Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules
This paper presents a study on neural network-based modeling techniques and sensor data to estimate the power output of photovoltaic systems under soiling conditions. Predicting maximum power output under soiling conditions is considered an important and difficult problem and a variety of models usi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/16621 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513439255232512 |
|---|---|
| author | Shapsough, Salsabeel Yousef |
| author2 | Dhaouadi, Rached Zualkernan, Imran |
| author2_role | author author |
| author_facet | Shapsough, Salsabeel Yousef Dhaouadi, Rached Zualkernan, Imran |
| author_role | author |
| dc.creator.none.fl_str_mv | Shapsough, Salsabeel Yousef Dhaouadi, Rached Zualkernan, Imran |
| dc.date.none.fl_str_mv | 2019 2020-02-23T08:42:31Z 2020-02-23T08:42:31Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Salsabeel Shapsough, Rached Dhaouadi, Imran Zualkernan, Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules, Procedia Computer Science, Volume 155, 2019, Pages 463-470, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.08.065. 1877-0509 http://hdl.handle.net/11073/16621 10.1016/j.procs.2019.08.065 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.relation.none.fl_str_mv | https://doi.org/10.1016/j.procs.2019.08.065 |
| dc.subject.none.fl_str_mv | PV Neural Networks Soiling |
| dc.title.none.fl_str_mv | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents a study on neural network-based modeling techniques and sensor data to estimate the power output of photovoltaic systems under soiling conditions. Predicting maximum power output under soiling conditions is considered an important and difficult problem and a variety of models using a host of factors including temperature and weather profiles have been proposed. This study used linear regression models and artificial neural networks and used only solar irradiation and ambient temperature, as well and the maximum power point (MPP) characteristic variables of photovoltaic (PV) modules obtained from online current-voltage (IV) tracers in the site of a PV installation. The two models were trained and validated using actual monitoring data of two 100-Watt PV modules installed in the UAE. One reference panel was cleaned on a weekly basis and the second panel was left to accumulate dust over the entire period between July 1, 2018 and 17 September, 2018. The results show that it is possible to predict maximum power output of soiled PV modules at about 97% accuracy. The proposed models perform at an accuracy comparable to more complex models in literature. |
| format | article |
| id | aus_5c4d0b1dd2acc80f8dd39095e07b23cc |
| identifier_str_mv | Salsabeel Shapsough, Rached Dhaouadi, Imran Zualkernan, Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules, Procedia Computer Science, Volume 155, 2019, Pages 463-470, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.08.065. 1877-0509 10.1016/j.procs.2019.08.065 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/16621 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV ModulesShapsough, Salsabeel YousefDhaouadi, RachedZualkernan, ImranPVNeural NetworksSoilingThis paper presents a study on neural network-based modeling techniques and sensor data to estimate the power output of photovoltaic systems under soiling conditions. Predicting maximum power output under soiling conditions is considered an important and difficult problem and a variety of models using a host of factors including temperature and weather profiles have been proposed. This study used linear regression models and artificial neural networks and used only solar irradiation and ambient temperature, as well and the maximum power point (MPP) characteristic variables of photovoltaic (PV) modules obtained from online current-voltage (IV) tracers in the site of a PV installation. The two models were trained and validated using actual monitoring data of two 100-Watt PV modules installed in the UAE. One reference panel was cleaned on a weekly basis and the second panel was left to accumulate dust over the entire period between July 1, 2018 and 17 September, 2018. The results show that it is possible to predict maximum power output of soiled PV modules at about 97% accuracy. The proposed models perform at an accuracy comparable to more complex models in literature.Elsevier2020-02-23T08:42:31Z2020-02-23T08:42:31Z2019Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSalsabeel Shapsough, Rached Dhaouadi, Imran Zualkernan, Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules, Procedia Computer Science, Volume 155, 2019, Pages 463-470, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.08.065.1877-0509http://hdl.handle.net/11073/1662110.1016/j.procs.2019.08.065en_UShttps://doi.org/10.1016/j.procs.2019.08.065oai:repository.aus.edu:11073/166212024-08-22T12:15:12Z |
| spellingShingle | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules Shapsough, Salsabeel Yousef PV Neural Networks Soiling |
| status_str | publishedVersion |
| title | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| title_full | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| title_fullStr | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| title_full_unstemmed | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| title_short | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| title_sort | Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules |
| topic | PV Neural Networks Soiling |
| url | http://hdl.handle.net/11073/16621 |