Les bâtiments sont de plus en plus équipés avec des réseaux d’automatisation dédiés, visant à réduire la consommation d’énergie ainsi que d’optimiser le confort. D’un autre côté, nous observons l’arrivée de capteurs et actionneurs liés à l’Internet des objets, pouvant naturellement se connecter à des réseaux IP. Dû à des contraintes d’obsolescence ou imposées par les propriétés physiques des bâtiments, il n’est pas rare que différentes technologies doivent cohabiter. Celles-ci fonctionnant avec des modèles et protocoles différents rend le développement de systèmes d’automatisation globaux compliqué. Les modèles classiques de systèmes distribués ne sont pas adaptés aux problématiques des réseaux de capteurs. Le paradigme du Web des objets est basé sur des ressources et vise quand à lui d’uniformiser la couche applicative entre différents objets à l’aide des technologies du Web, essentiellement HTTP et REST. Dans cette thèse, nous nous basons sur le Web des objets afin de créer un framework dédié au bâtiments intelligents, permettant aux développeurs de concevoir des applications composites sans connaissances des différentes technologies sous-jacentes. Grâce aux technologies Web, nous pouvons offrir des services homogènes tout en profitant des ressources disponibles à l’intérieur du réseau (capteurs et actionneurs), formant un nuage auto-géré. Dans le but de doter les bâtiments d’une plus grande intelligence, l’apprentissage automatique, souvent réservé aux experts, est rendu accessible à travers des interfaces Web cachant la complexité des processus. / Buildings are increasingly equipped with dedicated automation networks, aiming to reduce the energy consumption and to optimize the comfort. On the other hand, we see the arrival of sensors and actuators related to the Internet of Things, which can naturally connect to IP networks. Due to constraints imposed by the obsolescence or physical properties of buildings, it is not uncommon that different technologies have to coexist. These networks operate with different models and protocols, making the development of global automation systems difficult. Traditional models of distributed systems are not adapted to the context of sensor networks. The paradigm of the Web of Things is resource-based and strives to standardize the application layer of different objects using Web technologies, primarily HTTP and REST. In this thesis, we use the Web of Things to create a framework dedicated to smart buildings, allowing developers to develop composite applications without knowledge of the underlying technologies. By relying on Web technologies, we can provide seamless service while reusing the available resources within the network (sensors and actuators), forming a self-managed cloud. In order to equip the buildings with a higher-level intelligence, machine learning, often reserved for experts, is made accessible through Web interfaces hiding the complexity of the process.

The objective of the proposed research is to develop an intelligent load modeling, identification, and prediction technology to provide granular load energy consumption and performance details and drive building energy reduction, demand reduction, and proactive equipment maintenance. Electricity consumption in commercial and residential sectors accounts for about 70% of the total electricity generation in United States. Buildings are the most important consumers, and contribute to over 80% of the consumptions in these two sectors. To reduce electrical energy spending and carbon emission, several studies from Pacific Northwest National Lab (PNNL) and National Renewable Energy Lab (NREL) prove that if equipped with the proper technologies, a commercial or a residential building can potentially improve energy savings of buildings by up to about 10% to 30% of their usage. However, the market acceptance of these new technologies today is still not sufficient, and the reason is generally acknowledged to be the lack of solution to quantify the contributions of these new technologies to the energy savings, and the invisibility of the loads in buildings. A non-intrusive load monitoring (NILM) system is proposed in this dissertation, which can identify every individual load in buildings and record the energy consumption, time-of-day variations and other relevant statistics of the identified load, with no access to the individual component. The challenge of such a non-intrusive load monitoring is to find features that are unique for a particular load and then to match a measured feature of an unknown load against a database or library of known. Many problems exist in this procedure and the proposed research is going to focus on three directions to overcome the bottlenecks. They are respectively fundamental load studies for a model-driven feature extraction, adaptive identification algorithms for load space extendibility, and the practical simplifications for the real industrial applications. The simulation results show the great potentials of this new technology in building energy monitoring and management.

