Recommended control options of a daylight dimming system for daylit perimeter zones of open-planned office with curtain walls

Joohyang Byun,Younju Yoon,Sooyoung Kim

ENERGY REPORTS(2024)

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摘要
This study investigates control options of a daylight dimming system in an open-planned office for required lighting environments with lighting energy savings and acceptable glare ratings. Computer simulations were performed for photosensors with three ranges of viewing angles in two daylit perimeter zones of the office and consecutive computational processes based on dimming control algorithm were employed for final light output of lighting fixtures. Three CIE standard sky and blind conditions were employed for the prediction of indoor daylight illuminance, photosensor illuminance, and daylight glare probability (DGP). The results indicate that the perceived glare can be imperceptible for the majority of time periods and a daylight dimming control system can be effectively applied for perimeter zones of office with curtain walls. For the south-facing zone under clear and intermediate sky conditions, the horizontal and 45 degree blind conditions are recommended to be combined with the full or partial photosensor shielding conditions for desirable dimming control performances. The unshielded photosensor condition should be generally avoided. For the east-facing zone under clear and intermediate sky, the photosensor sensing range should be narrowed using full shielding for the improvement of system performance of daylight dimming control. The unshielded and partially-shielded photosensors are rarely recommended. For the east and south-facing zone under overcast sky, effective control performance was rarely achieved due to the lack of daylight. Predictions models for linear relationships between the workplane and photosensor illuminances due to daylight were statistically acceptable, and strong relationships existed between them.
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关键词
Control options,Daylight dimming,Lighting energy,Perimeter zone,Sky,Daylight glare probability,Prediction model
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