An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network
Joint Authors
Elmenreich, Wilfried
Khatib, Tamer
Source
International Journal of Photoenergy
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-11
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Khatib, Tamer& Elmenreich, Wilfried. 2014. An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network. International Journal of Photoenergy،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1037307
Modern Language Association (MLA)
Khatib, Tamer& Elmenreich, Wilfried. An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network. International Journal of Photoenergy No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1037307
American Medical Association (AMA)
Khatib, Tamer& Elmenreich, Wilfried. An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network. International Journal of Photoenergy. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1037307
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1037307