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

Arwa Ben Farhat, Shyam Singh Chandel, Wai Lok Woo, Cherif Adnene

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)77-87
Number of pages11
JournalInternational Journal of Computer Science and Network Security
Volume21
Issue number2
DOIs
Publication statusPublished - 28 Feb 2021

Keywords

  • Artificial Neural Network (ANN)
  • Radial Basis Function Neural Network (RBFNN)
  • Load forecasting
  • Electrical systems
  • Photovoltaic systems

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