A Novel Second Order Radial Basis Function Neural Network Technique for Enhanced Load Forecasting of Photovoltaic Power Systems

In this study, a novel improved second order Radial Basis Function
Neural Network based method with excellent scheduling
capabilities is used for the dynamic prediction of short and longterm energy required applications. The effectiveness and the
reliability of the algorithm are evaluated using training operations
with New England-ISO database. The dynamic prediction
algorithm is implemented in Matlab and the computation of mean
absolute error and mean absolute percent error, and training time
for the forecasted load, are determined. The results show the
impact of temperature and other input parameters on the accuracy
of solar Photovoltaic load forecasting. The mean absolute percent
error is found to be between 1% to 3% and the training time is
evaluated from 3s to 10s. The results are also compared with the
previous studies, which show that this new method predicts short
and long-term load better than sigmoidal neural network and
bagged regression trees. The forecasted energy is found to be the
nearest to the correct values as given by England ISO database,
which shows that the method can be used reliably for short and
long-term load forecasting of any electrical system.