Please use this identifier to cite or link to this item:
Title: Artificial neural networks to predict daylight illuminance in office buildings
Authors: Kazanasmaz, Zehra Tuğçe
Günaydın, Hüsnü Murat
Binol, Selcen
Keywords: Artificial neural networks
Building parameters
Issue Date: Aug-2009
Publisher: Elsevier Ltd.
Source: Kazanasmaz, 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.2008.11.012
Abstract: A 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.
ISSN: 0360-1323
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
Show full item record

CORE Recommender


checked on May 27, 2023


checked on May 27, 2023

Page view(s)

checked on May 22, 2023


checked on May 22, 2023

Google ScholarTM



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