A comunicação sem fio (wireless) vem sofrendo uma enorme expansão nos últimos anos. Para esse tipo de comunicação foram criados alguns padrões, destacando-se o Wi-Fi, o Bluetooth e mais recentemente o ZigBee. Este último, o ZigBee, engloba aplicações de monitoração e sensoriamento de sistemas, o que o torna adequado para aplicações residenciais. Essa característica do ZigBee motivou o desenvolvimento desse trabalho, cujo objetivo foi o de avaliar o padrão ZigBee através de uma aplicação de monitoração e sensoriamento para uso residencial, utilizando um kit de desenvolvimento da Microchip. Esse trabalho consiste em dois nós se comunicando via ZigBee, onde um nó possui um sensor de movimento, uma lâmpada e um LED (que simula um aparelho de ar condicionado) e um segundo nó, que pode ser conectado a um PC via USB utilizando um hardware externo desenvolvido. Esse nó ainda possui um sensor de temperatura, um sensor de luminosidade (LDR) e um sinalizador acústico. Os resultados obtidos mostram que o ZigBee é um padrão eficiente para aplicações de monitoração e sensoriamento de ambientes. / A huge expansion has occurred in wireless communication in the last few years. Some wireless standards were created, like Wi-Fi, Bluetooth and more recently the ZigBee standard, that includes monitoring and sensing systems application. This characteristic makes the ZigBee standard appropriate for residencial applications. This ZigBee characteristic motivated the development of this work, which objective was evaluate the ZigBee standard through a monitoring and sensing application for residencial use, using a evelopment kit by Microchip. This application has two nodes communicating through ZigBee. One node has a motion sensor, a lamp and a LED (that simulates an air conditioner device) and the second one can be connected to a PC through USB using a developed external hardware. This node also has a temperature sensor, a light dependent resistor (LDR) and a beep. The reached results shows that ZigBee is an efficient standard for environment monitoring and sensing.

A comunicação sem fio (wireless) vem sofrendo uma enorme expansão nos últimos anos. Para esse tipo de comunicação foram criados alguns padrões, destacando-se o Wi-Fi, o Bluetooth e mais recentemente o ZigBee. Este último, o ZigBee, engloba aplicações de monitoração e sensoriamento de sistemas, o que o torna adequado para aplicações residenciais. Essa característica do ZigBee motivou o desenvolvimento desse trabalho, cujo objetivo foi o de avaliar o padrão ZigBee através de uma aplicação de monitoração e sensoriamento para uso residencial, utilizando um kit de desenvolvimento da Microchip. Esse trabalho consiste em dois nós se comunicando via ZigBee, onde um nó possui um sensor de movimento, uma lâmpada e um LED (que simula um aparelho de ar condicionado) e um segundo nó, que pode ser conectado a um PC via USB utilizando um hardware externo desenvolvido. Esse nó ainda possui um sensor de temperatura, um sensor de luminosidade (LDR) e um sinalizador acústico. Os resultados obtidos mostram que o ZigBee é um padrão eficiente para aplicações de monitoração e sensoriamento de ambientes. / A huge expansion has occurred in wireless communication in the last few years. Some wireless standards were created, like Wi-Fi, Bluetooth and more recently the ZigBee standard, that includes monitoring and sensing systems application. This characteristic makes the ZigBee standard appropriate for residencial applications. This ZigBee characteristic motivated the development of this work, which objective was evaluate the ZigBee standard through a monitoring and sensing application for residencial use, using a evelopment kit by Microchip. This application has two nodes communicating through ZigBee. One node has a motion sensor, a lamp and a LED (that simulates an air conditioner device) and the second one can be connected to a PC through USB using a developed external hardware. This node also has a temperature sensor, a light dependent resistor (LDR) and a beep. The reached results shows that ZigBee is an efficient standard for environment monitoring and sensing.

