Search

Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance ( G H U V E ) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson's correlation coefficient. Multilinear Regression Model (M L R) and artificial neural network (A N N) techniques were employed to model G H U V E under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the C I E standard on a 10 min basis. A N N models of G H U V E outperform those based on MLR according to the traditional statistical indices used in this study ( R 2 , M B E , and n R M S E ). Moreover, the work proposes a simple all-sky A N N model of G H U V E based on usually recorded variables at ground meteorological stations. [ABSTRACT FROM AUTHOR]

Natural illumination has an important place in home automation applications. Among other advantages, it contributes to better visual health, energy savings, and lower CO 2 emissions. Therefore, it is important to measure illuminance in the most accurate and cost-effective way. This work compares several low-cost commercial sensors (VEML 7700, TSL2591, and OPT3001) with a professional one (ML-020S-O), all of them installed outdoors. In addition, a platform based on the Internet of Things technology was designed and deployed as a centralized point of data collection and processing. Summer months have been chosen for the comparison. This is the most adverse situation for low-cost sensors since they are designed for indoor use, and their operating range is lower than the maximum reached by sunlight. The solar illuminance was recorded every minute. As expected, the obtained bias depends on the solar height. This can reach 60% in the worst circumstances, although most of the time, its value stays below 40%. The positive side lies in the good precision of the recordings. This systematic deviation makes it susceptible to mathematical correction. Therefore, the incorporation of more sensors and data that can help the global improvement of the precision and accuracy of this low-cost system is left as a future line of improvement.