Posts tagged "smart buildings"

IRIT and  LMDC , Toulouse University

Keywords

Anomaly detection, multi-agent system, smart buildings, energy management, data stream

This research project deals with energy efficiency in buildings to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some existing software can record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts. To improve the energy management, an innovative anomaly detection system should aim at analyzing raw data, detect any kind of anomalies (point, contextual, collective) in an open environment, at large scale. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection with Multi-AgeNt systems). The system is semi-supervised by an expert of the field who confirms or overturns the feedback of SANDMAN. It processes data in a time constrained manner to detect anomalies as early as possible. SANDMAN is based on the paradigm of self-adaptive multi-agent system. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.  

Scientific goal

Anomalies detection in smart buildings streaming data by a semi-supervised multi-agent system.

Contacts

stephanie.combettes@irit.fr, berangere.lartigue@univ-tlse3.fr, marie-pierre.gleizes@irit.fr, corentin.tourne@irit.fr, valentin.lavigne@irit.fr

Context Presentation

This research project deals with energy efficiency in buildings to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exists to record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders.

The system designed in the SANDFOX project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy man-agers, researchers, technical staff or end-users of these buildings.

The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection withMulti-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. SANDMAN is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.

Keywords

Anomaly detection, dashboard, multi-agent system, smart buildings, energy management

Scientific goals

•    Anomalies detection in smart buildings streaming data by AI,

•    Restitution of the information to different stakeholders through an adapted dashboard.

Contacts

berangere.lartigue@univ-tlse3.fr, stephanie.combettes@irit.fr, marie-pierre.gleizes@irit.fr,  mathieu.raynal@irit.fr

Context Presentation

The measurement of indoor air quality is important for health protection against chemical and gaseous pollutants ... The indoor air can contain many pollutants such as CO, CO2, NO2 and VOCs. These pollutants exist in different materials and products that can be used in housing (furniture, cleaners ...), but can be also coming from human activities or outside source. In this case, the detection, measurement and monitoring of these gazeuse contaminants is necessary.

In view of its high performance and low cost, the innovative gas multi-sensor based on metal oxides semiconductors for analyzing and controlling indoor air quality is a good alternative to electrochemical and infrared sensors. This project is currently in progress in LAAS in collaboration with the LCC and Laplace and as part of a thesis funded by neOCampus and the Occitanie region.

This thesis focuses on the characterization of multiple MOX-based gas sensors and integrates these multi-sensors in electronic card to achieve a connected object to control the indoor air quality in offices and classrooms in University Paul Sabatier in Toulouse. The gas multi-sensor is a microsystem composed by four sensors on a microchip, realized to detect target gases.  

Keywords

Multi-sensors, MOS, Indoor Air Quality, Smart Building, neOCampus

Scientific goals

•    To characterize new nanomaterials (SnO2, CuO, ZnO, WO3 ...) designed by the LCC by using an experimental set-up,

•    To define an operating protocol by trying different operating modes.

Contacts

aymen.sendi@live.fr, menini@laas.fr, pierre.fau@lcc-toulouse.fr, katia.fajerwerg@univ-tlse3.fr, myrtil.kahn@lcc-toulouse.fr, vincent.bley@laplace.univ-tlse.fr

Context Presentation

On site access control exists almost everywhere in today's modern world. Keys have already been replaced by access cards in most places but accessOCampus aims to improve on these existing systems even more.

For less than 200€ (excluding the optional thermal camera) an accessOCampus client module can be built, this includes : NFC access card recognition, passcode entry, face recognition and a large screen for access related information. The system can also be locked down or forced open globally by an authorized security personnel via the easy to use web interface.

In theory, accessOCampus can be used to log who has entered any given zone and provide detailed information on those people. For instance, if any given access card is stolen we can detect who used it via face recognition either silently or to refuse access.This project could be implemented in select zones of Toulouse University to provide a low-cost high fidelity access system.

Keywords

neOCampus, IOT, Smart Building, Security, Access, Face Recognition

 Scientific goals

•    Provide a high security solution to restricted zones

•    Facilitate zone entry for the end user via face recognition•    Display easy to understand information

Contacts

gomme600@hotmail.com, Francois.Thiebolt@irit.fr

Context Presentation

With the environmental issues and the new ecological considerations, one of the challenge is the creation of sustainable electric grid to supply the demand. With this context, we observe the deployment of decentralized Low Voltage DC Micro-Grid (LVDC-MG) in building, with high penetration of Renewable Energy Sources (RES) and Energy Storage Systems (ESS). The aim of this PhD thesis is to contribute in this field by designing an LVDC MG in the ADREAM Building integrated PV (BiPV), at LAAS-CNRS, TOULOUSE. The main difficulties is to combined the ESS behavior and aging studies with a global system approach in order to proposed a sizing method and an energy management strategy optimized and simple to implemented for electrical research community.

