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The recent trends in designing sustainable power systems emphasise the importance of self-consumption (SC) both at individual and community level. This new paradigm changes the way in which we design photovoltaic facilities for residential houses and for various municipality services as well. In this context, the paper aims to formulate several optimisation problems using criteria such as self-consumption, self-sufficiency (SS) and net present value (NPV) as objectives to provide an optimal photovoltaic (PV) plant size for a singular power system - a subway station. By using this multi-objective approach, the work emphasises how each criteria impacts the profitability and value of the overall investment, involving possible shareholders in the design process by choosing a desired solution from the Pareto-efficient set of configurations. Moreover, a global optimal solution is provided by formulating an optimisation problem through a single-objective Mixed Integer Linear Programming (MILP) approach involving an equivalent metric, the net-energy exchanged with the grid (NEEG). The proposed methodology is validated in a case study on the power system of a subway station in Bucharest (Romania), thus identifying a configuration that focuses on self-consumption maximisation and a solution that reduces the yearly energy bill of the respective power system by 25%.

Future renewable energy communities will reshape the paradigm in which we design and control efficient power systems at the district level. In this manner, the focus will be fundamentally shifted towards sustainable related concepts such as self-consumption, self-sufficiency and net energy exchanged with the grid. In this context, the paper presents a novel approach for optimally designing and controlling the photovoltaic plant and energy storage systems for a metro station in order to increase collective self-consumption and self-sufficiency at the district level. The methodology considers a community of several households connected to a subway station and focuses on the interaction between energy sources and consumers. Furthermore, the optimal solution is determined by using a Mixed Integer Linear Programming Approach, and the impact of different configurations on the overall district benefit is investigated by using several simulation scenarios. The work presents a detailed case study to underline the benefits and flexibility offered by the energy storage system in comparison with a scenario involving only a photovoltaic plant.

Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort. ispartof: Journal of Sensors vol:2019 status: published

International audience; A general approach is proposed to determine the common sensors that shall be used to estimate and classify the approximate number of people (within a range) in a room. The range is dynamic and depends on the maximum occupancy met in a training data set for instance. Means to estimate occupancy include motion detection, power consumption, CO 2 concentration sensors, microphone or door/window positions. The proposed approach is inspired by machine learning. It starts by determining the most useful measurements in calculating information gains. Then, estimation algorithms are proposed: they rely on decision tree learning algorithms because these yield decision rules readable by humans, which correspond to nested if-then-else rules, where thresholds can be adjusted depending on the living areas considered. In addition, the decision tree depth is limited in order to simplify the analysis of the tree rules. Finally, an economic analysis is carried out to evaluate the cost and the most relevant sensor sets, with cost and accuracy comparison for the estimation of occupancy. C45 and random forest algorithms have been applied to an office setting, with average estimation error of 0.19-0.18. Over-fitting issues and best sensor sets are discussed.

WOS:000509819200044; International audience; Data quality assesment is a key component for many real applications, since it can drive better modelling. In this work a methodology to asses data quality (Qscore) is proposed and discussed. The validation of Qscore is performed via an interactive learning experiment related to occupancy estimation. Interactive learning has been shown to be crucial to consider and integrate occupant behavior in smart buildings. Indeed, valuable feedback and information can be collected from the occupants by involving them and by improving their consciousness about energy management systems. Users should feel involved to keep developing highly energy-efficient buildings. To reach this goal, occupants should be aware of the building features to feel more in control. This paper proposes a framework to interact with occupants to estimate building occupancy. This framework is based on an enhanced supervised learning approach that involves interaction with occupants, when necessary, to keep collecting training data. The training data consist of the measurements (i.e. features) collected from common sensors, for instance, motion detection, power consumption, and CO2 concentration, and the label (i.e. number of occupants) provided by the occupants during interactions. The considered learning machine in our experiments is the Multi-layer Perceptron regressor (MLP), although other approaches could be easily integrated within the proposed framework. In order to avoid useless interaction with users a new concept is introduced, called spread rate, to measure the quality of the data to decide if an interaction with the user is necessary or not. Extensive simulations have shown the merits of the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.

This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations. The approach has been used in the co-simulation of building physics and human behaviour in order to assess the CO 2 concentration in an office., IBPC 2015 Turin , Jun 2015, Turin, Italy. 2015

International audience; In the field of building energy efficiency, researchers generally focus on building performance and how to enhance it. The objective of this work is to empower the building occupants by putting them in the loop of efficient energy use, supporting them to achieve their objectives by pointing out how far their actions are from an optimal set of actions. Different levels of explanation are investigated. Indicators measuring the distance to optimality are, firstly, proposed. An algorithm that generates deeper explanations is then presented to determine how changing some actions impacts comfort. The paper emphasizes the importance of explanations with a real case study. It identifies the type and level of explanations needed for different occupants. The concept of replay is presented. An occupant can replay his past actions and learn from them.

