Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques
Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installati...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/9820 |
| _version_ | 1789406609030512640 |
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| author | Simal Pérez, Noelia Alonso Montesinos, Joaquín Blas Javier Batlles, Francisco |
| author_facet | Simal Pérez, Noelia Alonso Montesinos, Joaquín Blas Javier Batlles, Francisco |
| author_sort | Simal Pérez, Noelia |
| collection | DSpace |
| description | Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE). |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-9820 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-98202023-10-27T10:53:34Z Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques Simal Pérez, Noelia Alonso Montesinos, Joaquín Blas Javier Batlles, Francisco soiling photovoltaic plant solar energy PV plant maintenance ANN machine learning Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE). 2021-02-15T11:50:08Z 2021-02-15T11:50:08Z 2021-02-08 info:eu-repo/semantics/article 2076-3417 http://hdl.handle.net/10835/9820 en https://www.mdpi.com/2076-3417/11/4/1516 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | soiling photovoltaic plant solar energy PV plant maintenance ANN machine learning Simal Pérez, Noelia Alonso Montesinos, Joaquín Blas Javier Batlles, Francisco Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title | Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title_full | Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title_fullStr | Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title_full_unstemmed | Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title_short | Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques |
| title_sort | estimation of soiling losses from an experimental photovoltaic plant using artificial intelligence techniques |
| topic | soiling photovoltaic plant solar energy PV plant maintenance ANN machine learning |
| url | http://hdl.handle.net/10835/9820 |
| work_keys_str_mv | AT simalpereznoelia estimationofsoilinglossesfromanexperimentalphotovoltaicplantusingartificialintelligencetechniques AT alonsomontesinosjoaquinblas estimationofsoilinglossesfromanexperimentalphotovoltaicplantusingartificialintelligencetechniques AT javierbatllesfrancisco estimationofsoilinglossesfromanexperimentalphotovoltaicplantusingartificialintelligencetechniques |