Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems
The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Renewable Energy Development Center (CDER)
2022-12-01
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| Series: | Revue des Énergies Renouvelables |
| Subjects: | |
| Online Access: | https://revue.cder.dz/index.php/rer/article/view/1082 |
| _version_ | 1797778346629660672 |
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| author | Hichem Hafdaoui El Amin Kouadri Boudjelthia Salim Bouchakour Nasreddine Belhaouas |
| author_facet | Hichem Hafdaoui El Amin Kouadri Boudjelthia Salim Bouchakour Nasreddine Belhaouas |
| author_sort | Hichem Hafdaoui |
| collection | DOAJ |
| description | The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring. |
| first_indexed | 2024-03-12T23:15:56Z |
| format | Article |
| id | doaj.art-edd2938fefef4b33afe3b27d955ba43c |
| institution | Directory Open Access Journal |
| issn | 1112-2242 2716-8247 |
| language | English |
| last_indexed | 2024-03-12T23:15:56Z |
| publishDate | 2022-12-01 |
| publisher | Renewable Energy Development Center (CDER) |
| record_format | Article |
| series | Revue des Énergies Renouvelables |
| spelling | doaj.art-edd2938fefef4b33afe3b27d955ba43c2023-07-17T09:42:02ZengRenewable Energy Development Center (CDER)Revue des Énergies Renouvelables1112-22422716-82472022-12-01252199 – 210199 – 21010.54966/jreen.v25i2.10821082Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systemsHichem Hafdaoui0El Amin Kouadri Boudjelthia1Salim Bouchakour2Nasreddine Belhaouas3Centre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah, 16340, Algiers, AlgeriaCentre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah, 16340, Algiers, AlgeriaCentre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah, 16340, Algiers, AlgeriaCentre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah, 16340, Algiers, AlgeriaThe performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring.https://revue.cder.dz/index.php/rer/article/view/1082photovoltaic (pv)monitoringclassificationartificial intelligencemaximum power |
| spellingShingle | Hichem Hafdaoui El Amin Kouadri Boudjelthia Salim Bouchakour Nasreddine Belhaouas Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems Revue des Énergies Renouvelables photovoltaic (pv) monitoring classification artificial intelligence maximum power |
| title | Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems |
| title_full | Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems |
| title_fullStr | Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems |
| title_full_unstemmed | Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems |
| title_short | Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems |
| title_sort | use an artificial intelligence method machine learning for analysis of the performance of photovoltaic systems |
| topic | photovoltaic (pv) monitoring classification artificial intelligence maximum power |
| url | https://revue.cder.dz/index.php/rer/article/view/1082 |
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