Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery
The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/16/2643 |
| _version_ | 1797557675515445248 |
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| author | Román Mondragón Joaquín Alonso-Montesinos David Riveros-Rosas Roberto Bonifaz |
| author_facet | Román Mondragón Joaquín Alonso-Montesinos David Riveros-Rosas Roberto Bonifaz |
| author_sort | Román Mondragón |
| collection | DOAJ |
| description | The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%. |
| first_indexed | 2024-03-10T17:21:06Z |
| format | Article |
| id | doaj.art-21493ea72bcb4cecafbbcfeb5f7aadcb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T17:21:06Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-21493ea72bcb4cecafbbcfeb5f7aadcb2023-11-20T10:20:29ZengMDPI AGRemote Sensing2072-42922020-08-011216264310.3390/rs12162643Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam ImageryRomán Mondragón0Joaquín Alonso-Montesinos1David Riveros-Rosas2Roberto Bonifaz3Department of Solar Radiation at the Geophysics, Institute of the National Autonomous, University of México, México City 4513, MexicoDepartment of Chemistry and Physics, University of Almería, 04120 Almería, SpainDepartment of Solar Radiation at the Geophysics, Institute of the National Autonomous, University of México, México City 4513, MexicoDepartment of Solar Radiation at the Geophysics, Institute of the National Autonomous, University of México, México City 4513, MexicoThe atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%.https://www.mdpi.com/2072-4292/12/16/2643cloud detectiondigitized image processingsolar irradiance estimationsolar irradiance forecastingsolar energysky camera |
| spellingShingle | Román Mondragón Joaquín Alonso-Montesinos David Riveros-Rosas Roberto Bonifaz Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery Remote Sensing cloud detection digitized image processing solar irradiance estimation solar irradiance forecasting solar energy sky camera |
| title | Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery |
| title_full | Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery |
| title_fullStr | Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery |
| title_full_unstemmed | Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery |
| title_short | Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery |
| title_sort | determination of cloud motion applying the lucas kanade method to sky cam imagery |
| topic | cloud detection digitized image processing solar irradiance estimation solar irradiance forecasting solar energy sky camera |
| url | https://www.mdpi.com/2072-4292/12/16/2643 |
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