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Variational Autoencoders for Anomaly Detection and Transfer Knowledge in Electricity and District Heating Consumption
Shahid, Zahraa Khais
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
ORCID iD:
0000-0002-5704-4667
Saguna, Saguna
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
ORCID iD:
0000-0001-8561-7963
Åhlund, Christer
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
ORCID iD:
0000-0002-8681-9572
2024 (English)
In:
IEEE transactions on industry applications, ISSN 0093-9994, E-ISSN 1939-9367, Vol. 60, no 5, p. 7437-7450
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2024. Vol. 60, no 5, p. 7437-7450
Keywords [en]
Anomaly Detection, Anomaly detection, Autoencoder, Buildings, CNNLSTM, District heating, Electricity, electricity and district heating Daily Consumption, Energy consumption, Resistance heating, RNN-LSTM, School buildings, transfer knowledge, VAE, Water heating
National Category
Energy Engineering
Research subject
Pervasive Mobile Computing
Identifiers
URN:
urn:nbn:se:ltu:diva-108412
DOI:
10.1109/TIA.2024.3425805
ISI:
001319511900075
Scopus ID:
2-s2.0-85198352210
OAI: oai:DiVA.org:ltu-108412
DiVA, id:
diva2:1885817
Note
Validerad;2024;Nivå 2;2024-10-17 (joosat);
Available from:
2024-07-25
Created:
2024-07-25
Last updated:
2024-11-20
Bibliographically approved
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Shahid, Zahraa Khais
Saguna, Saguna
Åhlund, Christer
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Saguna, Saguna
Åhlund, Christer
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