2026-04-17T11:01:02Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72372024-05-14T10:09:05Zcom_10259_6229com_10259_4534com_10259.4_106com_10259_2604com_10259_6231com_10259_4266com_10259_4393com_10259_5086col_10259_6230col_10259_6232col_10259_4394

00925njm 22002777a 4500 dc García Rodríguez, Ana author García Rodríguez, Sol author Granados López, Diego author Diez Mediavilla, Montserrat author Alonso Tristán, Cristina author 2022-03 Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type. 2076-3417 http://hdl.handle.net/10259/7237 10.3390/app12052372 2076-3417 Photosynthetically active radiation kt sky classification ANN Multilinear regression models Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones