Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/52811
Título
Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León region, Spain
Autor
Año del Documento
2020
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Agronomy, 2020, vol. 10, n. 1, 96
Zusammenfassung
This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.
Palabras Clave
Solar irradiation
Irradiación solar
Evapotranspiration
Evapotranspiración
Agrometeorology
Agrometeorología
Artificial neuronal networks
Redes neuronales artificiales
ISSN
2073-4395
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
© 2020 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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