JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS, vol.1, no.2, pp.87-92, 2018 (Peer-Reviewed Journal)
Electricity generation from renewable energy
sources is increased day by day. Accurate estimation of
electricity generation from the renewable energy sources
which have intermittent and variable characteristics is a
requirement to ensure stable operation of the electrical grid.
In this study, a multi-layer artificial neural network (ANN)
system, which is supported by meteorological forecasting
data, has been proposed to predict day ahead hourly solar
radiation. In this context, the ANN system which operates by
based on cause-effect relationship has been designed. In order
to increase accuracy of the solar radiation prediction of the
designed ANN, a similar day selection algorithm has been
developed. A unique ANN has been constituted for each
season by evaluating the seasons within itself. The designed
ANN model has been designed, trained and tested in
MATLAB simulation environment without using codes of the
MATLAB ANN toolbox. Day ahead hourly solar radiation of
Trabzon province has been predicted by the proposed ANN.
The accuracy of the predictions has been evaluated by the
mean absolute percentage error (MAPE), the root means
squared error (RMSE), the mean absolute error (MAE) and
the correlation coefficient (r) performance measures