Figure 1: electrical synoptic of the LVDC MG study

Scientific Goals

- Study the impacts of BiPV and DC building loads power profiles on ESS behavior and lifecycle

- Proposed a methodology, with a systemic approach, to size the PV and the ESS

- Compared multiple sizing and energy management strategy in order to design the optimal LVDC MG to supply the ADREAM BiPV lightning network

- Compared the performances of Lead acid batteries and Lithium-ions batteries in our case study

Keywords

Energy Storage System, Low Voltage DC Micro Grid, Building integrated PV, Lead-acid batteries, ageing mechanisms

Contacts

PhD student: mgaetani@laas.fr / Supervisors: alonsoc@laas.fr & jammes@laas.fr

Context Presentation

In previous decades, environmental impact control through lifecycle analysis has become a hot topic in various fields. In some countries, such as France, the key figures for energy show that the building sector alone consumes around 45% of the energy produced each year. From this last observation emerged the idea to improve the methods hitherto employed in this field, in particular those related to the exchange of information between the various stakeholders involved throughout the lifecycle of a building. Information is particularly crucial for conducting various studies around the building; for instance, the assessment of the environmental impact of the latter. Concerning information exchange issues, the creation of open standards such as Industry Foundation Classes (IFC) or CityGML, but also semantic web technologies have been widely used to try to overcome it with some success elsewhere. Another striking issue is the heterogeneity between construction product databases. What would be particularly interesting is to know the environmental impact of a building at early phases of its lifecycle. However, there are a number of problems that still do not have solutions. This includes associating Building Information Modelling (BIM) and semantic web technologies with environmental databases to increase the flexibility needed to assess the building's environmental impact throughout its lifecycle.

Figure 1: MINDOC methodology process

Scientific Goals

- Study how information exchange is made within experts during a building lifecycle in order to figure out interoperability gaps ;

- Fill some of the encountered gaps by mean of formalization of building information.

- Combined with the formalization of environmental data on construction products, the latter will enable the introduction of product data at an early stages of the building lifecycle.

Keywords

Knowledge Modeling & Semantic Reasoning - Merging Ontologies - Decision Support - Building Information Modeling (BIM) - Environmental Databases.

Contacts

justine-flore.tchouanguem-djuedja@enit.fr, Bernard.Kamsu-Foguem@enit.fr, camille.magniont@iut-tarbes.fr, mkarray@enit.fr, fabanda@brookes.ac.uk.

Context Presentation

The measurement of indoor air quality is important for health protection against chemical and gaseous pollutants ... The indoor air can contain many pollutants such as CO, CO2, NO2 and VOCs. These pollutants exist in differents materials and products that can be used in housing (furniture, cleaners ...), but can be also comming from human activities or outside source. In this case, the detection, measurement and monitoring of these gazeuse contaminants is necessary.In view of its high performance and low cost, the innovative gas multi-sensor based on metal oxides semiconductors for analyzing and controlling indoor air quality is a good alternative to electrochemical and infrared sensors. This project is currently in progress in LAAS in collaboration with the LCC and Laplace and as part of a thesis funded by neOCampus and the Occitanie region. This thesis focuses on the characterization of multiple MOX-based gas sensors and integrates these multi-sensors in electronic card to achieve a connected object to control the indoor air quality in offices and classrooms in University Paul Sabatier in Toulouse

  Figure 1 : « MOX gas Multi-sensors»

Scientific Goals

The gas multi-sensor is a microsystem composed by four sensors on a microchip, realized to detect target gases. The scientific objective of this thesis is to characterize new nanomaterials (SnO2, CuO, ZnO, WO3 ...) designed by the LCC by using an exprimental set-up and to define an operating protocol by trying differents operationg modes.

Keywords

Multi-sensors,  MOS, Indoor Air Quality, Smart building, neOCampus

Contacts

aymen.sendi@laas.fr

menini@laas.fr

pierre.fau@lcc-toulouse.fr

katia.fajerwerg@univ-tlse3.fr

myrtil.kahn@lcc-toulouse.fr

vincent.bley@laplace.univ-tlse.fr

Présentation du contexte

La réalité d'un campus intelligent ou plus généralement d'une ville intelligente passe par une observation régulière de l'environnement par des capteurs ad-hoc, afin d’agir dans l’environnement avec des dispositifs automatiques pour améliorer le bien-être des usagers. Ces capteurs permettent d’obtenir une connaissance des activités humaines et des conditions dans lesquelles ces activités sont menées, mais le déploiement d'un grand nombre de capteurs peut être coûteux. Les coûts sont principalement liés à l'installation, la maintenance et les infrastructures de capteurs dans les bâtiments existants. Pour ces raisons, l’objectif de cette thèse vise à réduire ces coûts en utilisant quotidiennement des milliers d’informations partielles et intermittentes provenant de smartphones des usagers du campus de l’Université Toulouse III Paul Sabatier. Ces traitements sont fondés sur une technologie d’Intelligence Artificielle par systèmes multi-agents coopératifs.