Power management in homes and offices requires appliance usage prediction when the future user requests are not available. The randomness and uncertainties associated with an appliance usage make the prediction of appliance usage from energy consumption data a non-trivial task. A general model for prediction at the appliance level is still lacking. This work proposes to improve learning algorithms with expert knowledge and proposes a general model using a knowledge driven approach to forecast if a particular appliance will start during a given hour or not. The approach is both a knowledge driven and data driven one. The overall energy management for a house requires that the prediction is done for the next 24 h in the future. The proposed model is tested over the IRISE data and using different machine learning algorithms. The results for predicting the next hour consumption are presented, but the model works also for predicting the next 24 h.

http://www.sciencedirect.com/science/article/pii/S0378778812004987; International audience; Inhabitant's decisions and actions have a strong impact on the energy consumption and are an important factor in reducing energy consumption and in modelling future energy trends. Energy simulations that take into account inhabitants' behaviour are benchmarked at office buildings using controlled activity profiles and predefined scenarios. In this paper we have proposed a co-simulation environment for energy smart homes that takes into account inhabitants' dynamic and social behaviour. Based on this kind of complex behaviour, the setpoints for different controllers are adjusted in the physical simulator. In this platform, human behaviour is modelled using the Brahms environment and the thermal model and controllers for different appliances are modelled as a physical simulator. The thermal model computes the temperature decrease/increase in a room based on the contextual information resulting from the behaviour simulator. This information is then given to the controller to act upon.

International audience; A general approach is proposed to determine the occupancy in a room using sensor data and knowledge coming respectively from observation and questioning are determined. Means to estimate occupancy include motion detections, power consumption and and acoustic pressure rewarded by a microphone. The proposed approach is inspired from machine learning. It starts by determining the most useful measurements in calculating information gains. Then, a non supervised estimation algorithm is proposed: it relies on Bayesian Network algorithms to model a human behaviour with probabilistic cause-effect relations based on knowledge and questioning. In addition, knowledge has been extracted from supervised learning algorithm. Bayesian Network (BN) based approach has been applied to an office setting, with an average estimation error of 0.09 and an accuracy of 90%. This approach avoids the usage of a camera to determine the actual occupancy required for supervised learning.

International audience; As energy demand increased and production means diversified, conventional approaches of looking into distri- bution grids need to evolve. The Smart Grid paradigm introduces new possibilities of real-time market sensing and interaction models between producers and consumers. In particular, by understanding the types of con- sumers and their potential willingness to adapt their energy demand with price incentives, innovative pricing strategies in the Smart Grid are expected to lead to better production management, profit maximization and end consumers satisfaction levels. In this work we propose a novel framework and a simulation scenario of a global energy network with heterogeneous types of producers and consumers from which different types of behaviors and interactions can be studied.

This paper proposes a formulation of the global energy management problem of dwellings, which consists in a dynamic predictive control system able to generate optimized controls taking into account the model of the concerned dwellings, i.e. homes or offices. It focuses on the adjustment of the electric energy consumption and production in order to maximize energy usage efficiency, which is seen as a compromise between energy cost and overall comfort. To reach this objective, the concept of service is introduced: basically, a service participates to the comfort and may consume energy. The available flexibilities of the services provided by domestic appliances are used to compute anticipative optimal plans for appliance controls based on a mixed integer linear programming (MILP) algorithm. A reactive mechanism based on a list algorithm is added to face unforeseen events. The paper focuses on the computation of the anticipative plans. Different MILP models of common services are proposed. Application examples are given.

This paper depicts a formulation of the global home electricity management problem, which consists in adjusting the electric energy consumption to the cost and availability variations of the power supply. The available flexibilities of the services to inhabitants are used to compute optimal plans for appliances control. Two optimization criteria are addressed: the user's comfort and the economic cost. A global multi-layer solving approach is depicted that includes first the computation of consumption/production coordination plans and then the dynamic matching to the actual consumption/production data. The paper focuses on the computation of the anticipative plan through a MILP formulation of the energy management problem. Application examples are given.

The display of space filling data is still a challenge for the community of visualization. Direct volume rendering (DVR) is one of the most important techniques developed to achieve direct perception of such volumetric data. It is based on semitransparent representations, where the data are accumulated in a depth-dependent order. However, it produces images that may be difficult to understand, and thus several techniques have been proposed so as to improve its effectiveness, using for instance lighting models or simpler representations (e.g., maximum intensity projection). In this article, we present three perceptual studies that examine how DVR meets its goals, in either static or dynamic context. We show that a static representation is highly ambiguous, even in simple cases, but this can be counterbalanced by use of dynamic cues (i.e., motion parallax) provided that the rendering parameters are correctly tuned. In addition, perspective projections are demonstrated to provide relevant information to disambiguate depth perception in dynamic displays.

