=Paper=
{{Paper
|id=Vol-3633/industry3
|storemode=property
|title=An open endpoint and framework for the development of linked data for building energy
systems
|pdfUrl=https://ceur-ws.org/Vol-3633/industry3.pdf
|volume=Vol-3633
|authors=James Allan,JongGwan An,Reto Fricker,Sascha Stoller,Philipp Heer
|dblpUrl=https://dblp.org/rec/conf/ldac/AllanAFSH23
}}
==An open endpoint and framework for the development of linked data for building energy
systems==
https://ceur-ws.org/Vol-3633/industry3.pdf
An open endpoint and framework for the development of linked
data for building energy systems
James Allan 1, Jong Gwan An1, Reto Frickerr 1 , Sascha Stoller 1 and Philipp Heer 1
1
Urban Energy Systems Laboratory, Empa, Ueberlandstrasse 129, 8600 Dübendorf, Switzerland
Abstract
The NEST building is a research and innovation building in Switzerland. Data collected by the
NEST building is available for researchers and organisations to develop models and test
applications. This data resource includes an endpoint to an RDF knowledge graph containing
linked data about the building and its energy systems (https://graphdb.nestcloud.ch/).
Our primary motivation is to improve the energy performance of the building, which includes
both simulation and control of the energy systems. We have diverse static datasets containing
information about the building and its energy systems. The datasets include BIM models,
sensor metadata, engineering schematics and 3D city models. There are also real-time data
streams and historical data from sensors and actuators installed in the building. The BMS
records and stores approximately 10,000 measurements every minute. Semantic web
technologies offer a promising solution to improve the discoverability and interoperability of
this data. They also provide a flexible and extensible framework for representing and querying
complex data. Despite these benefits, RDF knowledge graphs are challenging to create and
maintain. A lack of strictness introduces scope for errors and inconsistencies in the data;
engineers have raised concerns that models will differ between creators, even if they follow
the same ontologies. Standards such as SHACL help achieve consistency at the expense of
flexibility. We have also found that converting all data into RDF is impractical. There is also
an abundance of ontologies to represent building and energy systems concepts, which contain
many overlapping classes and properties, e.g. BOT, BRICK, ifcOWL, SSN/SOSA, SAREF,
RealEstateCore. Despite this, there are limited examples of knowledge graph instances of the
same building according to different ontologies, which makes it difficult to compare data
modelling approaches.
We have recognised a need for a traceable and repeatable framework for developing knowledge
graphs for building energy applications. Such a framework will enable different approaches to
be replicated and compared. We propose an approach to maintain the connection between the
knowledge graph and the raw input datasets. We achieve this through version-controlled
repositories for processing scripts and data, e.g. GitHub and Zenodo. The connection between
the raw input data and knowledge graph should be maintained so that if one is updated, the
other is synchronised. We aim to provide a framework for the continual evolution of pipelines
to generate knowledge graphs for buildings and their energy systems. We will present our
initial pipeline and the resulting knowledge graph. We will give an overview of the challenges
faced and an outline for future research.
Keywords 1
Data Integration, Knowledge graphs, Use case
Proceedings LDAC2023 – 11th Linked Data in Architecture and Construction, June 15–16, 2023, Matera, Italy
EMAIL: james.allan@empa.ch (J. Allan); jonggwan.an@empa.ch (J.G. An); reto.fricker@empa.ch (R. Frickerr); sascha.stoller@empa.ch (S.
Stoller); philipp.heer@empa.ch (P. Heer)
ORCID: 0000-0001-8763-5831 (J. Allan); 0000-0002-6027-1027 (J.G. An); 0000-0003-2999-5753 (P. Heer)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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