Assessing neural network approaches for solar radiation estimates using limited climatic data in the mediterranean sea

Abstract

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 in stallation 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 were 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 were analyzed using different statistical indices (Root Mean Square Error—RMSE, Mean Bias Error—MBE, and Nash-Sutcliffe model efficiency coefficient—NSE).