International audience; In the recent years, surveys and studies have established the importance of occupant's behaviour on energy consumption in buildings. Therefore, inclusion of in-habitants' behaviours is compulsory for the assessment of building energy management system's (BEMS) strategies, which highly depends on human behaviour. The purpose of modelling the inhabitants behaviour is to see how their choices and control of household appliances can impact the energy consumption. In this paper, a co-simulation approach is presented where the inhabitants' behaviours are co-simulated with the SIMBAD-MOZART thermal model of a reference house and BEMS. The realization of all the different kinds of inhabitant behaviours into energy co-simulations will help to improve the smart grid technology and hence provide inhabitants with better services to save energy and cost while maintaining their comfort levels.

International audience; A general approach is proposed to estimate the number of occupants in a zone using different kinds of measurements such as motion detection, power consumption or CO2 concentration. The proposed approach is inspired from machine learning. It starts by determining among different measurements those that are the most useful by calculating the information gains. Then, an estimation algorithm is proposed. It relies on a C4.5 learning algorithm that yields human readable decision trees using measurements to estimate the number of occupants. It has been applied to an office setting.

This paper presents a fault isolation method, which is able to handle large scale distributed industrial plants. The method relies on a logical approach, which has been improved in order to soundly take into account doubts in the decisions provided by detection tests. Formal logic of consistency based diagnosis has been extended into a fuzzy logical. These techniques have been implemented within the EC project named MAGIC and applied on hydraulic looper.

International audience

Purpose – The use of energy storage devices helps the consumers to utilize the benefits and flexibilities brought by smart networks. One of the major energy storage solutions is using electric vehicle batteries. The purpose of this paper is to develop an optimal energy management strategy for a consumer connected to the power grid equipped with Vehicle-to-Home (V2H) power supply and renewable power generation unit (PV). Design/methodology/approach – The problem of energy flow management is formulated and solved as an optimization problem using a linear programming model. The total energy cost of the consumer is optimized. The optimal values of decision variables are found using CPLEX solver. Findings – The simulation results demonstrated that if the optimal decisions are made regarding the V2H operation and managing the produced power by solar panels then the total energy payments are significantly reduced. Originality/value – The gap that the proposed model is trying to fill is the holistic determination of an optimal energy procurement portfolio by using various embedded resources in an optimal way. The contributions of this paper are in threefold as: first, the introduction of mobile storage devices with a periodical availability depending on driving schedules; second, offering a new business model for managing the generation of PV modules by considering the possibility of grid injection or self-consumption; third, considering Real Time Pricing in the suggested formulation.

The Demand-Side-Load Management will change the way people behave. Different authors have proposed energy management algorithms for Smart Home that either integrates or not renewable energy. All these researches have the same general objective: minimizing the daily energy cost without affecting the comfort of occupants. This paper deals with the performance analysis of a Global Model Based Anticipative Building Energy Management System (GMBA-BEMS) managing household energy. This GMBA-BEMS is able to optimize a compromise between user comfort and energy cost taking into account occupant expectations and physical constraints like energy price and power limitations. To validate the GMBA-BEMS, the model of a building has been developed in MATLAB/Simulink. This work analyzes GMBA-BEMS application that manages appliances such as heating, washing machine and dishwasher from a grid point of view.

International audience; Energy waste due to inhabitants' behaviour in residential buildings has emerged as a potential research area due to the increasing worldwide population and growing energy needs. However, existing approaches for simulating energy consumption are mainly limited to office buildings and are based on static profiles. In this paper we propose a 4-step co-simulation methodology to assess how inhabitants' interactions with household appliances affect energy consumption. The approach is validated using a case study showing how human activities influence the energy consumption patterns of a refrigerator. The fridge was specifically chosen because it is a high energy-consuming appliance that is strongly affected by inhabitants' behaviours. In addition, modelling the fridge is nontrivial, and in choosing this appliance we show that it is possible to apply the approach to less complex appliances. A co-simulation approach is adopted with the fridge being physically modelled in Matlab and with human behaviour being modelled in the Brahms language and simulation environment. The consumption distribution from the simulated scenario is compared with the actual distribution (using data from a consumption database), to find optimum values of tuning parameters with less than 10% variation. This methodology enables us to simulate how human behaviours affect energy appliance consumption.

Publication des actes de la conférence sur CD ; Proceedings disponible sur IEEE Xplore; International audience; This paper presents an energy management system EMS controlling household energy capable both to satisfy the maximum available electrical energy constraint and to maximize user comfort criteria. It is composed into three layers: anticipation, reactive and device layers. Actually, this household energy control system is based on simple models. To validate them, we modeled the building more finely using MATLAB/Simulink. The validation is obtained through a real time simulation platform "RT_LAB" capable of interoperating with EMS. At the end of this paper, some results of optimization of energy are presented.

