Multivariate linear regression model for estimating average daylight illuminance

• Window design and the selection of glazing system have significant effect on daylight illuminance. Occupants’ productivity is highly dependent on daylight, as it associates with numerous health advantages. Hence conducting a systematic investigation considering the performance of various window designs and glazing systems is highly important at the early stage of design process. For this purpose, this study attempts to develop a multivariate linear regression model for estimating the average daylight illuminance. To perform the simulations, an office room prototype was modelled by COMFEN 5Beta software. The prototype is a hypothetical office room, as its size, HVAC system and envelopes construction are based on the common practice in construction in Sweden. Because average daylight illuminance is sensitive to window size, orientation, glazing system, design model and position, 544 simulations were performed based on thses variable to create an extensive dataset.  A multivariate linear regression model was developed based on 90% dataset, which was chosen randomly. The obtained R² value was exceeded 96%, which shows an excellent fit for the developed model. The interaction between variables was also studied. The remaining 10% of dataset was utilized for validating the developed model. The validity of the model was further compared with another multivariate linear regression model, developed based on 50% of the dataset.The results show the effectiveness of multivariate linear regression models in supporting architects and predicting average daylight illuminance in early stage of design analysis.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Arkitekturteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Architectural Engineering (hsv//eng)

Nyckelord

Multivariate linear regression

Daylight Illuminance

interaction analysis

Byggteknik

Civil engineering

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