Description

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.

Citation: ASHRAE/IBPSA-USA Bldg Simulation Conf, Sept 2018

Product Details

Published:

2018

Number of Pages:

8

Units of Measure:

Dual

File Size:

1 file , 3.3 MB

Product Code(s):

D-BSC18-C018