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Heating, ventilation, and air conditioning (HVAC) systems consume nearly 40% of the total energy consumption in developed countries. Traditional techniques such as rule based control (RBC) fail to control these systems in an optimal way. Model predictive control (MPC) has been widely explored in literature as well but it doesn't represent a practical solution due to the complexity of buildings' dynamics that it relies on. Recently, deep reinforcement learning (DRL) has shown great success in the domain of optimal control such as robotics and gaming. In this paper, we develop two model-free DRL approaches to optimize the energy consumption of an office while maintaining thermal comfort and good indoor air quality through controlling the radiator and the opening/closing of a window and a door existing in the office. The two DRL approaches belong to deep-Q network (DQN): the first approach represents a DQN agent with no knowledge of the environment and the second approach represents a DQN agent with initial knowledge of the environment: A hybrid approach DQN+RBC. The goal of having external knowledge in DQN agent is to boost convergence by exploiting the RBC rules. We evaluate the performance of these two approaches against an RBC approach through simulations using a physical model of the office's dynamics. Experiments show that the two DRL approaches succeeded to maintain better thermal comfort and better indoor air quality compared with RBC approach while consuming nearly the same energy. In addition, experiments demonstrate that the DQN with knowledge outperforms the DQN with no knowledge in the beginning and converges faster to the optimal value.

Similar to other engineering systems, buildings are also complex systems. The whole building system is highly vulnerable to different kinds of faults, failures and human misbehavior that could affect the building performance. The available fault diagnosis tools and methodologies particularly rely on rule or pure model-based approaches. It is assumed that the model could be applied to any situation without taking into account actual contexts. Setting a complete model with all contextual representations is a challenging job. On the other hand, unreliable instrumentation and non-formalization of the expert's knowledge are also challenging ones. The objective of this paper is to highlight the challenges that lie in this problem domain, as well as to provide a strategy about how to solve them.A real case study has been investigated to exemplify the new trends in buildings: a classroom in the University of Southern Denmark.

According to experts, the average temperature of the planet has increased at an unprecedented and alarmingly high rate over the last fifty (50) years. Carbon emissions have been found to be a major catalyst for climate change and the energy sector one of the highest emitters globally. Thus any significant reduction in energy related emissions would have a significant impact on global carbon emissions and consequently global warming. UN-Habitat estimates that approximately 56% of energy produced in most African nations is consumed in buildings. There is a need for energy efficiency and possibly conservation in buildings since they represent the single largest consumer of energy on the continent. Net Zero Energy Buildings (NZEBs), a possible solution for reducing the energy footprint of buildings, represents the evolution of buildings in the near future. The Zero energy concept has a major impact on the design and construction of future buildings. This paper focuses on the review and development of existing Load Match Indicators for zero energy buildings. Four indicators are provided and discussed (i.e. self-consumption, self-production, loss of load probability, and coverage rate indicators). For the purpose of this paper, Predis-MHI (a platform of G2ELab) was used as a case study. Data was collected from the platform’s living lab and was used in the calculation and evaluation of these indicators. The results indicate the relevance of each indicator in evaluating the energetic performance of a building and also highlight the practical difficulties faced in evaluating the platform.

The research presented in this paper is a first investigation of the application of online unsupervised learning techniques in the case of smart buildings. An online learning approach is proposed to estimate the number of people (within a range) in a room based on data collected from standard sensors. This estimated range is changing and depends on the highest occupancy faced in a training dataset for instance. Occupant behavior is modeled using an online learning approach. The resulting algorithm makes use of recorded sensor data for motion detection, power consumption, and door position as well as acoustic pressure from a microphone. This paper presents an online algorithm for a mixture model-based clustering which has been applied to estimate the occupancy in an office context, with an excellent estimation accuracy.

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.

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.

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.

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.

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.

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.

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.

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.

This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose user's behavior in housing. Some results and perspectives are presented in this paper.

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.

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.

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

This paper proposes a co-simulation architecture based on software component standard. It proposes a pattern to unify the co-simulation interfaces of several building energy simulation tools. It will thus facilitate and generalize the establishment of a co-simulation. Advantages of this strategy are highlighted through the coupling between the software COMFIE for thermal envelope simulation and the tool BRAHMS for occupant behaviour simulator, in the Matlab/Simulink environment. Linking these two tools is essential to take profit from their specific skills in each area and ensure a detailed simulation of both precise physical models and computational models of human cognitive abilities. This is needed to ensure an efficient building control taking into account the complexity of this system

International audience; The goal of this paper is to propose a real time solution to validate energy smart-home strategies that support Demand Side Management done by direct control. The validation concerns the appliances under control. An Energy Management System globally controls the appliances of a house, satisfying the subscription power limits. A hardware-in-the-loop test bench, based on a real-time simulator called RT_LAB, is proposed to exemplify the validation mean: it is used to assess the performances of an energy management strategy for a thermal zone based on the control of a heater. Physical devices are introduced into the simulated system to check whether they can support the control strategies.