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

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Resum

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	Anglès
Títol de la publicació	Proceedings of the Sustainable Places 2017 (SP2017)
Lloc de publicació	Middlesbrough, UK
Editor	MDPI
Capítol	1 (7)
DOIs	https://doi.org/10.3390/proceedings1071099
Estat de la publicació	Publicada - 5 de des. 2017

SDG de les Nacions Unides

Aquest resultat contribueix als següents objectius de desenvolupament sostenible.

ODS 7 Energia neta i assequible

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@inproceedings{c6434fcf33bd4518932f9462e88472bd,

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",

month = dec,

day = "5",

doi = "10.3390/proceedings1071099",

language = "English",

booktitle = "Proceedings of the Sustainable Places 2017 (SP2017)",

publisher = "MDPI",

address = "Switzerland",

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TY - GEN

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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SDG de les Nacions Unides

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