Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones

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...

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Bibliographic Details
Published in:Applied sciences 2022-03, Vol.12 (5), p.2372
Main Authors: García-Rodríguez, Ana, García-Rodríguez, Sol, Granados-López, Diego, Díez-Mediavilla, Montserrat, Alonso-Tristán, Cristina
Format: Article
Language:English
Subjects:
Aerosols
ANN
Artificial intelligence
Climate change
Climatic zones
Cloud cover
Ground stations
Humidity
kt sky classification
multilinear regression models
Neural networks
Photosynthetically active radiation
Radiation
Regression analysis
Root-mean-square errors
Satellites
Variables
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12052372