This paper focuses on Energy Management System (EMS) applied to the residential sector. The EMS architecture is proposed. It is composed on three layers: anticipation, reactive and device (local) layers. However, to validate global control algorithms, a simulator capable of interoperating with energy management systems must be used. This work presents a solution for real-time simulation with a networked system that manages energy flows. It makes it possible to perform fine simulations of the physics, including fast dynamics of electrical appliances imbedded on the buildings. This paper deals with computing the starting times of some services in one hand and in determining set points of others on other hand; the maximal power constraints and predicted events are taken into account. A Satisfaction Problem formulation has been proposed. So, the purpose is to maximize user comfort and minimize energy costs. An application example is presented.

The world human population increases today more than 80 millions of individuals per year, therefore, the energy needs increase more and more. The buildings represent the most important part of the energy consumption and the greenhouse effect. the inhabitants are confronted to a variable tarifs of energy and some heater setpoints effects according to hour and days and to energy producers. In this dynamic context of production and consumption of energy that a building, equipped with a Home Automation system to control the energy, takes its importance. In this paper, we describe the architecture of a Home Automation system. The system is divided into two parts: a centralized part represented the resolution of linear problems noted regular service part and a Multi-Agent part noted singular service part.

National audience; Le secteur de bâtiment représente 43% de la consommation totale du pays en énergie primaire. Pour cette raison, le bâtiment est considéré comme un énorme gisement potentiel d'économies d'énergie, d'où l'existence de nombreux travaux de recherche sur ce thème. Parmi ces derniers travaux, le projet ANR Multisol a proposé une solution pour la gestion des flux énergétique dans le bâtiment en gérant les équipements du système bâtiment. Or, pour valider des algorithmes de pilotage global sur des modèles fins de systèmes bâtiment, il faut disposer de simulateur pouvant intéropérer avec des systèmes de gestion énergétique. Ce travail présente une solution de simulation temps-réel interconnectée avec un système de gestion des flux énergétiques qui permet de réaliser des simulations fines du système bâtiment.

Suitable installed sensors in a industrial process is a necessary condition for fault diagnosis. Sensor placement for diagnosis purposes is to study which process variables have to be measured to satisfy diagnosis specifications (detectability, dis-criminability and diagnosability). This paper presents a method based on the study of the structural model properties and the Dulmage-Mendelsohn decomposition. Due to the use of structural models, the proposed approach can be applied to a wide variety of system (linear, algebraic, dynamics, etc.). Assuming that the cost of placing a sensor for each possible variable is defined, this method finds the minimal cost sensor configuration according to the diagnosability criteria. This method does not require the computation of testable subsystems

A new efficient and flexible algorithm for the design of testable subsystemsIn complex industrial plants, there are usually many sensors and the modeling of plants leads to lots of mathematical relations. This paper presents a general method for finding all the possible testable subsystems, i.e., sets of relations that can lead to various types of detection tests. This method, which is based on structural analysis, provides the constraints that have to be used for the design of each detection test and manages situations where constraints contain non-deductible variables and where some constraints cannot be gathered in the same test. Thanks to these results, it becomes possible to select the most interesting testable subsystems regarding detectability and diagnosability criteria. Application examples dealing with a road network, a digital counter and an electronic circuit are presented.

International audience; A dense network of rain gages, set up in the Upper Oueme Valley in Benin is used to study the spatial organization and the kinematics of the convective systems that cross the region. The study area is situated under Soudanian climate and set up as part of the AMMA-CATCH (African Monsoon Multidisciplinary Analysis – Couplage de l'Atmosphere Tropicale et du Cycle Hydrologique) observing system. Previous works focusing on the rainy events that occur in the Sahelian region of Niamey have shown that most of the rainfall in that region is provided by Organized Convective System that cover several thousand km2 and usually propagate with a strong westward component. It was shown also that the time evolution of these Sahelian rainy events usually exhibits a convective peak followed by longer lasting and weaker stratiform rainfall. The aim of the present study is to analyze the spatial organization and kinematics of the rainy events occurring further south under the distinct, much more humid, Soudanian climate. These events have been poorly documented so far and the extent to which the Soudanian rainfall events behave like their Sahelian counterparts remains unclear. Seven years of rainfall data gathered over the AMMA-CATCH Benin site are studied. A new method called the ‘Average Synchronized Hyetograph' (ASH) is proposed to analyze the kinematics of the rain patterns. The method also allows the assessment of the spatial organization of the system. A classification of the rainy events is proposed. It is based on assessing if (i) the rain patterns show a global propagation velocity and direction and (ii) if the time evolution of the rain rate within the network is typical of organized tropical Mesoscale Convective Systems (MCS) with a well-defined convective peak. The present study shows that about 55% of the events have a signature typical of those of MCS. Conversely, about 27% of rainfall events do not show evidence of being associated with MCS or even propagating. The kinematic properties of the events classified as MCS appear to be globally consistent with what was observed for the Sahelian zone from satellite tracking; the dominant direction is south-southwest but with a large departure from this average trend; their velocity ranges mostly between 20 and 50 km/h but a significant number of events are faster than that. MCS remains the dominant type of events during the whole rainy season but the period between mid July and mid August. The classification of rainfall events resulting from ground tracking has been compared with METEOSAT satellite tracking for the years 2003 and 2004. There is a significant correspondence of classes of rainfall events between the two tracking methods despite differences of scales and criterions in their definition.

