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Predicting daylight illuminance in urban city using multiple regression techniques
Published in Taylor’s University
Volume: 13.0
Issue: 7.0
Pages: 2181.0 - 2194.0
Prediction of daylight illuminance has become the prime concern for building designers. This paper presents the daylight illuminance frequency distribution, correlation coefficients amongst climate variables and linear regression models developed for moderate climate zone. The emphasis of the present study is to predict daylight illuminance intensities and to investigate its relationship amongst the important climatic variables such as temperature, percentage sky clearance and percentage relative humidity at specific timings of the day, observed during different climatic conditions and seasons. The daylight illuminance data is collected with the help of digital ‘Lux’ meter across the Pune city (India). The daylight illuminance frequency distribution, correlation coefficients and the linear regression models derived for four climate specific months are presented and explained. The minimum illuminance level of 6260 Lux is observed at 8.00 am whereas the maximum is 147300 Lux at 12.00 noon in the month of July and the highest frequency of illuminance intensities falls in the range 140000-145000 Lux. A better association (positive) of illuminance intensities has been observed with percentage sky clearance variable; the correlation coefficients in the month of July are 0.678, 0.656 and 0.453. The percentage error between predicted and measured values of daylight illuminance levels derived from the developed regression models varies from 4.47% to 12.33%.
About the journal
JournalJournal of Engineering Science and Technology
PublisherTaylor’s University
Open AccessNo