Artificial Neural Network Model for Prediction Solar Radiation Data:
Application for Sizing Stand-alone Photovoltaic Power System
Proc. of IEEE, (2005)Abstract
The prediction of daily global solar radiation data is very important
for many solar applications, possible application can be found in
meteorology, renewable energy and solar conversion energy. In this
paper, we investigate using Radial Basis Function (RBF) networks
in order to find a model for daily global solar radiation data from
sunshine duration and air temperature. This methodology is considered
suitable for prediction time series. Using the database of daily
sunshine duration, air temperature and global solar radiation data
corresponding to Typical Reference Year (TRY). A RBF model has been
trained based on 300 known data from TRY, in this way, the network
was trained to accept and even handle a number of unusual cases.
Known data were subsequently used to investigate the accuracy of
prediction. Subsequently, the unknown validation data set produced
very accurate estimation, with the mean relative error (MRE) not
exceed 1.5% between the actual and predicted data, also the correlation
coefficient obtained for the validation data set is 98.9%, these
results indicates that the proposed model can successfully be used
for prediction and modeling of daily global solar radiation data
from sunshine duration and air temperature. An application for sizing
of stand-alone PV system has been presented in this paper in order
to shows the importance of this modeling.