As buildings evolve towards the envisioned smart building paradigm, smart buildings' cyber-security issues and physical security issues are mingling. Although research studies have been conducted to detect and prevent physical (or cyber) intrusions to smart building systems(SBS), it is still unknown (1) how one type of intrusion facilitates the other, and (2) how such synergic attacks compromise the security protection of whole systems. To investigate both research questions, the author proposes a graph-based testbed to simulate cyber-physical attacks on smart buildings. The testbed models both cyber and physical accesses of a smart building in an integrated graph, and simulates diverse cyber-physical attacks to assess their synergic impacts on the building and its systems. In this thesis, the author presents the testbed design and the developed prototype, SHSIM. An experiment is conducted to simulate attacks on multiple smart home designs and to demonstrate the functions and feasibility of the proposed simulation system. / Master of Science / A smart home/building is a residence containing multiple connected devices which enable remote monitoring, automation, and management of appliances and systems, such as lighting, heating, entertainment, etc. Since the early 2000s, this concept of a smart home has becomequite popular due to rapid technological improvement. However, it brings with it a lot of security issues. Typically, security issues related to smart homes can be classified into two types - (1) cybersecurity and (2) physical security. The cyberattack involves hacking into a network to gain remote access to a system. The physical attack deals with unauthorized access to spaces within a building by damaging or tampering with access control. So far the two kinds of attacks on smart homes have been studied independently. However, it is still unknown (1) how one type of attack facilitates the other, and (2) how the combination of two kinds of attacks compromises the security of the whole smart home system. Thus, to investigate both research questions, we propose a graph-based approach to simulate cyber-physical attacks on smart homes/buildings. During the process, we model the smart home layout into an integrated graph and apply various cyber-physical attacks to assess the security of the smart building. In this thesis, I present the design and implementation of our tool, SHSIM. Using SHSIM we perform various experiments to mimic attacks on multiple smart home designs. Our experiments suggest that some current smart home designs are vulnerable to cyber-physical attacks

Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt.

The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.

abstract: Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

Valhalla is an old school building constructed in the beginning of the 20th century. Originally designed to be used as a school for girls, the building is now being used for cultural purposes and decaying. The municipality of Falun, who owns the building, wishes to restore it to the point where it meets todays modern standards and amenities, regarding indoor climate and comfort. The idea is to convert Valhalla into an office building for the employees of the municipality. Whilst doing this, a refurbishment goal set by the municipality of Falun applies where the annual amount of bought energy for the building shall be reduced by half when the refurbishment is complete. This report will evaluate the optimal solutions for Valhalla to meet the municipality’s goal. Due to the cultural protection of the building a package of appropriate suggestions will also be displayed. These suggestions are applied with the preservation of Valhalla in focus. A replica of Valhalla will be constructed in IDA ICE which is a simulation software where different energy measures can be investigated and evaluated. This is done to decide which kind of measures is profitable. By only focusing on improving the insulation to Valhalla a total energy reduction of 37.5% is achieved, which is not enough. This report will find out if the goal of reducing the annual amount of bought energy by half is achievable if geothermal heat pumps are introduced. Modern heating and ventilation systems will be installed to ensure that the indoor climate and comfort of Valhalla meets the regulations. Investigating the impact from insulation of the façade, external walls, roof, and windows and how it will improve the building. As well as applying smart controlling of the appliances and lighting, how the occupants can affect the annual electricity usage. By changing the primary heating source to a geothermal heat pump instead of the district heating already installed at Valhalla, to see the total impact in terms of both bought- and primary energy.

This thesis deals with the energy saving in smart building with focus on the impact of the user behaviour on the energy consumption. The problem of human behaviour modelling has been widely studied in the state of the art, but it is still an open problem in the field of smart building since the stochastic nature of the behaviour is difficult to be accurately represented by numerical tools. An interdisciplinary approach is proposed in order to identify the suitable user features from the psychological and social point of view and to integrate such a representation into a DSS for appliance scheduling and energy cost reduction. The proposed method has exploited location-based features of the users in order to represent their habits and needs and to compute the schedules that maximize the user acceptance toward an “energy-aware” behaviour. The obtained results point out a reduction of the peak-to-average ratio higher than 40% also considering the user constraints imposed by their presence into the building.