Compared with illuminance-based metrics, luminance-based metrics and evaluations provide better understandings of occupant visual experience. However, it is computationally expensive and time consuming to incorporate luminance-based metrics into architectural design practice because annual simulations require generating a luminance map at each time step of the entire year. This paper describes the development of a novel prediction model to generate annual luminance maps of indoor space from a subset of images by using deep neural networks (DNNs). The results show that by only rendering 5% of annual luminance maps, the proposed DNNs model can predict the rest with comparable accuracy that closely matches those high-quality point-in-time renderings generated by Radiance (RPICT) software. This model can be applied to accelerate annual luminance-based simulations and lays the groundwork for generating annual luminance maps utilizing High Dynamic Range (HDR) captures of existing environments.
| File Size : | 1
file
, 3.3 MB |
| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 8 |
| Product Code(s) : | D-BSC18-C018 |
| Published : | 2018 |
| Units of Measure : | Dual |
