Toward AI-assisted, human-centered daylighting operation: Non-invasive daylighting preference evaluation using deep learning

Abstract

Lu et al. [1] proved that High Dynamic Range Imaging (HDRI) camera sensors from different viewpoints can capture consistent and transferable luminance patterns in daylit spaces through Conditional Generative Adversarial Networks (CGANs). Building on that, this paper validates that non-intrusive luminance monitoring can be used to evaluate daylighting preferences, using collected experimental datasets with human subjects at different seating locations in a real open-plan office. To apply paired comparisons for effective learning, subjects compared successive pairs of different visual conditions and indicated their visual preferences through online surveys. Meanwhile, ten small, low-cost, and calibrated cameras captured luminance maps from both the field of view (FOV) of each occupant and non-intrusive viewpoints (on computer monitors, luminaire/ceiling and desk) under various sky conditions and interior luminance distributions. Convolutional Neural Network (CNN) models were developed and trained on luminance similarity index maps (generated from pixel-wise comparisons between successive luminance maps captured from FOV and non-intrusive cameras separately), to classify each subject’s daylight visual preferences. The results showed that the models trained on luminance distributions measured by monitor-mounted and ceiling-mounted cameras produced preference predictions consistent with those derived from FOV cameras, and can reliably learn visual preferences (83–94 % accuracy) in all cases except for locations furthest from the windows. Overall, this study is the first to demonstrate that daylight preferences can be learned non-invasively by employing the full potential of HDRI and deep learning techniques, marking a significant milestone toward practical, AI-assisted, human-centered daylighting operation.