International audience; In complex industrial plants, there are usually many sensors and the modeling of plants leads to lots of mathematical relations. Before using classical tools for fault detection, the first problem to solve is: what sensors and mathematical relations should be selected for the design of a detection test such as a state observer or a parity space based detection algorithms. This paper presents a general method for finding all the possible testable subsystems i.e. sets of relations, than can lead to detection tests, taking into account actuator and sensor locations. This method, which is based on a structural analysis, provides the constraints that have to be used for the design of each detection test and manages situations where constraints contain non deductible variables. Thanks to these results, it becomes possible to select the most interesting testable subsystems regarding detectability and diagnosability criteria.

A Method for Sensor Placement Taking into Account Diagnosability CriteriaThis paper presents a new approach to sensor placement based on diagnosability criteria. It is based on the study of structural matrices. Properties of structural matrices regarding detectability, discriminability and diagnosability are established in order to be used by sensor placement methods. The proposed approach manages any number of constraints modelled by linear or nonlinear equations and it does not require the design of analytical redundancy relations. Assuming that a constraint models a component and that the cost of the measurement of each variable is defined, a method determining sensor placements satisfying diagnosability specifications, where all the diagnosable, discriminable and detectable constraint sets are specified, is proposed. An application example dealing with a dynamical linear system is presented.

This paper presents the principles of a Home Automation System dedicated to power management that adapts power consumption to available power ressources according to inhabitant comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent supports a service achieved by several devices, it cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a plan for global consumption according to predicted productions and consumptions and to inhabitant criteria. The paper shows how to compute a global consumption plan relying on Bellman-Ford's algorithm and how to reduce the problem complexity by dividing the whole problem into independent sub-problems.

This paper presents the principles of a Home Automation System dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent, supporting a type of service achieved by one or several devices, cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a predicted plan of global consumption according to predicted productions and consumptions and to user criteria. The paper presents a design for the Multi Agent Home Automation System.

This paper presents a new three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user comfort criteria. These layers are composed by: an equipment layer, with local and fast control mechanism, a protection layer, which is triggered when energy constraints are violated, and an anticipation mechanism, which adjusts future set-points of equipments in order to tackle energy events that can be forseen. The scheduling problem of the set-points of equipments is modelled by a graph and solved by the Bellman-Ford's algorithm. This control mechanism makes it possible to have a more flexible control of the overall power consumption in housing in exploiting natural thermal energy accumulation.

International audience; This paper deals with the generalization of state observers in the framework of LTI deterministic discrete-time systems using complementary matrix formalism. Four necessary and sufficient general conditions have been established in the design of any type of state observer. Furthermore, necessary sufficient intrinsic conditions have been deduced. Respecting these intrinsic conditions guarantees satisfaction of the preceding general conditions. An illustrative example is also included

International audience; This communication deals with the problem of state estimation for a class of non linear singular systems. Sufficient conditions for the existence of such observers are provided and the design of an observer is examined. Assuming that the measurement matrix is full row rank involves many calculation simplifications ; therefore special emphasis in the computational aspect of the observer matrices is provided

International audience; This paper presents an approach which is called meta-optimization combining with scattering analysis used to enhance on-site real-time temperature anticipation for energy management. The aim of this approach is to analyse the sensitivity of the parameters in order to simplify, and then attain, the best reduced model able to match with measurements regularly in a robust manner. This will be done with the effort of keeping their physical properties. Indeed, parameter identification is a key challenge for modelling system that are used with many uncertainties such as construction and material quality, weather conditions, and occupant behaviour that are changing during building life. This method is applied for a nearly-zero energy building in France to validate our approach.

