One of the most crucial variables in Agricultural Meteorology is Solar Radiation (Rs), although it is measured in a very limited number of weather stations due to its high cost in both installation and maintenance. Moreover, the quality of the data is usually low because of sensor failure and/or lack of calibration, which made scientists search for new approaches such as neural network models. Thus, the improvement of traditional solar radiation estimation models with minimum data availability is still needed for different purposes. In this work, several neural network models have been developed and assessed (Multilayer perceptron -MLP-, Support Vector Machines -SVM-, Extreme Learning Machine, Convolutional Neural Networks -CNN- and Long Short-Term Memory -LSTM-) with different temperature-based input variables configurations in Southern Spain (weather station located in the Mediterranean Sea coast). The performances have been analyzed using different statistical indices (Root Mean Square Error -RMSE-, Mean Bias Error -MBE-, correlation coefficient -R2- and Nash-Sutcliffe model efficiency coefficient -NSE-).
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Assessing Neural Network Approaches for Solar Radiation Estimates Using Limited Climatic Data in the Mediterranean Sea
Published: 13 November 2020 by MDPI in The 3rd International Electronic Conference on Atmospheric Sciences session Meteorology
Keywords: neural networks; solar radiation; bayesian optimization; convolutional neural network; Long Short-Term Memory