Clear sky color modeling based on BP neural network

Abstract

Daylight is a valuable resource that provides energy and luminance, as it contains changing spectrum distributions, which also carry color information and have an important impact on human vision and perception. Several sky models have been proposed for use in daylighting simulation calculations to quantify the effect of daylight. However, these models do not fully consider the color, or are modeled using the correlation of the color temperature and luminance fitting, which has color deviation, so it is difficult to realize the chromaticity-related research of daylight. Therefore, the clear sky spectral data in the Beijing area was measured and used to calculate the color coordinates of sky patches. Corresponding it with the sun position, sky patch position, and radiation data to model the sky color distribution based on backpropagation neural network method. Four-, five-, and six-variable models were established, where the five-variable model exhibited the smallest error: NMBEx = 0.52%, CVRMSEx = 3.54%, NMBEy = 0.67%, and CVRMSEy = 3.55%. However, the four- and six-variable errors were also within the specification and were easier to use. The proposed models can achieve the real-time prediction of the sky color distribution, provide a theoretical basis for daylighting design in future buildings, and guide the development of new spectrally-selective material applications and intelligent lighting control systems.