Fault diagnosis and maintenance of a whole-building system is a complex task to perform. Available building fault detection and diagnosis tools are only capableof performing fault detection using behavioral constraints analysis. The thesis of Mahendra Pratap Singh proposes to use heterogeneous tests with validity constraints in the context of building fault diagnosis but the proposed approach assumes that the sensors are reliable. Nevertheless, validity constraints are checked with potentially faulty sensors. If these sensors are faulty, the diagnostic result is not guarantee and there is a need for method to prove the test as well as global diagnoses.To make a test, data are required from different parts: meteorological, human and physical parts. However, the data gaps is the main sensor fault in buildings. Sensor values are not uniformly sampled and there is a need to decide from which delay the sensor becomes faulty?The objective of this work is to highlight these challenges as well as to provide a strategy about how to solve them. Three solutions for diagnosis in building are proposed1-A level of completeness for better formalizing validity.In this work, we make the hypothesis that there is no precise global model for a building system but there is contextual models with limited validity. The validity is measured with potentially faulty sensors. The completeness level is proposed as a method to prove if a test space is fully covered or not i.e to assess the level of validity of a test.2-A confidence level for proving global diagnosis.A test is characterized by thresholds i.e the behavioral constraint is either satisfied or unsatisfied. Uncertainty is related to the validity constraints. Indeed, it is difficult to set a threshold for the level of completeness from which one can say that a test is valid.Diagnostic results are calculated from a set of tests, each one defined by itscompleteness level. The contribution is to propose a solution to compute the confidence level of a global diagnosis deduced from a set of tests whose some of them have a completeness level lower than 1. A method based on fuzzy logic reasoning is used for this purpose.3- Automatic thresholding for sensor data gap detection.The delay depends on the measured value and the type of sensor. The objective is toidentify from which delay a sensor become faulty. Two techniques are proposed: a time series analysis and a statistical approaches.Different applications have been studied for validation: an office at G-SCOP lab,an appartement at Grenoble and a platform in the University of southern Denmark., Le diagnostic des défauts et la maintenance d'un système bâtiment est une tâche complexe à effectuer. Les outils existants pour la détection et le diagnostic de défauts dans les bâtiments permettent d'effectuer cette détection à l'aide d'une analyse des contraintes comportementales.La thèse de Mahendra Pratap Singh propose le concept de tests hétérogènes avec des contraintes de validité dans le contexte du diagnostic de défauts dans les bâtiments, mais l'approche proposée suppose que les capteurs sont fiables et ne s'intéresse qu'aux processus thermo-aérauliques et aux systèmes de chauffage. Les contraintes de validité sont mesurées avec des capteurs. Si ces capteurs sont défectueux, le résultat du diagnostic n'est pas garanti et il est nécessaire d'avoir une méthode permettant de prouver le test ainsi que le diagnostic global.Pour effectuer un test, il est nécessaire de disposer de données provenant de différentes parties: météorologiques, humaines et physiques. Cependant, les données manquantes constituent le type de défauts de capteurs majeur dans les bâtiments. Les mesures des capteurs ne sont pas échantillonnées de manière uniforme et il est nécessaire de décider à partir de quel retard le capteur devient défectueux.L'objectif de ce travail est de mettre en évidence ces défis et de fournir une stratégie sur la façon de les résoudre. Trois solutions pour le diagnostic du système bâtiment sont proposées1- Un niveau de complétude pour une formalisation de la validité lorsque les capteurs sont potentiellement défaillants.Pour le système bâtiment, il n'existe pas de modèle global précis mais il existe des modèles contextuels à validité limitée. L'espace de test consiste en un ensemble de mesures. Le niveau de complétude est proposé comme méthode pour prouver si un espace de test est entièrement couvert ou non c'est-à-dire pour évaluer le niveau de validité d'un test en présence de capteurs non fiables.2- Un niveau de confiance pour prouver un diagnostic globalUn test automatique est caractérisé par des seuils, c’est-à-dire que la contrainte comportementale est satisfaite ou non satisfaite. L'incertitude est liée aux contraintes de validité. En effet, il est difficile de définir un seuil pour le niveau de complétude à partir duquel on peut dire qu'un test est valide. Le résultat du diagnostic est calculé à partir d’un ensemble de tests, chacun défini par sonniveau de complétude. La contribution est de proposer une solution permettant de calculer le niveau de confiance d’un diagnostic global déduit d’un ensemble de tests dont certains ont un niveau de complétude inférieur à 1. Une méthode basée sur le raisonnement de logique floue est utilisée à cet effet.3- Seuillage automatique pour la détection de données de capteurs manquantesLe retard dépend de la valeur mesurée et de type du capteur. L'objectif est d'identifier à partir de quel retard un capteur devient défectueux. Deux techniques sont proposées: une analyse de séries temporelles et une approche statistique.Différentes applications ont été étudiées pour la validation: un bureau au laboratoire G-SCOP, un appartement Grenoblois et une plateforme à l'université de Danemark Sud.

