An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations
Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/21/5496 |
| _version_ | 1797466573174210560 |
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| author | Martín Muñoz-Salcedo Fernando Peci-López Francisco Táboas |
| author_facet | Martín Muñoz-Salcedo Fernando Peci-López Francisco Táboas |
| author_sort | Martín Muñoz-Salcedo |
| collection | DOAJ |
| description | Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess its potential. Remote sensing represents one of the low-cost solutions for solar energy assessment. Nevertheless, cloud cover is a main problem when validating the data. This study identifies satellite GHI profiles that cannot be used in energy production simulation. The validation is performed using parametric and non-parametric statistical tests. From the profile identified as invalid for simulation purposes, a site-adaptation methodology is proposed based on statistical learning using the machine learning algorithms “Best subset selection” and “Forward Stepwise Selection”. Linear and non-linear heuristic models are also proposed. The final AS7 model is selected through RMSE, MBE and adjusted R<sup>2</sup> indicators and is valid for any sky condition. The results show an increase in R<sup>2</sup> from 0.607 to 0.876. |
| first_indexed | 2024-03-09T18:41:42Z |
| format | Article |
| id | doaj.art-0c0c0f801563489a9e1eb4c072159478 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0c0c0f801563489a9e1eb4c0721594782023-11-24T06:39:58ZengMDPI AGRemote Sensing2072-42922022-10-011421549610.3390/rs14215496An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index LocationsMartín Muñoz-Salcedo0Fernando Peci-López1Francisco Táboas2Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro, Milagro 091051, EcuadorDepartamento de Química-Física y Termodinámica Aplicada, Universidad de Córdoba, 14014 Córdoba, SpainDepartamento de Química-Física y Termodinámica Aplicada, Universidad de Córdoba, 14014 Córdoba, SpainFacing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess its potential. Remote sensing represents one of the low-cost solutions for solar energy assessment. Nevertheless, cloud cover is a main problem when validating the data. This study identifies satellite GHI profiles that cannot be used in energy production simulation. The validation is performed using parametric and non-parametric statistical tests. From the profile identified as invalid for simulation purposes, a site-adaptation methodology is proposed based on statistical learning using the machine learning algorithms “Best subset selection” and “Forward Stepwise Selection”. Linear and non-linear heuristic models are also proposed. The final AS7 model is selected through RMSE, MBE and adjusted R<sup>2</sup> indicators and is valid for any sky condition. The results show an increase in R<sup>2</sup> from 0.607 to 0.876.https://www.mdpi.com/2072-4292/14/21/5496solar resource assessmentcloudiness empirical modelsite-adaptationremote sensing |
| spellingShingle | Martín Muñoz-Salcedo Fernando Peci-López Francisco Táboas An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations Remote Sensing solar resource assessment cloudiness empirical model site-adaptation remote sensing |
| title | An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations |
| title_full | An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations |
| title_fullStr | An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations |
| title_full_unstemmed | An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations |
| title_short | An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations |
| title_sort | empirical correction model for remote sensing data of global horizontal irradiance in high cloudiness index locations |
| topic | solar resource assessment cloudiness empirical model site-adaptation remote sensing |
| url | https://www.mdpi.com/2072-4292/14/21/5496 |
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