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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.

After some remarks on terminology, this paper introduces a general method for fault diagnosis in complex dynamic systems, which takes advantage of the results on analytical redundancy methods from the Automatic Control community and on logical reasoning from the Artificial Intelligence community. The proposed method tackles both the problem of diagnosing complex dynamic systems with models of normal and abnormal behavior including differential equations, and the problem of providing logically sound diagnosis. Moreover, it is shown how diagnoses can be sorted.

The variety of diagnostic methods proves that none can pretend to be much better than the others. A significant improvement of industrial applications can only be achieved when FDI problems are dealt with and solved in a framework of integrated use of different FDI methods. This paper presents a study about the possible cooperation between detection methods. Two different isolation methods, which analyze symptoms provided by observer-based and signal-based detection algorithms are compared using a two water tank system: one method is based on signature tables and the other one is based on a logical approach. Strategic aspects are also stressed.

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.

This paper focuses on a fault detection method taking model uncertainties into account. Every uncertain parameter is represented by a bounded variable. The proposed strategy for fault detection and diagnosis consists in modeling the uncertainties, in generating output envelopes and then in testing their coherency with measurements. An application of the method was carried out on a nuclear fuel reprocessing plant simulator. A fault scenario will be presented in order to enlighten the methodology

In this paper, an extended model of induction machines bearing short-circuits on a single stator phase is proposed. It is shown that a model based on five differential equations whose state vector contains only stator variables expressed in the stator Park base is more relevant to represent such failures. Moreover a simpler model, valid for small short-circuits, is obtained by linearization. It is further extended in order to take into account resistor disturbances. Finally, a structural analysis is developed to point out; that the stator currents are sufficient to generate residuals for discriminating failures under abnonnal operation.

International audience; This paper contributes to the design of intelligent electrical network system. A distributed management and control approach, realized through the Multi Agent Systems (MAS) technology, is proposed for Smart Electrical network, due to the MAS advantages: scalability, openness, reliability and communications efficiency. Each agent is embedded into a principal actor and cooperates and coordinates its actions in order to find acceptable near optimal solution. This system proposes multi-level control architecture composed of two main complementary mechanisms: adjustment and anticipative. This paper details the anticipation level that computes the best future set-points according to energy needs and production capacity. These set points are then adjusted in real time by the adjustment mechanism. The platform JADE is used in order to simulate the behavior of the different network actors.

This paper describes an approach aiming to automatically transform a model depicting a high level physical behavior into two different optimized building energy management application models. Basically, each application requires specific formalism and information therefore it is necessary to rewrite an initial physical behavior model to obtain application models. Up to now, these two application models should be rewritten manually, which is representing an significant work and possibly a source of error. In order to automatize this process, the MDE approach seems to be an appropriate solution. This paper presents core specifications of transformation of hinge model into application models. To illustrate this approach, transformations into both an acausal anticipative model based on a mixed integer linear programming problem and a nonlinear causal model for fast simulated annealing optimization are shown. These models are used for energy management of a smart building platform named “Monitoring and Habitat Intelligent” PREDIS in Grenoble, France.

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.

The Demand-Side-Load Management applied to household is a control strategy which will change the way we consume and produce energy. Several works give different forms of management algorithms of Smart Home integrated or not renewable energy and especially the photovoltaic. All these researches have the same general objective: minimize the daily energy consumption level to reduce the electricity bill without harming the comfort of residents. This paper deals with a Building Energy Management (BEM) System controlling household energy capable both to satisfy the maximum available electrical energy constraint and maximize user comfort criteria. It is composed of three layers. This household energy control system is based on simple models. To validate them, a virtual building modeled more finely using MATLAB/Simulink is used. This work presents the application of optimal control to a thermal zone of a virtual building. The objective is to control energy “Smart Homes” consumption through predicted energy price.

Data centers energy efficiency has become an issue given the rising of energy prices and the increase of Web applications, which are hosted, mainly in data centers. In order to understand the opportunities for improving data center energy efficiency, it is necessary to determinate the real energy requirements for one computer application. This article will examine the power chain starting from the facility level and ending at the processor level. In this paper a data center energy model is proffered along with two important indicators that address energy efficiency: the first one is addressing Facility Efficiency and the second one is addressing IT Efficiency. The energy model is taking into account a global overview of the data center (power distribution units, cooling system and the IT system). The aim of the research is to find effective solutions to make data center reduce power consumption while keeping the desired quality of service or service level agreement.

