Immotique

Résumé

L'Immotique est un néologisme formé à partir du mot immeuble et du suffixe -tique, qui connote l'informatique et l'électronique voire l'automatique etc. Comme la domotique, ce domaine est issu de la réunion de l'électronique, de l'informatique et des télécommunications. Autant que possible et théoriquement, l'immotique est un service et une somme de processus qui devraient être rendu de manière transparente ou discrète, fiable et non-intrusive pour les usagers et visiteurs d'un bâtiment. Elle peut aussi avoir une vertu pédagogique et d'aide et conseil, par exemple en affichant et expliquant les anomalies de consommation d'énergie (ou de production dans les bâtiments équipés pour produire tout ou partie de leur énergie). Histoires et concepts connexes Ce concept est apparu parallèlement au lent développement de la domotique et de la gestion technique de bâtiment, notamment dans les années 1990. Il peut s'agir d'un immeuble d'habitation, ou d'un immeuble tertiaire ou de

Source officielle

https://fr.wikipedia.org/wiki/Immotique

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (41)

Publications associées

Chargement

Personnes associées (5)

, , , ,

Personnes associées

Chargement

Unités associées (4)

Unités associées

Chargement

Concepts associés (16)

Concepts associés

Chargement

Cours associés (9)

Cours associés

Chargement

Séances de cours associées (13)

Séances de cours associées

Chargement

Robust Learning Model Predictive Control for Periodically Correlated Building Control

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems. To fully exploit their potential while ensuring occupant comfort, a robust control scheme is required to handle various uncertainties, such as external weather and occupant behaviour. However, prominent patterns, especially periodicity, are widely seen in most sources of uncertainty. This paper incorporates this correlated structure into the learning model predictive control framework, in order to learn a global optimal robust control scheme for building operations.

2021

Chargement

People in developed countries spend today most of their time inside buildings as part of the modern way of life. As a result, the building sector accounts for almost 40% of the total energy consumption and a big part of the energy bill goes to maintain the visual and thermal comfort of their occupants. At the same time, awareness is being raised during the last decades about the greenhouse gas emissions and the possibly irreversible effects of global warming; both linked to excessive use of non-renewable primary energy sources which still power most of the world, including our buildings. Thus, moderating the energy consumed in them is a top priority. However, this does not imply a horizontal cut in energy consumption that would result in a drop of user comfort. Instead, we suggest that improving energy efficiency in buildings while maintaining or even improving the user comfort is the optimal solution. It is indeed the core of this thesis that there is a great energy saving potential in refining the control of building systems such as electric lighting, heating, cooling and ventilation, which more than often consume a lot of energy without delivering the analogous amount of visual and thermal comfort. In this direction, this thesis proposes the development of a novel predictive control algorithm for the control of electrochromic glazing using a low cost sky scanner using a simple web camera. The developed algorithm demonstrated an average prediction accuracy of 92% and integrates and controls the blinds and electric lighting to maximise visual comfort taking into account outdoor and indoor conditions, presence and user actions. Measurements and extensive simulations showed that the elaborated algorithm improves thermal and visual comfort when compared to standard glazing coupled with blinds and exhibits acceptable levels of energy consumption for space heating and electric lighting. In the same subject of improving building control, a novel approach for controlling building systems by using state-based stochastic data-driven models to identify "season" is defined and developed. We reason that the season variable is unique to every building and it depends on weather conditions, user behaviour and building construction. The developed models identified "season" with an accuracy that ranged from 69 to 91% and it was shown through simulations that a controller based on Hidden Markov Models can reduce energy demand for heating and improve the thermal comfort of occupants in different building construction types. Finally, the use of Hidden Markov Models was further explored in this thesis by suggesting a novel model for the estimation of occupants' visual comfort in buildings. The proposed model is based on horizontal workplane illuminance measurements using ceiling-mounted sensors as well on vertical illuminance monitoring at the observer's eyes plane (pupillary illuminance) by means of wearable portable sensors. We argue that the proposed model improves greatly over the various existing discomfort glare indices and metrics and it is also convincingly demonstrated that it can be seamlessly integrated and used in building automation systems based on fuzzy logic.

EPFL2015

Chargement

Integrated Daylighting and Artificial Lighting Control based on High Dynamic Range Vision Sensors

Ali Motamed

One fifth of the electricity consumption of Swiss buildings is due to electric lighting. Integrated control of sun shading and artificial lighting can mitigate this demand while maintaining user comfort. However, the drawback of existing building control approaches is that they do not consider one of the main aspects of human-centric lighting: visual comfort. The goal of this doctoral thesis is to develop an integrated energy efficient sun shading and electric lighting control system that incorporates widely accepted visual comfort criteria and privileges daylighting over electric lighting. The first part is dedicated to High Dynamic Range (HDR) vision sensor calibration, programing, validation and preliminary testing. A sensor originally developed by the Centre Suisse dâElectronique et de Microtechnique (CSEM) was photometrically, spectrally and geometrically calibrated and validated with respect to reliable illuminance and multi-point luminance meters. This HDR vision sensor was then equipped with an embedded image processing routine in order to assess âon the flyâ discomfort glare indices. It has been demonstrated that the developed device, is able to serve as an enhanced visual comfort feedback sensor in building automation systems. On the other hand, it can be employed to characterize highly glazed facades and workspaces regarding visual comfort and glare risks, as demonstrated in a project in Singapore. Two monitoring campaigns are reported in the second part of this thesis. Firstly, 30 human subjects occupied two identical office rooms of the LESO solar experimental building for 15 afternoons to compare the performance of a fuzzy logic control system incorporating two HDR vision sensors with respect to a âbest-practiceâ controller. Subjective self-reported visual comfort surveys, paper- and computer-based visual tests and monitoring of the electric lighting consumption were carried out simultaneously in both offices. It was shown that the electricity demand of the office with the advanced controller is 32% lower than that of the reference room, while the subjectsâ visual performance remained comparable. Secondly, an eight-month data monitoring campaign was carried out in the same building in order to study the ability of a novel control approach to maintain optimal visual and thermal comfort conditions while reducing the energy performance gap of a room as well as its electric lighting demand. The experimental results showed that the advanced controller mitigated the performance gap during the heating season by 72% with regard to standard occupant behavior and by 19% with respect to a best-practice automated system. This system reduced backup heating demand leading to lower CO2 gas emissions. At the same time, visual comfort constraints regarding Daylight Glare Probability (DGP) and workplane horizontal illuminance were respected during work hours. Finally, a self-commissioning integrated controller for Venetian blinds enhanced with a learning module was developed and validated for 22 days in a daylighting testbed at the Fraunhofer Institute for Solar Energy (ISE) in Freiburg, Germany. It has been shown that the visual comfort constraints are respected for 96% of the work hours and that the controller can successfully limit the number of shading movements. The market potential for HDR vision sensors and integrated control platforms has been studied and possible commercialization tracks have been identified.

EPFL2017

Chargement

Integrated Daylighting and Artificial Lighting Control based on High Dynamic Range Vision Sensors

Ali Motamed

EPFL2017

EPFL2015