Modeling of the output current of a photovoltaic grid-connected system using random forests technique

Ibrahim A. Ibrahim, Tamer Khatib*, Azah Mohamed, Wilfried Elmenreich

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Citations (Scopus)
51 Downloads (Pure)

Abstract

This study presents a prediction technique for the output current of a photovoltaic grid-connected system by using random forests technique. Experimental data of a photovoltaic grid-connected system are used to train and validate the proposed model. Three statistical error values, namely root mean square error, mean bias error, and mean absolute percentage error, are used to evaluate the developed model. Moreover, the results of the proposed technique are compared with results obtained from an artificial neural network-based model to show the superiority of the proposed method. Results show that the proposed model accurately predicts the output current of the system. The root mean square error, mean absolute percentage error, and mean bias error values of the proposed method are 2.7482, 8.7151, and −2.5772%, respectively. Moreover, the proposed model is faster than the artificial neural network-based model by 0.0801 s.

Original languageEnglish
Pages (from-to)132-148
Number of pages17
JournalEnergy Exploration and Exploitation
Volume36
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • artificial neural network
  • modeling of photovoltaic systems
  • performance evaluation
  • random forests

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