CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

Abstract

CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented in Ruiz-Arias and Gueymard (2023, doi: 10.1016/j.solener.2023.111895). This dataset was used to develop, validate and benchmark CAELUS. It is made up by 1-min quality-assured observations of global horizontal irradiance (GHI) and diffuse horizontal irradiance at 54 stations of the Baseline Surface Radiation Network (BSRN) archive, which is publicly available (see download instructions in https://bsrn.awi.de/data). The dataset includes 5 years of data per station, except in two of them (Petrolina, Brazil, and Solar Village, Saudi Arabia), combined with other variables that are required to run CAELUS, namely: solar zenith angle (sza), extraterrestrial horizontal solar irradiance (eth), clear-sky GHI (ghics) and GHI in a clean and dry atmosphere (ghicda). In addition, the dataset also provides the sky classification obtained with CAELUS. Further details about CAELUS and the dataset compilation is available in Ruiz-Arias and Gueymard (2023, doi: 10.1016/j.solener.2023.111895). A Python implementation of CAELUS is available in https://github.com/jararias/caelus.