Figure 1 : «On utilise les informations des dispositifs intermittents et mobiles pour fournir des estimations précises»

Objectifs scientifiques

- Apprendre à partir de données brutes, imprécises et intermittentes sans feedback.

- Fournir les informations en continu, même en l’absence de données de smartphone des usagers.

- Utiliser une approche hybride de l’Internet des objets qui mixe capteurs réels et capteurs virtuels.

Mots clés

Systèmes multi-agents auto-adaptatifs, fusion de données, apprentissage, smart campus

Contacts

Davide Andrea Guastella, Valérie Camps, Marie-Pierre Gleizes, {davide.guastella, camps, gleizes}@irit.fr

La mesure de la qualité de l'air intérieur est importante pour la protection de la santé contre les polluants chimiques, gazeux ... En effet, l'air intérieur peut contenir plusieurs polluants tels que les CO, CO2, COVs. Ces polluants existent dans plusieurs matériaux et produits utilisables dans les logements (les meubles, nettoyants...), mais peuvent aussi être issus des activités humaines. Dans ce cas, la détection, la mesure et la surveillance de ces polluants sont nécessaires. Au vue de ses performances  élevées et son faible coût, le multi-capteur de gaz innovant pour l'analyse et le contrôle de la qualité d'air intérieur est une bonne alternative aux capteurs  électrochimiques et infrarouges. Ce projet est en cours de réalisation au sein du LAAS en collaboration avec le LCC et Laplace dans le cadre d’une thèse financée par neOCampus et la région Occitanie. Cette thèse porte essentiellement sur la caractérisation des multi-capteurs de gaz à base MOX et d’intégrer ces multi-capteurs dans son environnement électronique pour réaliser un objet connecté afin de contrôler la qualité de l'air intérieur dans les bureaux et les salles d'enseignements de l’université Paul Sabatier.

Figure 1 : « Multi-capteurs du gaz à base des oxydes métalliques»

Objectifs scientifiques

Le multi-capteur de gaz est un microsystème composé, de quatre capteurs sur une micro puce, destiné à détecter des gaz cibles. L'objectif scientifique de cette thèse est de caractériser des nouveaux nanomatériaux (SnO2, CuO, ZnO) conçus par le LCC en utilisant un banc de caractérisation afin de définir un protocole de fonctionnement  et d'analyse des données en choisissant un profil optimal de détection des gaz cibles en utilisant différentes modes de fonctionnement.

Contacts

aymen.sendi@laas.fr, menini@laas.fr, pierre.fau@lcc-toulouse.fr, katia.fajerwerg@univ-tlse3.fr

myrtil.kahn@lcc-toulouse.fr, vincent.bley@laplace.univ-tlse.fr

L’habitat du futur est une préoccupation actuelle qui a plusieurs objectifs dont celui du suivi et du contrôle intelligents de la consommation énergétique. En effet, il est possible aujourd’hui d’équiper la maison de capteurs connectés en réseau, pour acquérir une meilleure connaissance de la consommation énergétique des équipements mais également pour donner à l’utilisateur la possibilité de piloter son habitat via des commandes envoyées aux actionneurs à travers une tablette ou un téléphone. Cette connaissance permet aussi d’identifier des profils de comportements permettant d’optimiser la consommation d’énergie.

L’étape suivante consiste à rendre le système intelligent pour que ce soit lui qui décide des ordres à passer au système afin d’optimiser le confort, la sécurité, la sûreté et les économies d’énergies.

Figure 1 : systèmes de gestion de l'énergie dans la maison intelligente 

Objectifs scientifiques

Les objectifs du stage sont :

 Déploiement d’un réseau de capteurs hétérogènes pour le suivi de la consommation d’énergie dans un habitat.

 Conception  d’un prototype pour le pilotage de l’autoconsommation.

Contacts

Abdelhadi.bentayeb@irit.fr, kacimi@irit.fr, berangere.lartigue@univ-tlse3.fr, philippe.rerat@habitat-energies.com