  Annual luminance maps provide meaningful evaluations for occupants' visual
comfort, preferences, and perception. However, acquiring long-term luminance
maps require labor-intensive and time-consuming simulations or impracticable
long-term field measurements. This paper presents a novel data-driven machine
learning approach that makes annual luminance-based evaluations more efficient
and accessible. The methodology is based on predicting the annual luminance
maps from a limited number of point-in-time high dynamic range imagery by
utilizing a deep neural network (DNN). Panoramic views are utilized, as they
can be post-processed to study multiple view directions. The proposed DNN model
can faithfully predict high-quality annual panoramic luminance maps from one of
the three options within 30 minutes training time: a) point-in-time luminance
imagery spanning 5% of the year, when evenly distributed during daylight hours,
b) one-month hourly imagery generated or collected continuously during daylight
hours around the equinoxes (8% of the year); or c) 9 days of hourly data
collected around the spring equinox, summer and winter solstices (2.5% of the
year) all suffice to predict the luminance maps for the rest of the year. The
DNN predicted high-quality panoramas are validated against Radiance (RPICT)
renderings using a series of quantitative and qualitative metrics. The most
efficient predictions are achieved with 9 days of hourly data collected around
the spring equinox, summer and winter solstices. The results clearly show that
practitioners and researchers can efficiently incorporate long-term
luminance-based metrics over multiple view directions into the design and
research processes using the proposed DNN workflow.
