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Prediction improvement of potential PV production pattern, imagery satellite-based
The results obtained by using an existing model to estimate global solar radiation (GHI) in three different locations in Tunisia. These data are compared with GHI meteorological measurements and PV_Gis satellite imagery estimation. Some statistical indicators (R, R(2), MPE, AMPE, MBE, AMBE and RMSE)...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673135/ https://www.ncbi.nlm.nih.gov/pubmed/33204008 http://dx.doi.org/10.1038/s41598-020-76957-8 |
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author | Ben Othman, A. Belkilani, K. Besbes, M. |
author_facet | Ben Othman, A. Belkilani, K. Besbes, M. |
author_sort | Ben Othman, A. |
collection | PubMed |
description | The results obtained by using an existing model to estimate global solar radiation (GHI) in three different locations in Tunisia. These data are compared with GHI meteorological measurements and PV_Gis satellite imagery estimation. Some statistical indicators (R, R(2), MPE, AMPE, MBE, AMBE and RMSE) have been used to measure the performance of the used model. Correlation coefficient for the different stations was close to 1.0. The meteorology and satellite determination coefficient (R(2)) were also near 1.0 except in the case of Nabeul station in which the meteorology measurements (R) were equals to 0.5848 because of the loss of data in this location due to meteorological conditions. This numerical model provides the best performance according to statistical results in different locations; therefore, this model can be used to estimate global solar radiation in Tunisia. The R square values are used as a statistical indicator to demonstrate that the model’s results are compatible with those of meteorology with a percentage of error less than 10%. |
format | Online Article Text |
id | pubmed-7673135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76731352020-11-19 Prediction improvement of potential PV production pattern, imagery satellite-based Ben Othman, A. Belkilani, K. Besbes, M. Sci Rep Article The results obtained by using an existing model to estimate global solar radiation (GHI) in three different locations in Tunisia. These data are compared with GHI meteorological measurements and PV_Gis satellite imagery estimation. Some statistical indicators (R, R(2), MPE, AMPE, MBE, AMBE and RMSE) have been used to measure the performance of the used model. Correlation coefficient for the different stations was close to 1.0. The meteorology and satellite determination coefficient (R(2)) were also near 1.0 except in the case of Nabeul station in which the meteorology measurements (R) were equals to 0.5848 because of the loss of data in this location due to meteorological conditions. This numerical model provides the best performance according to statistical results in different locations; therefore, this model can be used to estimate global solar radiation in Tunisia. The R square values are used as a statistical indicator to demonstrate that the model’s results are compatible with those of meteorology with a percentage of error less than 10%. Nature Publishing Group UK 2020-11-17 /pmc/articles/PMC7673135/ /pubmed/33204008 http://dx.doi.org/10.1038/s41598-020-76957-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ben Othman, A. Belkilani, K. Besbes, M. Prediction improvement of potential PV production pattern, imagery satellite-based |
title | Prediction improvement of potential PV production pattern, imagery satellite-based |
title_full | Prediction improvement of potential PV production pattern, imagery satellite-based |
title_fullStr | Prediction improvement of potential PV production pattern, imagery satellite-based |
title_full_unstemmed | Prediction improvement of potential PV production pattern, imagery satellite-based |
title_short | Prediction improvement of potential PV production pattern, imagery satellite-based |
title_sort | prediction improvement of potential pv production pattern, imagery satellite-based |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673135/ https://www.ncbi.nlm.nih.gov/pubmed/33204008 http://dx.doi.org/10.1038/s41598-020-76957-8 |
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