Prediction of Maximum Solar Radiation Using Artificial Neural Networks
Date Issued
2002
Abstract
The prediction of solar radiation is very important for many solar applications. Due to the very nature of solar
radiation, many parameters can influence both its intensity and its availability and therefore it is difficult to
employ analytical methods for such predictions. For this reason, multivariate prediction techniques are more
suitable. In the present research, artificial neural networks are utilised due to their ability to be trained with
past data in order to provide the required predictions. The input data that are used in the present approach are
those which influence mostly the availability and intensity of solar radiation, namely, the month, day of
month, Julian day, season, mean ambient temperature and mean relative humidity (RH).
A multilayer recurrent architecture employing the standard back-propagation learning algorithm has been applied.
This methodology is considered suitable for time series predictions. Using the hourly records for one complete
year, the maximum value of radiation and the mean daily values of temperature and relative humidity (RH)
were calculated. The respective data for 11 months were used for the training and testing of the network,
whereas the data for the remaining one month were used for the validation of the network. The training of the
network was performed with adequate accuracy. Subsequently, the “unknown” validation data set produced
very accurate predictions, with a correlation coefficient between the actual and the ANN predicted data of
0.9867. Also, the sensitivity of the predictions to ±20% variation in temperature and RH give correlation
coefficients of 0.9858 to 0.9875, which are considered satisfactory. This is considered as an adequate accuracy
for such predictions.
radiation, many parameters can influence both its intensity and its availability and therefore it is difficult to
employ analytical methods for such predictions. For this reason, multivariate prediction techniques are more
suitable. In the present research, artificial neural networks are utilised due to their ability to be trained with
past data in order to provide the required predictions. The input data that are used in the present approach are
those which influence mostly the availability and intensity of solar radiation, namely, the month, day of
month, Julian day, season, mean ambient temperature and mean relative humidity (RH).
A multilayer recurrent architecture employing the standard back-propagation learning algorithm has been applied.
This methodology is considered suitable for time series predictions. Using the hourly records for one complete
year, the maximum value of radiation and the mean daily values of temperature and relative humidity (RH)
were calculated. The respective data for 11 months were used for the training and testing of the network,
whereas the data for the remaining one month were used for the validation of the network. The training of the
network was performed with adequate accuracy. Subsequently, the “unknown” validation data set produced
very accurate predictions, with a correlation coefficient between the actual and the ANN predicted data of
0.9867. Also, the sensitivity of the predictions to ±20% variation in temperature and RH give correlation
coefficients of 0.9858 to 0.9875, which are considered satisfactory. This is considered as an adequate accuracy
for such predictions.
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