Centre de documentation
scientifique

Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 9 (September 2022) . - pp 593 - 601 Titre : Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques Type de document : Article/Communication Auteurs : Wang Yue, Auteur ; Changgang Yu, Auteur ; A. Antonidoss, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 593 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] capteur (télédétection) [Termes IGN] économie d'énergie [Termes IGN] internet des objets [Termes IGN] performance énergétique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'aide à la décision [Termes IGN] ville durable [Termes IGN] ville intelligente Résumé : (auteur) In modern society, energy conservation is an important consideration for sustainability. The availability of energy-efficient infrastructures and utilities depend on the sustainability of smart cities. The big streaming data generated and collected by smart building devices and systems contain useful information that needs to be used to make timely action and better decisions. The ultimate objective of these procedures is to enhance the city's sustainability and livability. The replacement of decades-old infrastructures, such as underground wiring, steam pipes, transportation tunnels, and high-speed Internet installation, is already a major problem for major urban regions. There are still certain regions in big cities where broadband wireless service is not available. The decision support system is recently acquiring increasing attention in the smart city context. In this article, a deep learning–based sustainable decision support system (DLSDSS) has been proposed for energy building in smart cities. This study proposes the integration of the Internet of Things into smart buildings for energy management, utilizing deep learning methods for sensor information decision making. Building a socially advanced environment aims to enhance city services and urban administration for residents in smart cities using remote sensing techniques. The proposed deep learning methods classify buildings based on energy efficiency. Data gathered from the sensor network to plan smart cities' development include a deep learning algorithm's structural assembly of data. The deep learning algorithm provides decision makers with a model for the big data stream. The numerical results show that the proposed method reduces energy consumption and enhances sensor data accuracy by 97.67% with better decision making in planning smart infrastructures and services. The experimental outcome of the DLSDSS enhances accuracy (97.67%), time complexity (98.7%), data distribution rate (97.1%), energy consumption rate (98.2%), load shedding ratio (95.8%), and energy efficiency (95.4%). Numéro de notice : A2022-812 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00010R2 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.14358/PERS.22-00010R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101972 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022091	SL	Revue	Centre de documentation	Revues en salle	Disponible

Sense City, mini ville sensible expérimentale / Marielle Mayo in Géomètre, n° 2153 (décembre 2017)

Public [article]  inGéomètre > n° 2153 (décembre 2017) . - pp 46 - 48 Titre : Sense City, mini ville sensible expérimentale Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2017 Article en page(s) : pp 46 - 48 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Urbanisme [Termes IGN] changement climatique [Termes IGN] dégradation de l'environnement [Termes IGN] lutte contre la pollution [Termes IGN] milieu urbain [Termes IGN] modélisation environnementale [Termes IGN] performance énergétique [Termes IGN] phénomène climatique extrême [Termes IGN] rayonnement infrarouge thermique [Termes IGN] ville intelligente Résumé : (Auteur) Dans l'Est parisien, une mini-ville intelligente, unique en Europe, équipée de nanocapteurs, va servir de laboratoire d'innovations au service de la ville durable. Numéro de notice : A2017-761 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88792 [article]

Energy planning tools and CityGML-based 3D virtual city models: experiences from Trento (Italy) / Giorgio Agugiaro in Applied geomatics, vol 8 n° 1 (March 2016)  

Public [article]  inApplied geomatics > vol 8 n° 1 (March 2016) . - pp 41 - 56 Titre : Energy planning tools and CityGML-based 3D virtual city models: experiences from Trento (Italy) Type de document : Article/Communication Auteurs : Giorgio Agugiaro, Auteur Année de publication : 2016 Article en page(s) : pp 41 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bâtiment [Termes IGN] caractérisation [Termes IGN] CityGML [Termes IGN] consommation [Termes IGN] intégration de données [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] outil d'aide à la décision [Termes IGN] performance énergétique [Termes IGN] Trente [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) This article presents the first results concerning the development and implementation of a tool for the estimation of the energy performance for residential buildings at city scale. Space heating and domestic hot water production are taken into account. Project “EnerCity” focuses on two main topics: (a) the creation of a CityGML-compliant 3D city model from sub-optimal datasets and (b) its adoption as information hub to develop energy-related assessment tools. A part of the city of Trento, in northern Italy, was chosen as the case study area for testing purposes; however, the methodology was developed to be extended to the whole city. Only publicly available data were used. The energy demand calculation method is based on the Italian Technical Specifications UNI/TS 11300:2008. For each building, the primary energy demand for space heating and domestic hot water production, as well as the resulting energy performance index, are estimated. In order to characterise the buildings, heterogeneous datasets (cadastral data, statistical data, etc.) were harmonised and integrated. All residential buildings were successively classified into distinct building types according to the criteria defined for Italy in the European project “Tabula”. The developed tool allows for data visualisation, editing as well as interactive refurbishment of the buildings. The article describes all relevant steps of the project and discusses possible enhancements and the future improvements. Numéro de notice : A2016--057 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-015-0163-2 En ligne : http://dx.doi.org/10.1007/s12518-015-0163-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84205 [article]