Currently, indoor discomfort in dwellings is one of the crucial issues along with the building energy consumption. Indeed, people spend 60-90% of their lives in buildings. Indoor comfort plays a vital role in occupants health, productivity, and well-being. However, various optimization and rule-based anticipative or predictive building strategies have been proposed to achieve the perceived comfort taking into account the energy consumption. However, in practice, anticipation or plans are far from the reality. Usually, anticipative plans are synchronized with one-hour anticipation period and do not consider the various sources of discrepancies as well as current envelope configurations. Unbeknownst to many, discrepancies from different sources could cause big penalty over cost and comfort. To tackle this issue, building management system needs to be designed as reactive or almost with no planning so that it can respond to all discrepancies re-actively. To address this problem, a multi-scale Anticipative Reactive Diagnosing- Building Management System (ARD-BMS) is proposed in this dissertation. ARD-BMS is an internal management and performs three important actions i.e., Discrepancydetection, Cause isolation, and finally Corrective actions. ARD-BMS follow the short-time resolution i.e., 10-minute to analyze the fault trends and current the building dynamics and take necessary corrective actions to maintain the desired level of comfort. This thesis proposes a fast dynamics simplified reactive model that can be used to estimate the current status of the building. Modern buildings are very sophisticated system with a large number of sensors, controllers, and HVACs. Most of the building facilities are using a scheduled preventive maintenance services derived from periodic operations of the buildings. These preventive actions do not take into account the other inadmissible issues such as unplanned situations, weather prediction failures etc. These unplanned issues could cause unaccountable impacts over occupant’s comfort during the 24-hour operation cycle. Diagnosability of short-term discomfort causes is still a challenging job at whole building operation level. Furthermore, to analyze this situation the thesis proposes a diagnostic methodology for detection and isolation of cause (faults) in buildings. The proposed methodology includes a rule-based HAZOP (Hazard and Operability analysis) and model-based approach. Further, in order to oversee unplanned discomforts, a short-term reactive optimization has been proposed., Actuellement, l'inconfort intérieur dans les bâtiments est l'une des questions cruciales, ainsi que la consommation énergétique du bâtiment. En effet, les gens passent 60 à 90% de leur vie dans les bâtiments. Le confort intérieur est indispensable en termes de bienfaits sur la santé, la productivité et le bien-être des occupants. C'est pourquoi diverses stratégies d'optimisation fondées sur des règles, anticipatives ou prédictives ont été proposées pour atteindre le confort perçu en tenant compte de la consommation d'énergie. Dans la pratique, il existe un écart entre l'anticipation et la réalité. Habituellement, les plans anticipatifs sont synchronisés avec une période d'anticipation d'une heure et ne tiennent pas compte des différentes sources de conflits ainsi que des configurations d'enveloppes des bâtiments possibles. Il peut en résulter des conséquences négatives sur le coût et le confort. Pour résoudre ce problème, le système de gestion du bâtiment doit être conçu aussi comme réactif, de sorte à ce qu'il puisse répondre à toutes sortes de divergences par rapport au plan anticipatif, de manière réactive. Dans ce but, un système multi-échelle d'analyse de diagnostic réactif anticipatif (ARD- BMS) est proposé dans cette thèse. ARD-BMS est une gestion interne qui effectue trois actions importantes, c'est-à-dire la détection de défaut, l'isolement des causes et, enfin, les actions correctives. ARD-BMS permet ainsi une gestion réactive à court terme, à savoir 10 minutes pour analyser les tendances des défauts et la mise à jour de la dynamique du bâtiment et prendre ainsi les mesures correctives nécessaires pour maintenir le niveau de confort désiré. Cette thèse propose donc un modèle réactif à dynamique rapide simplifiée qui peut être utilisé pour estimer l'état actuel du bâtiment. Les bâtiments modernes sont un système très sophistiqué avec un grand nombre de capteurs, de contrôleurs et de CVC. La plupart des installations de construction utilisent des services prévus de maintenance préventive provenant des opérations périodiques des bâtiments. Ces problèmes imprévus puce causer des répercussions inexplicables sur le confort de l'occupant pendant le cycle de fonctionnement de 24 heures. Ces problèmes ne sont pas inadmissibles tels que les situations imprévues, les pannes de prévisions météorologiques. Le diagnostic des causes d'inconfort à court terme est encore un problème difficile au niveau de l'opération de construction intégrale. En outre, pour analyser cette situation, proposez une méthodologie diagnostique pour la détection et l'isolement des causes (fautes) dans les bâtiments. La méthodologie proposée comprend une HAZOP fondée sur les règles (analyse des risques et de l'optimisation) et une approche basée sur un modèle.

