RT info:eu-repo/semantics/article
T1 Benchmarking of meteorological indices for sky cloudiness classification
A1 Suárez García, Andrés
A1 Diez Mediavilla, Montserrat
A1 Granados López, Diego
A1 González Peña, David
A1 Alonso Tristán, Cristina
K1 Clearness index
K1 Sky classification
K1 CIE standard
K1 Cloudiness classification
K1 Meteorological indices
K1 Electrotecnia
K1 Electrical engineering
AB Sky classification is a complex problem, due in part to such abstract conceptual definitions as clear, intermediate, and overcast, as well as other intermediate ranges. The CIE (Commission Internationale de L’Éclairage) Standard classification offers a solution to this problem, although its application requires data on the luminance distribution of the whole sky that are less commonly available. A benchmarking and classification system of ten meteorological indices is introduced in this study to classify the sky types from overcast to clear. The indices can be calculated from measurements of global, diffuse, and direct irradiance that are widely available from meteorological ground stations. The classification system uses confusion matrices, a machine-learning tool that generates a visual display of the results of supervised-learning algorithms. The CIE Standard skies classification, applied to half hourly sky-scanner measurements in Burgos (Spain), over the period June 2016 - May 2017, is used in this study as a baseline reference for a comparative review of the results from the meteorological indices and their results. They are classified by four performance ratings: Accuracy, Jaccard, Cohen, and Matthews, which feature both classification similarity and the randomness of any agreement. All meteorological indices yielded a high average degree of accuracy - close to 80% - in a detailed review of their classification. Neverthless, the results suggested that Perez’s Clearness Index based on global, diffuse and direct radiation measurements offered the most precise classification of the skies, followed closely by the Klucher Clearness Index and the Perraudeau Nebulosity Index.
PB Elsevier
SN 0038-092X
YR 2020
FD 2020-01
LK http://hdl.handle.net/10259/5484
UL http://hdl.handle.net/10259/5484
LA eng
NO Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and the Spanish Ministry of Science, Innovation & Universities under the I + D + i state programme “Challenges Research Projects” (Ref. RTI2018-098900-B-I00)
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024