TY - JOUR
T1 - Semantic digital twin creation of building systems through time series based metadata inference – A review
AU - Benfer, Rebekka
AU - Müller, Jochen
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/10/15
Y1 - 2024/10/15
N2 - Numerous applications are being developed to enhance the energy efficiency of building systems, including fault detection and diagnosis, performance assessment, and intelligent control. For these applications to be effectively utilised, a data connection between the real and virtual worlds must be established. One potential solution to establish this connection and enable semantic enrichment of data with metadata is the semantic digital twin. Semantic digital twins use semantic technologies, such as ontologies, as metadata schemas. However, creating these twins requires substantial manual effort due to the need to examine diverse sources of information about the building systems and normalise this information into a metadata schema. This review investigates whether metadata inference based on time series data from building systems can assist in the automated creation of semantic digital twins. To this end, 53 artificial intelligence-based publications on metadata inference are analyzed for their applicability and efficiency. Three key tasks of metadata inference are examined to create a semantic digital twin: type classification, relation inference, and extraction of operational information. Based on these findings, future research directions are proposed.
AB - Numerous applications are being developed to enhance the energy efficiency of building systems, including fault detection and diagnosis, performance assessment, and intelligent control. For these applications to be effectively utilised, a data connection between the real and virtual worlds must be established. One potential solution to establish this connection and enable semantic enrichment of data with metadata is the semantic digital twin. Semantic digital twins use semantic technologies, such as ontologies, as metadata schemas. However, creating these twins requires substantial manual effort due to the need to examine diverse sources of information about the building systems and normalise this information into a metadata schema. This review investigates whether metadata inference based on time series data from building systems can assist in the automated creation of semantic digital twins. To this end, 53 artificial intelligence-based publications on metadata inference are analyzed for their applicability and efficiency. Three key tasks of metadata inference are examined to create a semantic digital twin: type classification, relation inference, and extraction of operational information. Based on these findings, future research directions are proposed.
KW - Artificial intelligence
KW - Building operation
KW - Building systems
KW - Data analytics
KW - Digital twin
KW - Information extraction
KW - Metadata inference
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85201088732&partnerID=8YFLogxK
U2 - 10.1016/j.enbuild.2024.114637
DO - 10.1016/j.enbuild.2024.114637
M3 - Article
AN - SCOPUS:85201088732
SN - 0378-7788
VL - 321
JO - Energy and Buildings
JF - Energy and Buildings
M1 - 114637
ER -