Research Abstract:
A
novel prediction model for the output current of PV module is proposed
in this paper. The proposed model is based on cascade-forward back
propagation artificial neural network with two inputs and one output.
Solar radiation and ambient temperature are the inputs and the predicted
current is the output. Experiment data for a 1.4 kWp PV systems
installed in Sohar city, Oman are utilized in developing the proposed
model. These data has an interval of 2 seconds in order to consider the
uncertainty of the system's output current. In order to evaluate the
accuracy of the neural network, three statistical values are used namely
mean absolute percentage error (MAPE), mean bias error (MBE) and root
mean square error (RMSE). Moreover, the ability of the proposed model to
predict performance with high uncertainty rate is validated. The
results show that the MAPE, MBE and RMSE of the proposed model are
7.08%, −4.98% and 7.8%, respectively