As buildings become more energy-efficient, the effect of human activities on their global consumption increases. To limit this impact, a first attempt was made to increase the level of automation in buildings. However, from surveys led in residential and office buildings, Carassus and Sidler conclude that automation does not reduce the gap between predicted and effective consumption. This work reveals that static rules can not be sufficient. In order to reach the goal of low energy consumption in the operation phase of buildings, occupants must then be involved in the energy management process. The proposed work is part of a research project focusing on a system embedding energy services with a high level of interaction to empower users with energy services. The role of these energy services is to allow users to define their own objective and consequently generate information and advice regarding the best strategies to apply on building systems (HVAC systems, sun-shading devices, windows opening,…) and envelope configuration. An analysis of the sociological literature was made in order to implement relevant services for the users, favouring real and durable changes in their behaviours. Some of these services require thermal and aeraulic behavioural models easy to set up and little expert knowledge. This thesis focuses on the development and implementation of a model relevant for energy management end-user services. It involves to deal with the definition of relevant model structures together with parameter estimation methods to tune the parameter values. The model must be simple enough to ease its set up with estimation and prediction capabilities. The limits in terms of complexity and computational time are discussed. The energy management services should be configured by occupants thus it must not require much expert knowledge. Consequently, some sensors are needed to estimate the thermal and aeraulic model. Tests have been performed to limit the needed instrumentation. Relevant model structures are discussed because they should be generic: it should adapt to both mono-zone and multi-zone case studies and it should be based on a minimal number of sensors and require little expert knowledge., Les bâtiments devenant de plus en plus performants énergétiquement, l'impact de l'activité humaine devient prépondérante. Afin de limiter cet impact, les bâtiments ont tout d'abord vu leur niveau d'automatisation augmenter. Toutefois, des études menées dans des bâtiments résidentiels et de bureaux ont mis en évidence que l'automatisation ne réduisait pas l'écart entre la consommation d'énergie prévue en phase de conception la consommation d'énergie réellement constatée. Afin d'atteindre une faible consommation en phase d'exploitation du bâtiment, les occupants doivent être impliqués dans la gestion d'énergie. Le travail proposé fait partie d'un projet de recherche visant à mettre en place un gestionnaire énergétique redonnant le pouvoir aux occupants via des services énergétiques. Ces services permettront à l'utilisateur de définir ses propres objectifs et le conseillera sur les meilleures actions à mettre en place pour les atteindre. Une analyse de la littérature sociologique est menée afin de permettre l'implémentation de services pertinents et favorisant de réels et durables changements de comportement. La mise en place de ces services requiert un modèle thermique et aéraulique du bâtiment faciles à configurer et ne nécessitant aucune donnée experte. Le travail présenté dans cette thèse se concentre sur l'implémentation d'un tel modèle. Cela implique de définir dans un premier temps la structure de modèle adéquate : permettant d'atteindre à la fois les précisions requises à la prédiction à horizon 24 heures tout en ne nécessitant que peu d'informations expertes. Dans un deuxième temps, il s'agit d'étudier les différentes méthodes d'estimation paramétrique répondant au cahier des charges. Des validations du modèle sont faites sur deux cas d'études réel : un bureau et un appartement. Cette thèse décrit la méthodologie nécessaire à la détermination de structures de modèles pertinentes. Ces structures doivent être génériques afin de s'adapter à la fois à des cas d'études mono et multi-zones. De plus, elles doivent se baser sur le moins de capteurs et de connaissance experte possible.

Energy management for building has become a major issue this last decade because of its energy impact. Building energy management reduces energy wastes and enable a better matching between energy needs and grid capabilities. Different types of energy management systems are proposed in scientific literature, most of them with anticipation capacities.The first results do not really consider the complexity issue coming from the number of modeling elements and also coming from the diversity of energy management applications.This thesis proposes elements of solution to the complexity problem. The work started by analyzing the energy management system 'GHomeTech' and its adaptation to the complex building prototype CANOPEA. The issue of composition from elementary models is explored. A solution is proposed; it enables the reusability of elementary models. Aggregation and transformation into mixed integer linear programming optimization models is presented. The resulting tool has been validated on the CANOPEA project.Energy management is not limited to MILP optimization. Different types of applications are also used to provide other services: parametric estimation models to simplify the configuration of energy management systems, simulation for validation and prediction depending on pre-defined scenarios for example. This other dimension of complexity is discussed in a second part of the manuscript. Solutions for automatic rewriting of models are detailed. It relies on symbolic manipulations in different types of processing. Several examples of applications illustrating the automatic generation of models are presented., Du fait de son impact énergétique, la gestion énergétique dans le bâtiment est devenu un enjeu majeur ses dernières années, qu'il s'agisse d'encourager la sobriété énergétique ou de s'adapter aux besoins des réseaux énergétiques. Différents travaux de recherche ont conduit à des gestionnaires énergétiques souvent dotés de capacités d'anticipation. Les premiers résultats, bien qu'encourageants, n'envisagent pas la complexité tant du fait nombre d'éléments que de la diversité des applications de gestion énergétique.Cette thèse propose d'apporter des éléments de solution à la problématique de complexité. Les travaux ont démarré par l'analyse du gestionnaire énergétique GHomeTech et son adaptation au prototype de bâtiment complexe CANOPEA. Les problématiques de composition sont explorées. Une solution favorisant la réutilisabilité d'éléments de modèles, l'agrégation et la transformation vers des modèles d'optimisation de type programmation linéaire en nombres entiers (PLNE), est proposée. L'outil résultant a été validé sur le projet CANOPEA.La gestion énergétique ne se limite pas à l'optimisation PLNE. Différentes natures d'applications permettent d'offrir d'autres services : l'estimation paramétrique de modèles pour simplifier la configuration des gestionnaires énergétiques, la simulation pour la validation et la prédiction fonction de scénarios définis par exemple. Cette autre dimension de la complexité est abordée dans un second volet du manuscrit. Des solutions de réécriture automatique de modèles sont proposées grâce à des manipulations symboliques permettant différents types de transformation. Plusieurs exemples de génération automatique de modèles applicatifs sont présentés.