Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery

Energy

Volumn 73, Issue , 2014, Pages 890-897

  Alonso, J ^a   Batlles, F J ^a  

^a UNIVERSITY OF ALMERÍA   (Spain)

Author keywords

Cloudiness forecasting; Remote sensing; Sky camera; Solar radiation

Indexed keywords

CAMERAS; FORECASTING; REMOTE SENSING; SOLAR ENERGY; SOLAR RADIATION; SUN;

CLOUD COVER; CONCENTRATED SOLAR POWER; ELECTRICITY PRODUCTION; REMOTE SENSING TECHNIQUES; SKY CAMERAS;

CLOUDS;

CLOUD COVER; ELECTRICITY GENERATION; FORECASTING METHOD; OPTIMIZATION; PHOTOGRAPHY; REMOTE SENSING; SOLAR RADIATION;

EID: 84905725206     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2014.06.101     Document Type: Article

References (31)

1. Gómez A., Zubizarreta J., Dopazo C., Fueyo N. Spanish energy roadmap to 2020: socioeconomic implications of renewable targets. Energy 2011, 36:1973-1985.
2. Gonçalves C. Renewable energies: choosing the best options. Energy 2010, 35:3179-3193.
3. Oak N., Lawson D., Champneys A. Performance comparison of renewable incentive schemes using optimal control. Energy 2014, 64:44-57.
4. An N., Zhao W., Wang J., Shang D., Zhao E. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 2013, 49:279-288.
5. Zidanšek A., Ambrožič M., Milfelner M., Blinc R., Lior N. Solar orbital power: sustainability analysis. Energy 2011, 36:1986-1995.
6. Zarzalejo L.F., Ramirez L., Polo J. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index. Energy 2005, 30:1685-1697.
7. Kisi O. Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy 2014, 64:429-436.
8. López G., Batlles F.J., Tovar-Pescador J. Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 2005, 30:1675-1684.
9. Rusen S.E., Hammer A., Akinoglu B.G. Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery. Energy 2013, 58:417-425.
10. Moreno A., Gilabert M.A., Camacho F., Martínez B. Validation of daily global irradiation images from MSG over Spain. Renew Energy 2013, 60:332-342.
11. Ghosh A., Pal N.R., Das J. A fuzzy rule based approach to cloud cover estimation. Remote Sens Environ 2006, 100:531-549.
12. Escrig H., Batlles F.J., Alonso J., Baena F.M., Bosch J.L., Salbidegoitia I.B., et al. Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast. Energy 2013, 55:853-859.
13. Leese J.A., Novak C.S., Taylor V.R. The determination of cloud pattern motions from geosynchronous satellite image data. Pattern Recognit 1970, 2(4):279-292.
14. Rutledge G., Legg E., Menzel P. Operational production of winds from cloud motions. Global Planet Change 1991, 4(13):141-150.
15. Laurent H. Wind extraction from meteosat water vapor channel image data. J Appl Meteorol 1993, 32(6):1124-1133.
16. Schmetz J., Holmlund K., Hoffman J., Strauss B., Mason B., Gaertner V., et al. Operational cloud-motion winds from Meteosat infrared images. J Appl Meteorol 1993, 32(7):1206-1225.
17. Gueymard C.A. Importance of atmospheric turbidity and associated uncertainties in solar radiation and luminous efficacy modelling. Energy 2005, 30:1603-1621.
18. Olmo F.J., Cazorla A., Alados-Arboledas L., López-Álvarez M.A., Hernández-Andrés J., Romero J. Retrieval of the optical depth using an all-sky CCD camera. Appl Opt 2008, 47:182-189.
19. Kassianov E.I., Long C.N., Ovtchinnikov M. Cloud sky cover versus cloud fraction: whole-sky simulations and observations. J Appl Meteorol 2005, 44:86-98.
20. Cazorla A., Olmo F.J., Alados-Arboledas L. Estimación de la cubierta nubosa en imágenes de cielo mediante el algoritmo de clasificación KNN. Puerto la Cruz 2005, 335-338. Tenerife (España): XI Congreso Nacional de Teledetección.
21. Heinle A., Macke A., Srivastav A. Automatic cloud classification of whole sky images. Atmos Meas Tech 2010, 3:557-567.
22. Martínez-Chico M., Batlles F.J., Bosch J.L. Cloud classification in a mediterranean location using radiation data and sky images. Energy 2011, 36:4055-4062.
23. Thuillier G., Perrin J., Keckhut P., Huppert F. Local enhanced solar irradiance on the ground genererated by cirrus: measurements and interpretation. J Appl Remote Sens 2013, 7. Art. no. 13057.
24. Long C.N., Sabburg J.M., Calbó J., Pagès D. Retrieving cloud characteristics from ground-based daytime color all-sky images. J Atmos Ocean Technol 2006, 23:633-652.
25. Román R., Antón M., Cazorla A., de Miguel A., Olmo F.J., Bilbao J., et al. Calibration of an all-sky camera for obtaining sky radiance at three wavelengths. Atmos Meas Tech Discuss 2012, 5:1873-1905.
26. Kazantzidis A., Tzoumanikas P., Bais A.F., Fotopoulos S., Economou G. Cloud detection and classification with the use of whole-sky ground-based images. Atmos Res 2012, 113:80-88.
27. Alonso J., Batlles F.J., Villarroel C., Ayala R., Burgaleta J.I. Determination of the sun area in sky camera images using radiometric data. Energy Convers Manag 2014, 78:24-31.
28. Alonso J., Batlles F.J., Ternero A., López G. Sky camera imagery processing based on a sky classification using radiometric data. Energy 2014, 10.1016/j.energy.2014.02.035.
29. Schmetz J., Pili P., Tjemkes S., Just D., Kerkmann J., Rota S., et al. An introduction to meteosat second generation (MSG). Bull Am Meteorol Soc 2002, 83(7):977-992.
30. Stowe L.L., Wellemeyer C.G., Yeh H.Y.M., Eck T.F. Nimbus-7 global cloud climatology. part I: algorithms and validation. J Clim 1988, 1(5):445-470.
31. Markou M., Bartzokas A., Kambezidis H. Daylight climatology in Athens, Greece: types of diurnal variation of illuminance levels. Int J Climatol 2009, 29(14):2137-2145.