Machine learning for predictive maintenance of photovoltaic panels: cleaning process application
It is well known that the presence of dust on the surface of PV modules has a significant impact on their efficiency. Then, the consequent reduction in energy production has a non-negligible effect on the incomes. Although there is a growing need for accurate data showing where the solar arrays need...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/03/e3sconf_icegc2022_00021.pdf |
| _version_ | 1818964807544471552 |
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| author | Nabti Mohamed Bybi Abdelmajid Chater El Ayachi Garoum Mohammed |
| author_facet | Nabti Mohamed Bybi Abdelmajid Chater El Ayachi Garoum Mohammed |
| author_sort | Nabti Mohamed |
| collection | DOAJ |
| description | It is well known that the presence of dust on the surface of PV modules has a significant impact on their efficiency. Then, the consequent reduction in energy production has a non-negligible effect on the incomes. Although there is a growing need for accurate data showing where the solar arrays need maintenance in this rush for renewable energy. The purpose of this article is to introduce the research on existing photovoltaic panel maintenance solutions and introduce a new machine learning algorithm application to minimize the cleaning process of photovoltaic modules. |
| first_indexed | 2024-12-20T13:06:59Z |
| format | Article |
| id | doaj.art-062bee73681a486693a444c581db64dd |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-20T13:06:59Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-062bee73681a486693a444c581db64dd2022-12-21T19:39:45ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013360002110.1051/e3sconf/202233600021e3sconf_icegc2022_00021Machine learning for predictive maintenance of photovoltaic panels: cleaning process applicationNabti Mohamed0Bybi Abdelmajid1Chater El Ayachi2Garoum Mohammed3Mohammed V University in Rabat, Higher School of Technology in SaleMohammed V University in Rabat, Higher School of Technology in SaleMohammed V University in Rabat, Higher School of Technology in SaleMohammed V University in Rabat, Higher School of Technology in SaleIt is well known that the presence of dust on the surface of PV modules has a significant impact on their efficiency. Then, the consequent reduction in energy production has a non-negligible effect on the incomes. Although there is a growing need for accurate data showing where the solar arrays need maintenance in this rush for renewable energy. The purpose of this article is to introduce the research on existing photovoltaic panel maintenance solutions and introduce a new machine learning algorithm application to minimize the cleaning process of photovoltaic modules.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/03/e3sconf_icegc2022_00021.pdf |
| spellingShingle | Nabti Mohamed Bybi Abdelmajid Chater El Ayachi Garoum Mohammed Machine learning for predictive maintenance of photovoltaic panels: cleaning process application E3S Web of Conferences |
| title | Machine learning for predictive maintenance of photovoltaic panels: cleaning process application |
| title_full | Machine learning for predictive maintenance of photovoltaic panels: cleaning process application |
| title_fullStr | Machine learning for predictive maintenance of photovoltaic panels: cleaning process application |
| title_full_unstemmed | Machine learning for predictive maintenance of photovoltaic panels: cleaning process application |
| title_short | Machine learning for predictive maintenance of photovoltaic panels: cleaning process application |
| title_sort | machine learning for predictive maintenance of photovoltaic panels cleaning process application |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/03/e3sconf_icegc2022_00021.pdf |
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