Energy consumption is a major issue nowadays. The importance of forecasting energy consumption from end-user to power system operator becomes more obvious than ever. The consumption in the residential sector represents a significant percentage in the total electricity demand in Europe and all over the world and it is expected to grow. So, the prediction of energy consumption becomes a key component in the management (e.g. power flow) of the electrical grid. This paper presents different methods for prediction of energy consumption of electrical appliances used in dwellings. A stochastic approach is used since forecasting the consumption for a single appliance is more difficult that predicting the overall consumption. Different basic predictors are presented and a stochastic predictor is proposed and tested according to a prediction precision criterion. The enhancement of forecast precision is done by segmentation and aggregation of data. Several experiments are conducted for different appliances in the house and the results are discussed.

The global dwelling energy management problem can be formalized as an optimization problem of energy consumption/ production. An optimal solution for the home energy management problem is usually solved by centralized solvers. The solver gets the totality of the thermal model of the dwelling but also each appliance composing the system. Nevertheless, this centralized resolution has some limits due to some particular appliances. For example: the appliances with a non-sharable model because of the manufacturer, the appliances that need some precisions that cannot be included in their standard representation used by the solver, the appliances which require specific solvers and the appliances that are managed by user-defined specific heuristic rules. This work proposes to combine the centralized solving approach for energy management problem in dwellings with a multi-agent solving system. The multi agent system provides the possibility of integrating specific models in the global solving of the problem. The proposed system is a mixed centralized/decentralized approach for the solving of global energy management problem. In this paper we present the system and the results of the perfamance tests realized. This work is part of the ANR project ReActivHome.

Building Energy Management Systems (BEMS) have been developed in order to improve dwelling energy efficiency, which is seen as a compromise between energy cost and overall comfort. A BEMS optimize the control of energy supply and demand according to forecasting data. This paper proposes new BEMS strategies to support Demand Side Management and validate them using a real time platform: A Power-Hardware-In the-Loop (PHIL) test bench. Physical devices are introduced into the simulated system to check whether they can support the control strategies.

National audience; Cet article développe une architecture de co-simulation basée sur un standard de composants logiciels. Il propose une norme de composant permettant d'unifier les interfaces de co-simulation de plusieurs outils de simulation énergétique du bâtiment. Il permettra ainsi de faciliter et de généraliser la mise en place d'une co-simulation. L'intérêt de cette stratégie sera mis en évidence à travers le couplage entre le logiciel COMFIE de simulation de l'enveloppe thermique et l'outil Brahms de simulation du comportement des occupants dans l'environnement Matlab/SIMULINK. La mise en relation de ces deux outils était indispensable pour profiter de leurs compétences spécifiques dans chaque domaine et assurer la complémentarité entre eux.

Building systems are equipped with building automation system, supervisory controllers, and a lot of sensors which make them complex systems. In this context, technical malfunctions can have a huge impact on building operation and occupant’s comfort. To make a resilient building management system, it is important to identify the severity, cause, and type of each fault using fault diagnosis techniques. The first step for a building diagnostic framework is the designing of tests. To make a test, data are required from different sensors. Due to a battery problem, sensors yield gaps. The delay between two data sent depends on the measured value and the type of sensor and the question that arises is from which delay, a sensor becomes faulty? This is a challenge. In a building system, there is no universal test, but there are contextual tests with limited validity. These local contexts are measured with potentially faulty sensors, and the problem is how to conclude about a test that can be valid or not knowing that validity can only be tested with possibly faulty sensors? This is also a complex problem to solve.

