Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects

Álvaro Sicilia Gómez; Gonçal Costa Jutglar

doi:10.3390/proceedings1071099

Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects

Producción científica: Capítulo del libro › Contribución a congreso/conferencia › revisión exhaustiva

Resumen

Energy efficient retrofitting projects of urban areas require to analyze data from multiple sources and domains—BIM, GIS, statistics, energy data, and climate. An interoperability solution is needed to overcome the semantic and structural heterogeneity of the data sources. Within OptEEmAL project, we have design and implemented a District Data Model which integrates multiple data sources and makes them interoperable with several simulation tools (Energy plus, Nest, CitySim) using Semantic Web technologies, namely, ontologies and SPARQL construct queries.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the Sustainable Places 2017 (SP2017)
Lugar de publicación	Middlesbrough, UK
Editorial	MDPI
Capítulo	1 (7)
DOI	https://doi.org/10.3390/proceedings1071099
Estado	Publicada - 5 dic 2017

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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title = "Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects",

abstract = "Energy efficient retrofitting projects of urban areas require to analyze data from multiple sources and domains—BIM, GIS, statistics, energy data, and climate. An interoperability solution is needed to overcome the semantic and structural heterogeneity of the data sources. Within OptEEmAL project, we have design and implemented a District Data Model which integrates multiple data sources and makes them interoperable with several simulation tools (Energy plus, Nest, CitySim) using Semantic Web technologies, namely, ontologies and SPARQL construct queries.",

keywords = "Energy efficiency retrofitting, semantic web, Linked Data, Data integration, Ontologies, SPARQL",

author = "\{Sicilia G{\'o}mez\}, {\'A}lvaro and \{Costa Jutglar\}, Gon{\c c}al",

year = "2017",

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T1 - Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects

AU - Sicilia Gómez, Álvaro

AU - Costa Jutglar, Gonçal

PY - 2017/12/5

Y1 - 2017/12/5

N2 - Energy efficient retrofitting projects of urban areas require to analyze data from multiple sources and domains—BIM, GIS, statistics, energy data, and climate. An interoperability solution is needed to overcome the semantic and structural heterogeneity of the data sources. Within OptEEmAL project, we have design and implemented a District Data Model which integrates multiple data sources and makes them interoperable with several simulation tools (Energy plus, Nest, CitySim) using Semantic Web technologies, namely, ontologies and SPARQL construct queries.

AB - Energy efficient retrofitting projects of urban areas require to analyze data from multiple sources and domains—BIM, GIS, statistics, energy data, and climate. An interoperability solution is needed to overcome the semantic and structural heterogeneity of the data sources. Within OptEEmAL project, we have design and implemented a District Data Model which integrates multiple data sources and makes them interoperable with several simulation tools (Energy plus, Nest, CitySim) using Semantic Web technologies, namely, ontologies and SPARQL construct queries.

KW - Energy efficiency retrofitting

KW - semantic web

KW - Linked Data

KW - Data integration

KW - Ontologies

KW - SPARQL

U2 - 10.3390/proceedings1071099

DO - 10.3390/proceedings1071099

M3 - Conference contribution

BT - Proceedings of the Sustainable Places 2017 (SP2017)

PB - MDPI

CY - Middlesbrough, UK

ER -

Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects

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