RT info:eu-repo/semantics/article
T1 Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
A1 García Rodríguez, Ana
A1 García Rodríguez, Sol
A1 Granados López, Diego
A1 Diez Mediavilla, Montserrat
A1 Alonso Tristán, Cristina
K1 Photosynthetically active radiation
K1 kt sky classification
K1 ANN
K1 Multilinear regression models
K1 Ingeniería eléctrica
K1 Electric engineering
AB 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.
PB MDPI
SN 2076-3417
YR 2022
FD 2022-03
LK http://hdl.handle.net/10259/7237
UL http://hdl.handle.net/10259/7237
LA eng
NO This research was funded by the Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00, and Consejería de Empleo e Industria, Junta de Castilla y León, grant number INVESTUN/19/BU-0004.
DS Repositorio Institucional de la Universidad de Burgos
RD 16-abr-2024