilibrées; couvrant les besoins de base pour la mise en œuvre d'applications de réseaux intelligents. / Simulation of power systems is an important tool for designing, developing and assessment of new grid architectures and controls within the smart grid concept for the last decades. This tool has evolved for answering the questions proposed by academic researchers and engineers in industry applications; providing different alternatives for covering several realistic scenarios. Nowadays, due to the recent advances in computing hardware, Digital Real-Time Simulation (DRTS) is used to design power systems, to support decisions made in automated Energy Management Systems (EMS) and to reduce the Time to Market of products, among other applications.Power system simulations can be classified in the following categories: (1) Analog simulation (2) off line simulation (3) Fully digital simulation (4) Fast simulation (5) Controller Hardware-In-the-Loop (CHIL) simulation and (6) Power Hardware-In-the-Loop (PHIL) simulation. The latest 3 are focused on Real-Time Hardware-In-the-Loop (RT-HIL) simulation. These categories cover issues related to Electromagnetic Transients (EMT), phasor simulation or mixed (phasor and EMT). As mentioned above, these advances are possible due to the evolution of computing architectures (hardware and software); however, for the particular case of power flow analysis of Distribution Systems (DS) there are still challenges to be solved.The current computing architectures are composed by several cores, leaving behind the paradigm of the sequential programing and leading the digital system developers to consider concepts such as parallelism, concurrency and asynchronous events. On the other hand, the methods for solving the dynamic power flow of distribution systems consider the system as a single block; thus they only use a single core for power flow analysis, regardless of the existence of multiple cores available for improving the simulation performance.Divided into phase and sequence frame methods, these methods have in common features such as considering a single sparse matrix for describing the DS and that they can solve a single frequency simultaneously. These features make of the mentioned methods non-suitable for multithread processing. As a consequence, current computer architectures are sub-used, affecting simulator's performance when handling large scale DS, changing DS topology and including advanced models, among others real life activities.To address these challenges this thesis proposes an approach called A-Diakoptics, which combines the power of Diakoptics and the Actor model; the aim is to make any conventional power flow analysis method suitable for multithread processing. As a result, the nature and complexity of the power system can be modeled without affecting the computing time, even if several parts of the power system operate at different base frequency as in the case of DC microgrids. Therefore, the dynamic load flow analysis of DS can be performed for covering different simulation needs such as off-line simulation, fast simulation, CHIL and PHIL. This method is an advanced strategy for simulating large-scale distribution systems in unbalanced conditions; covering the basic needs for the implementation of smart grid applications.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

The building industry makes up a considerable fraction of world&amp / #8217 / s energy consumption. The adverse effects of a growing energy demand such as depletion in fossil fuel reserves and natural resources hassled the building industry to a search for new technologies that result in less energy consumption together with the maximum utilization of natural resources. Energy- and ecology-conscious European countries incorporated the well-being of occupants while conducting research on innovative technologies. In view of the fact that double-skin fa&ccedil / ades offer a healthy and comfortable milieu for the occupants and use natural resources hence consume less energy they became a promising invention for all concerns. The analysis of the performance of the double-skin fa&ccedil / ades and energy consumption is inconclusive at this time. However, based upon thermal performance analysis have been done so far, a double-skin fa&ccedil / ade perform better and provide some energy reduction, particularly on the heating side cycle, from a standard double glazed unit wall. The aim of this study was to examine the relationship between double-skin fa&ccedil / ades and building management systems in intelligent office buildings as they relate to energy efficiency issues thus to find out whether or not the integration of these systems into intelligent buildings provides optimization in energy performance and comfort conditions. The building for the case study, which is an intelligent office building incorporating a double-skin fa&ccedil / ade was selected as one that promises high comfort conditions for the occupants with low energy consumption. The working principles of integrated fa&ccedil / ade systems, together with their advantages and disadvantages were investigated by means of the case study. It was concluded that due to their high initial costs, these systems offer no real advantages for today. However with the inevitable exhaustion of fossil fuels that is foreseen for the future, these systems would become an innovative solution in terms of energy conservation.

The first part of this diploma thesis is to design an energy-efficient hotel with restaurant and car parking. The second part is also to create an assessment of the energy performance of the building and usage of energy from renewable sources. Hotel is designed with the idea of „smart buildings“ which means, that all the technological background is controlled by the main computer, which also harvests data to achieve a minimum energy use and financial demand and maximum comfort and safety at the same time by controlling the connected technology. By placing on a sloped terrain in the center of Brno, the basement floor can be used as car parking. The roof of this building is flat, green, with modifications for placing solar panels, whose design is part of this work. The third part of this diploma thesis is the spatial acoustics of the conference room on the first ground floor.