Linear regression based indoor daylight illuminance estimation with simple measurements for daylight-linked lighting control

Abstract

Accurate prediction of indoor daylight illuminance is crucial for daylight-based lighting controls. However, determining the illuminance using physics-based simulation tools requires significant amounts of information, e.g. grid of sensors, sky model, 3D geometry of a target building and surroundings, etc. In this study, the authors suggest a daylight illuminance estimation method with minimal data of two reference sensors and two prior measurements. It is shown that the daylight coefficient and sky luminance distribution can be substituted by the illuminance of the reference points and illuminance of two or more target points at past times. The method was validated on a large open space with north-facing skylight windows and showed an 11.9% mean absolute percentage error. Additionally, a reference point selection method is presented. The proposed method is practical for daylight-based lighting control applications.