Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2497
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKazanasmaz, Zehra Tuğçe-
dc.contributor.authorGünaydın, Hüsnü Murat-
dc.contributor.authorBinol, Selcen-
dc.date.accessioned2016-11-22T12:42:11Z
dc.date.available2016-11-22T12:42:11Z
dc.date.issued2009-08
dc.identifier.citationKazanasmaz, T., Günaydın, M., and Binol, S. (2009). Artificial neural networks to predict daylight illuminance in office buildings. Building and Environment, 44(8), 1751-1757. doi:10.1016/j.buildenv.2008.11.012en_US
dc.identifier.issn0360-1323
dc.identifier.issn0360-1323-
dc.identifier.urihttp://dx.doi.org/10.1016/j.buildenv.2008.11.012
dc.identifier.urihttp://hdl.handle.net/11147/2497
dc.description.abstractA prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofBuilding and Environmenten_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectBuildingen_US
dc.subjectDaylightingen_US
dc.subjectModelingen_US
dc.subjectBuilding parametersen_US
dc.titleArtificial neural networks to predict daylight illuminance in office buildingsen_US
dc.typeArticleen_US
dc.authoridTR28229en_US
dc.authoridTR7988en_US
dc.institutionauthorKazanasmaz, Zehra Tuğçe-
dc.institutionauthorGünaydın, Hüsnü Murat-
dc.institutionauthorBinol, Selcen-
dc.departmentIzmir Institute of Technology. Architectureen_US
dc.identifier.volume44en_US
dc.identifier.issue8en_US
dc.identifier.startpage1751en_US
dc.identifier.endpage1757en_US
dc.identifier.wosWOS:000265171300023en_US
dc.identifier.scopus2-s2.0-61849143861en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.buildenv.2008.11.012-
dc.relation.doi10.1016/j.buildenv.2008.11.012en_US
dc.coverage.doi10.1016/j.buildenv.2008.11.012en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.cerifentitytypePublications-
crisitem.author.dept02.02. Department of Architecture-
Appears in Collections:Architecture / Mimarlık
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
2497.pdfMakale595.87 kBAdobe PDFThumbnail
View/Open
Show simple item record

CORE Recommender

SCOPUSTM   
Citations

59
checked on Jan 15, 2022

WEB OF SCIENCETM
Citations

59
checked on Jan 15, 2022

Page view(s)

48
checked on Jan 17, 2022

Download(s)

98
checked on Jan 17, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.