Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León Region, Spain

Diez, F.J., Navas-Gracia, L.M., Chico-Santamarta, L., Correa-Guimaraes, A. and Martínez-Rodríguez, A, (2020) Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León Region, Spain. Agronomy, 10 (1). p. 96.

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Abstract

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.

Item Type: Article
Keywords: horizontal daily global solar irradiation, evapotranspiration, agrometeorology, artificial neuronal networks, computing intelligence, prediction
Divisions: Crop and Environment Sciences
Depositing User: Ms Kath Osborn
Date Deposited: 20 Feb 2020 09:59
Last Modified: 20 Feb 2020 09:59
URI: http://hau.repository.guildhe.ac.uk/id/eprint/17520

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