Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks
The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an operational photovolta...
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2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5844 |
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| author | Sonia Montecinos Carlos Rodríguez Andrea Torrejón Jorge Cortez Marcelo Jaque |
| author_facet | Sonia Montecinos Carlos Rodríguez Andrea Torrejón Jorge Cortez Marcelo Jaque |
| author_sort | Sonia Montecinos |
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| description | The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an operational photovoltaic park in the semi-arid north of Chile using Artificial Neural Networks (ANNs). We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. The ANN model’s effectiveness was evaluated by comparing it to five different deterministic models: the Standard model, King’s model, Faiman’s model, Mattei’s model, and Skoplaki’s model. Additionally, we examined the sensitivity of panel temperature to changes in air temperature, TSR, and wind speed. Our findings show that the ANN model had the best prediction accuracy for panel temperature, with a Root Mean Squared Error (RMSE) of 1.59 °C, followed by Mattei’s model with a higher RMSE of 3.30 °C. We also determined that air temperature has the most significant impact on panel temperature, followed by TSR and wind speed. These results demonstrate that the ANN is a powerful tool for predicting panel temperature with high accuracy. |
| format | Article |
| id | doaj-art-e8f6bc4ad12f45c2940ec88ef368a0f9 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e8f6bc4ad12f45c2940ec88ef368a0f92024-12-13T16:25:06ZengMDPI AGEnergies1996-10732024-11-011723584410.3390/en17235844Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural NetworksSonia Montecinos0Carlos Rodríguez1Andrea Torrejón2Jorge Cortez3Marcelo Jaque4Departamento de Física, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, ChileDepartamento de Química, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, ChileDepartamento de Física, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, ChileDepartamento de Ingeniería de Minas, Facultad de Ingeniería, Universidad de La Serena, La Serena 1700000, ChileInstituto Multidisciplinario de Investigación y Postgrado, Universidad de La Serena, La Serena 1700000, ChileThe performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an operational photovoltaic park in the semi-arid north of Chile using Artificial Neural Networks (ANNs). We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. The ANN model’s effectiveness was evaluated by comparing it to five different deterministic models: the Standard model, King’s model, Faiman’s model, Mattei’s model, and Skoplaki’s model. Additionally, we examined the sensitivity of panel temperature to changes in air temperature, TSR, and wind speed. Our findings show that the ANN model had the best prediction accuracy for panel temperature, with a Root Mean Squared Error (RMSE) of 1.59 °C, followed by Mattei’s model with a higher RMSE of 3.30 °C. We also determined that air temperature has the most significant impact on panel temperature, followed by TSR and wind speed. These results demonstrate that the ANN is a powerful tool for predicting panel temperature with high accuracy.https://www.mdpi.com/1996-1073/17/23/5844photovoltaic parkpanel temperatureartificial neural networksarid zones |
| spellingShingle | Sonia Montecinos Carlos Rodríguez Andrea Torrejón Jorge Cortez Marcelo Jaque Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks Energies photovoltaic park panel temperature artificial neural networks arid zones |
| title | Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks |
| title_full | Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks |
| title_fullStr | Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks |
| title_full_unstemmed | Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks |
| title_short | Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks |
| title_sort | panel temperature dependence on atmospheric parameters of an operative photovoltaic park in semi arid zones using artificial neural networks |
| topic | photovoltaic park panel temperature artificial neural networks arid zones |
| url | https://www.mdpi.com/1996-1073/17/23/5844 |
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