Development of a neural network model to predict daily solar radiation

Abstract

Many computer simulation models which predict growth, development, and yield of agronomic and horticultural crops require daily weather data as input. One of these inputs is daily total solar radiation, which in many cases is not available owing to the high cost and complexity of the instrumentation needed to record it. The aim of this study was to develop a neural network model which can predict solar radiation as a function of readily available weather data and other environmental variables. Four sites in the southeastern USA, i.e. Tifton, GA, Clayton, NC, Gainesville, FL, and Quincy, FL, were selected because of the existence of longterm daily weather data sets which included solar radiation. A combined total of 23 complete years of weather data sets were available, and these data sets were separated into 11 years for the training data set and 12 years for the testing data set. Daily observed values of minimum and maximum air temperature and precipitation, together with daily calculated values for daylength and clear sky radiation, were used as inputs for the neural network model. Daylength and clear sky radiation were calculated as a function of latitude, day of year, solar angle, and solar constant. An optimum momentum, learning rate, and number of hidden nodes were determined for further use in the development of the neural network model. After model development, the neural network model was tested against the independent data set. Root mean square error varied from 2.92 to 3.64 MJ m −2 and the coefficient of determination varied from 0.52 to 0.74 for the individual years used to test the accuracy of the model. Although this neural network model was developed and tested for a limited number of sites, the results suggest that it can be used to estimate daily solar radiation when measurements of only daily maximum and minimum air temperature and precipitation are available.