An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network
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. Th...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2014/748142 |
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| author | Tamer Khatib Wilfried Elmenreich |
| author_facet | Tamer Khatib Wilfried Elmenreich |
| author_sort | Tamer Khatib |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8bed5f0df04442adaf3d6778c009a53e |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-8bed5f0df04442adaf3d6778c009a53e2025-08-20T03:37:40ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2014-01-01201410.1155/2014/748142748142An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural NetworkTamer Khatib0Wilfried Elmenreich1Institute of Networked and Embedded Systems and Lakeside Labs, University of Klagenfurt, 9020 Klagenfurt, AustriaInstitute of Networked and Embedded Systems and Lakeside Labs, University of Klagenfurt, 9020 Klagenfurt, AustriaIn 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.http://dx.doi.org/10.1155/2014/748142 |
| spellingShingle | Tamer Khatib Wilfried Elmenreich An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network International Journal of Photoenergy |
| title | An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network |
| title_full | An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network |
| title_fullStr | An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network |
| title_full_unstemmed | An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network |
| title_short | An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network |
| title_sort | improved method for sizing standalone photovoltaic systems using generalized regression neural network |
| url | http://dx.doi.org/10.1155/2014/748142 |
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