Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects†
by
Álvaro Sicilia * and Gonçal Costa
Álvaro Sicilia * and Gonçal Costa
ARC, La Salle Engineering and Architecture, Ramon Llull University, 08022 Barcelona, Spain
*
Author to whom correspondence should be addressed.
†
Presented at Sustainable Places 2017 (SP2017) Conference, Middlesbrough, UK, 28–30 June 2017.
Proceedings 2017, 1(7), 1099; https://doi.org/10.3390/proceedings1071099
Published: 5 December 2017
(This article belongs to the Proceedings of the Sustainable Places 2017 (SP2017) Conference)
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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Sicilia, Á.; Costa, G. Energy-Related Data Integration Using Semantic Data Models for Energy Efficient Retrofitting Projects. Proceedings 2017, 1, 1099.
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