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International Journal of Photoenergy
Volume 2014, Article ID 748142, 8 pages
http://dx.doi.org/10.1155/2014/748142
Research Article

An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network

Institute of Networked and Embedded Systems and Lakeside Labs, University of Klagenfurt, 9020 Klagenfurt, Austria

Received 14 May 2014; Revised 9 July 2014; Accepted 23 July 2014; Published 11 August 2014

Academic Editor: Dimitrios Karamanis

Copyright © 2014 Tamer Khatib and Wilfried Elmenreich. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Tamer Khatib, Ibrahim A. Ibrahim, and Azah Mohamed, “A review on sizing methodologies of photovoltaic array and storage battery in a standalone photovoltaic system,” Energy Conversion and Management, vol. 120, pp. 430–448, 2016. View at Publisher · View at Google Scholar
  • Ayman Youssef, Mohammed El-Telbany, and Abdelhalim Zekry, “The role of artificial intelligence in photo-voltaic systems design and control: A review,” Renewable and Sustainable Energy Reviews, vol. 78, pp. 72–79, 2017. View at Publisher · View at Google Scholar
  • Aladdin Alwisy, Beda Barkokebas, Samer Bu Hamdan, Mustafa Gül, and Mohamed Al-Hussein, “Energy-based Target Cost Modelling for Construction Projects,” Journal of Building Engineering, 2018. View at Publisher · View at Google Scholar
  • Nkateko E. Mabunda, and Meera K. Joseph, “Microcontroller-Based Strategies for the Incorporation of Solar to Domestic Electricity,” Energies, vol. 12, no. 14, pp. 2811, 2019. View at Publisher · View at Google Scholar
  • Dhiaa Halboot Muhsen, Moamen Nabil, Haider Tarish Haider, and Tamer Khatib, “A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods,” Energy, 2019. View at Publisher · View at Google Scholar