File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model

TitlePrediction of energy use intensity of urban buildings using the semi-supervised deep learning model
Authors
Jiang, FMa, JLI, ZDing, Y
Issue Date2022
Citation
Energy, 2022, v. 249, p. 123631 How to Cite?
DOI: http://dx.doi.org/10.1016/j.energy.2022.123631
Persistent Identifierhttp://hdl.handle.net/10722/311823

 

DC FieldValueLanguage
dc.contributor.authorJiang, F-
dc.contributor.authorMa, J-
dc.contributor.authorLI, Z-
dc.contributor.authorDing, Y-
dc.date.accessioned2022-04-01T09:13:39Z-
dc.date.available2022-04-01T09:13:39Z-
dc.date.issued2022-
dc.identifier.citationEnergy, 2022, v. 249, p. 123631-
dc.identifier.urihttp://hdl.handle.net/10722/311823-
dc.languageeng-
dc.relation.ispartofEnergy-
dc.titlePrediction of energy use intensity of urban buildings using the semi-supervised deep learning model-
dc.typeArticle-
dc.identifier.emailJiang, F: ffjiang@hku.hk-
dc.identifier.emailMa, J: junma@hku.hk-
dc.identifier.authorityMa, J=rp02719-
dc.identifier.doi10.1016/j.energy.2022.123631-
dc.identifier.hkuros332274-
dc.identifier.volume249-
dc.identifier.spage123631-
dc.identifier.epage123631-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats