| Title |
Stochastic Daylighting Model for Predictive Control in Large Open-space Building |
| Authors |
조형곤(Jo, Hyeong-Gon) ; 김영섭(Kim, Young-Sub) ; 박철수(Park, Cheol-Soo) |
| DOI |
https://doi.org/10.5659/JAIK.2021.37.12.255 |
| Keywords |
Model Uncertainty; daylight prediction model; Electric Lighting Control; Gaussian Process |
| Abstract |
Development of stochastic daylighting prediction model for a large open-space building was presented in this paper. The daylit prediction
model uses solar altitude and azimuth, an illuminance value at a reference point and a cloud cover and predict daylit illuminances at sixteen
workplane. The model can be regarded as ‘virtual sensor’ without installing actual photosensor. For capturing stochastic characteristics of
daylit luminous indoor environment, Gaussian process was used. The daylit prediction model was then integrated to electric lighting control of
the building. The optimal lighting control variables that can minimize electric lighting power consumption while satisfying required
illuminance level expressed as a safety margin were found. Based on the eight days’ validation, it is found that the proposed approach could
save energy by 12.3%. It is expected that this stochastic control approach could be applied to other lighting control or indoor environmental
control system. |