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International Journal of Photoenergy
Volume 2014 (2014), Article ID 748142, 8 pages
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.


In this research an improved approach for sizing standalone PV system (SAPV) is presented. This work is an improved work developed previously by the authors. The previous work is based on the analytical method which faced some concerns regarding the difficulty of finding the model’s coefficients. Therefore, the proposed approach in this research is based on a combination of an analytical method and a machine learning approach for a generalized artificial neural network (GRNN). The GRNN assists to predict the optimal size of a PV system using the geographical coordinates of the targeted site instead of using mathematical formulas. Employing the GRNN facilitates the use of a previously developed method by the authors and avoids some of its drawbacks. The approach has been tested using data from five Malaysian sites. According to the results, the proposed method can be efficiently used for SAPV sizing whereas the proposed GRNN based model predicts the sizing curves of the PV system accurately with a prediction error of 0.6%. Moreover, hourly meteorological and load demand data are used in this research in order to consider the uncertainty of the solar energy and the load demand.