Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models
Knowing the behavior of solar energy is imperative for its use in photovoltaic systems; moreover, the number of weather stations is insufficient. This study presents a method for the integration of solar resource data: images and datasets.  For this purpose, variables are extracted from...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2023-07-01
|
| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/98648/download/pdf/ |
| _version_ | 1797769790417272832 |
|---|---|
| author | Luis Eduardo Ordoñez Palacios Víctor Bucheli Guerrero Hugo Ordoñez |
| author_facet | Luis Eduardo Ordoñez Palacios Víctor Bucheli Guerrero Hugo Ordoñez |
| author_sort | Luis Eduardo Ordoñez Palacios |
| collection | DOAJ |
| description | Knowing the behavior of solar energy is imperative for its use in photovoltaic systems; moreover, the number of weather stations is insufficient. This study presents a method for the integration of solar resource data: images and datasets.  For this purpose, variables are extracted from images obtained from the GOES-13 satellite and integrated with variables obtained from meteorological stations. Subsequently, this data integration was used to train solar radiation prediction models in three different scenarios with data from 2012 and 2017. The predictive ability of five regression methods was evaluated, of which, neural networks had the highest performance in the scenario that integrates the meteorological variables and features obtained from the images. The analysis was performed using four evaluation metrics in each year. In the 2012 dataset, an R2 of 0.88 and an RMSE of 90.99 were obtained. On the other hand, in the 2017 dataset, an R2 of 0.92 and an RMSE of 40.97 were achieved. The model integrating data improves performance by up to 4% in R2 and up to 10 points less in the level of dispersion according to RMSE, with respect to models using separate data. |
| first_indexed | 2024-03-12T21:13:07Z |
| format | Article |
| id | doaj.art-9829dc59560149329ea7467cfc4802ed |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-03-12T21:13:07Z |
| publishDate | 2023-07-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-9829dc59560149329ea7467cfc4802ed2023-07-30T08:11:04ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682023-07-0129773875810.3897/jucs.9864898648Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning modelsLuis Eduardo Ordoñez Palacios0Víctor Bucheli Guerrero1Hugo Ordoñez2Universidad del ValleUniversidad del ValleUniversidad del CaucaKnowing the behavior of solar energy is imperative for its use in photovoltaic systems; moreover, the number of weather stations is insufficient. This study presents a method for the integration of solar resource data: images and datasets.  For this purpose, variables are extracted from images obtained from the GOES-13 satellite and integrated with variables obtained from meteorological stations. Subsequently, this data integration was used to train solar radiation prediction models in three different scenarios with data from 2012 and 2017. The predictive ability of five regression methods was evaluated, of which, neural networks had the highest performance in the scenario that integrates the meteorological variables and features obtained from the images. The analysis was performed using four evaluation metrics in each year. In the 2012 dataset, an R2 of 0.88 and an RMSE of 90.99 were obtained. On the other hand, in the 2017 dataset, an R2 of 0.92 and an RMSE of 40.97 were achieved. The model integrating data improves performance by up to 4% in R2 and up to 10 points less in the level of dispersion according to RMSE, with respect to models using separate data.https://lib.jucs.org/article/98648/download/pdf/GOES-13Meteorological stationsSolar Radiation |
| spellingShingle | Luis Eduardo Ordoñez Palacios Víctor Bucheli Guerrero Hugo Ordoñez Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models Journal of Universal Computer Science GOES-13 Meteorological stations Solar Radiation |
| title | Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| title_full | Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| title_fullStr | Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| title_full_unstemmed | Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| title_short | Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| title_sort | integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models |
| topic | GOES-13 Meteorological stations Solar Radiation |
| url | https://lib.jucs.org/article/98648/download/pdf/ |
| work_keys_str_mv | AT luiseduardoordonezpalacios integrationofsatelliteimageryandmeteorologicaldatatoestimatesolarradiationusingmachinelearningmodels AT victorbucheliguerrero integrationofsatelliteimageryandmeteorologicaldatatoestimatesolarradiationusingmachinelearningmodels AT hugoordonez integrationofsatelliteimageryandmeteorologicaldatatoestimatesolarradiationusingmachinelearningmodels |