At the most fundamental level, smart buildings deliver useful building services. They aim to give occupants a good level of comfort (e.g., thermal comfort, air quality, and more) at low cost and low environmental impact. Most approaches and solutions to this problem promote automated and autonomous systems that take full control of the decisions. Although these systems relieve users from setting the temperature in offices and homes, the level of thermal comfort chosen by the technology may not meet the individuals’ requirements. Automating control is an obvious solution, but it keeps the human out of the loop. And occupant activities play a significant role in building energy consumption. A gap has been created between EMSs (Energy Management Systems) and occupants but cooperation is necessary. Neither the occupant nor the EMS can solve the problem alone: the occupant knows his/her intentions and goals; the EMS knows the physics that can help the user to fulfill his/her requirements. To make this possible requires not only developing intelligent energy systems but also needs the involvement of occupants in the loop together with the EMS and the building environment. Rather than “doing instead-of” (automation), we promote a doing together approach (cooperation) where the inhabitant (i.e., user) is involved in the final decisions and actions. However, decisions and actions require that users understand the EMS functioning and rationale. The embedded knowledge in the EMSs cannot be easily shared with occupants because they are designed and made by engineers with the main objective of optimizing the energy consumption. To assist the occupants to better understand their EMSs, this work will promote the explanations as a tool to enhance the cooperation between the EMSs and occupants. Explanations are a powerful and intuitive tool to transfer knowledge from the EMS to the end user. The explanation module needs to be able to automatically extract the knowledge from the EMSs to allow occupants to understand which criteria the EMS is acting upon when making decisions. Additionally, it should provide occupants with feedback about the impact of their actions and decisions on energy consumption and their comfort criteria. The main objective is to enable the user to make informed choices and decisions. In this chapter, we will investigate two approaches to generate causal explanations from the environment from EMSs and sensors information.

This chapter is an introductory chapter that states the main issues appearing in building energy management based on practical examples. Each issue is going to be deeper investigated in the next chapters. It starts by stating the problem in proposing modeling concepts and a global formalism independent of the type of chosen model for a building system including models related to envelopes, appliances, controls, and human practices. Then the different types of models are discussed together with their respective advantages and drawbacks. It covers the model parameter estimation issues, which are often underestimated in the scientific literature: either the model is assumed to be known, which is usually unrealistic in real life, and it is used to generate datasets, or simple model like rule-based model is used for real applications. Energy management algorithms using complex models are difficult to solve in practice. Different ways for generating optimized energy strategies are presented and discussed and a promising case-based optimized approach is presented: it makes it possible to easily solve real applications. The latter approach can be advantageously combined with input-output model based approaches.

Energy consumption in buildings is affected by various factors including its physical characteristics, the appliances inside, and the outdoor environment, etc. However, inhabitants’ behaviour that determines the global energy consumption must not be forgotten. In most of the previous works and simulation tools, human behaviour is modelled as occupancy profiles. In this chapter, the focus is more on detailed behaviour representation, particularly the cognitive, reactive, and deliberative mechanisms. The inhabitants’ dynamic behaviour is modelled and co-simulated together with the physical aspects of a building and an energy management system. The analysis of different household appliances has revealed that energy consumption patterns are highly associated with inhabitants’ behaviours. Data analysis of inhabitants’ actions and appliances’ consumptions is used to derive a model of inhabitants’ behaviour that impacts the energy consumption. This model represents the cognitive mechanisms that provide causes that motivate the actions, including the communication with other inhabitants. An approach based on multi-agent systems is developed along with a methodology for parameter tuning in the proposed behaviour model. These tools are used to co-simulate, not only the physical characteristics of the building, the reactive behaviour that is sensitive to physical data, and deliberative behaviour of the inhabitants, but also the building energy management system. The energy management system allows the direct adjustment of the building parameters or simply giving advice to the inhabitants. The impact of different types of inhabitants’ behaviours, with and without the inclusion of an energy management system is analysed. This work opens new perspectives not only in the building simulation and in the validation of energy management systems but also in the representation of buildings in the smart grid where signals can be sent to end users advising them to modulate their consumption.

Data centers provide the global community withan indispensable service: nearly unlimited access to almost any kind of information we can imagine. With the increasing demand of information, the high energy costs has raised the issue of power consumption. The main goal in achieving data center energy efficiency can be translated into the ensuring that IT equipment uses, as maximum power as possible from the power entering the data center to compute useful work but at the same time the service level agreement between costumers and managers has to be satisfied. In this paper, different concepts of efficiency that are linked with multiple methods of increasing energy efficiency into a data center, will be presented, as well as different principles of energy management. A comprehensive overview of the currently available energy metrics will be provided, followed by an in-depth analysis of their commonalities and differences. An analyze about the degree of freedom encountered into the process of energy management, will be afforded. This paper presents a global overview of the energy efficiency management process into a data center.

This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.

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.

International audience; Multi-sources building is a complex system composed of different types of electrical sources, solar panels, batteries...etc. To optimally anticipate the energy management a lot of input data should be taken in account, like weather forecast, energy prices, load variation...etc. Furthermore anticipation may have different equivalent strategies as consequence of problem formulation. In this paper a stability study applying numeric analyses on the optimization problem of energy management is studied.

