Sensitivity of artificial neural network based model for photovoltaic system actual performance
A novel prediction model for the output current of PV module is proposed in this paper. The proposed model is based on cascade-forward back propagation artificial neural network with two inputs and one output. Solar radiation and ambient temperature are the inputs and the predicted current is the ou...
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833412524458901504 |
|---|---|
| author | Ameen A.M. Pasupuleti J. Khatib T. |
| author2 | 56602552200 |
| author_facet | 56602552200 Ameen A.M. Pasupuleti J. Khatib T. |
| author_sort | Ameen A.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A novel prediction model for the output current of PV module is proposed in this paper. The proposed model is based on cascade-forward back propagation artificial neural network with two inputs and one output. Solar radiation and ambient temperature are the inputs and the predicted current is the output. Experiment data for a 1.4 kWp PV systems installed in Sohar city, Oman are utilized in developing the proposed model. These data has an interval of 2 seconds in order to consider the uncertainty of the system's output current. In order to evaluate the accuracy of the neural network, three statistical values are used namely mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. The results show that the MAPE, MBE and RMSE of the proposed model are 7.08%, -4.98% and 7.8%, respectively © 2014 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-21908 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-219082023-05-16T10:45:59Z Sensitivity of artificial neural network based model for photovoltaic system actual performance Ameen A.M. Pasupuleti J. Khatib T. 56602552200 11340187300 31767521400 A novel prediction model for the output current of PV module is proposed in this paper. The proposed model is based on cascade-forward back propagation artificial neural network with two inputs and one output. Solar radiation and ambient temperature are the inputs and the predicted current is the output. Experiment data for a 1.4 kWp PV systems installed in Sohar city, Oman are utilized in developing the proposed model. These data has an interval of 2 seconds in order to consider the uncertainty of the system's output current. In order to evaluate the accuracy of the neural network, three statistical values are used namely mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. The results show that the MAPE, MBE and RMSE of the proposed model are 7.08%, -4.98% and 7.8%, respectively © 2014 IEEE. Final 2023-05-16T02:45:59Z 2023-05-16T02:45:59Z 2014 Conference Paper 10.1109/PECON.2014.7062449 2-s2.0-84946691724 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946691724&doi=10.1109%2fPECON.2014.7062449&partnerID=40&md5=06afc94a56e8cf06df1642e4165f0028 https://irepository.uniten.edu.my/handle/123456789/21908 7062449 241 244 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Ameen A.M. Pasupuleti J. Khatib T. Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title | Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title_full | Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title_fullStr | Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title_full_unstemmed | Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title_short | Sensitivity of artificial neural network based model for photovoltaic system actual performance |
| title_sort | sensitivity of artificial neural network based model for photovoltaic system actual performance |
| url_provider | http://dspace.uniten.edu.my/ |