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

International audience; The purpose of this work is to study the impact of user defined data on the optimal sizing of PV-connected to grid. As the variety of inputs in such type of optimization problem is high, it is interesting to clustering them under hypotheses, and then manages the sensitivity analyses. In the first part three hypotheses was generated due to types of inputs, in the second part a combination of the hypotheses was studied for a defined study case. At the end a brief sensitivity analysis of the sizing PV grid connected system has been presented.

This paper presents a multi-agent approach dedicated to power management in buildings. This approach, defines a multi-layer control mechanism: reactive and anticipative mechanisms. In a first part, the paper presents the power system and its problems. Then, it presents the agents modeling of the proposed system, called MAHAS. In the second part of this paper, we show how a multi-agent system, well adapted to solve problems spatially distributed and opened, can dynamically adapt the consumption of energy to various constraints by exploiting the flexibilities of the services provided by domestic devices (services shifting, energy accumulating).

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

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.

This paper contributes to the design of intelligent buildings. A Multi- Agents Home Automation System (MAHAS) is proposed which controls appliances and energy sources in buildings. The objective of this paper is to show that by using intelligent agents related to appliances, it is possible to improve the energy consumption/ production in buildings. The proposed MAHAS system is characterised by its openness, its scalability and its capability to manage diversity. In this paper, we show how a multi-agent system, well adapted to solve problems spatially distributed and opened, can dynamically adapt the consumption of energy to various constraints by exploiting the flexibilities of the services provided by domestic devices (services shifting, energy accumulating). Finally, we conclude on the contribution of Multi-Agent approach to the power management problem.

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.

International audience; Maintenance and diagnosis of complex systems are common activities in the industrial world. Technological advances have led to a continuously increasing complexity of industrial systems. This complexity, which is due to an increasing number of components reduces in turn the reliability of plants. Therefore, fault diagnosis is becoming a growing field of interest. But fault diagnosis relies on sensors: effcient fault diagnosis procedures require a relevant sensor placement. This paper presents fundamental results for sensor placement based on diagnosability criteria. These results contribute to the design of sensor placement algorithms, which satisfies diagnosability specications.

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 Tabu Search's algorithm and how to reduce the problem complexity by dividing the whole problem into independent sub-problems.

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.

International audience; This paper focuses on Demand-Side load Management applied to the residential sector. A home automation system controlling household energy is proposed. It is decomposed into three layers: anticipation, reactive and device layers. This paper deals with an anticipation layer that allocates energy by taking into account predicted events. It consists in computing both the starting times of some services and in determining set points of others while satisfying the maximal power constraint. A Constraint Satisfaction Problem formulation has been proposed. Because the complexity is NP-Hard, a tabu search is used to solve the problem. It maximizes user comfort and minimizes energy cost. An application example is presented

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.

In complex industrial plants, there are usually lots of sensors and the modelling of the plant leads to lots of mathematical relations. Before using classical tools for fault detection, the first problem to solve is: what sensors and mathematical relations have to 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 automatically selecting relevant sensors and relations that may be used for the design of the different detection tests. This method, which is based on a structural analysis of the process, provides all the testable subsystems and permits the selection of the most interesting detection tests regarding detectability and diagnosticabillity criteria.

International audience; This paper presents PID tuning method. The proposed method is based on the performances represented by a permitted domain for time response of the controlled system. To reach these performances, the controller parameters have to satisfy inequality constraints which can be solved by linear matrix inequality (LMI) methods. A brief introduction reviews the principle of parameters adjustment of a controller due to the model method. An approximate method of adjustment with respect to inequality constraints is considered. An optimal version is then given

International audience; Static linear models characterized by bounded uncertainties on both the equation error and the parameters are studied. The additive equation error is assumed to belong to an interval while the parameters fluctuate inside a time-invariant bounded domain. An algorithm is proposed for evaluating different bounded domains. The algorithm can be extended to cope with the determination of the central value of the domain containing the parameters. Contrary to most of traditional estimators, the resulting estimator takes into account the distribution of the uncertainties of a model

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

Knowledge based models of induction machine lie on differential equations. In this paper, we intend to establish discrete time models which are relevant to fault detection. Different methods are under consideration among them, we introduce a new one which leads to better results than the usual 2nd order Taylor expansion of matrix exponential and which does not require computation of matrix exponential at each iteration. In addition, we propose an approximate method which propagates additive uncertainties from differential equations to discrete time models keeping their additive nature

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