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A Multivariate Regression Model for the Assessment of Solar Radiation in the Senegalese Territories

  • Ousmane WaneEmail author
  • A. A. Navarro
  • L. Ramírez
  • R. X. Valenzuela
  • José M. Vindel
  • F. Ferrera Cobos
  • Cheikh M. F. Kébé
  • L. F. Zarzalejo
Conference paper
  • 334 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 204)

Abstract

Senegal has a great solar potential, so it could be used to shift from a diesel-based power generation to cheaper renewable energy resources. To exploit this inexhaustible natural resource, the global horizontal irradiation remains one of the key parameters for any solar energy project at a given location. This work establishes a multiple linear regression approach to estimate the solar radiation in the Senegalese territories using the information of the global network of weather geostationary satellites (Meteosat and GOES), satellites database and the ground measurement data available in the website of the World Radiation Data Center (WRDC) as inputs to the model. Jointly a set of multivariate regression models, a statistical analysis between Meteonorm data and outputs of different linear combinations are presented in this work, which also gives the opportunity to appreciate the precision and consistency of each solar radiation model on different locations in the study area.

Keywords

Solar radiation Global horizontal irradiation Multivariate regression model Satellites database 

Notes

Acknowledgement

An acknowledgement to the Radiation Solar Group of CIEMAT for supervising this work, not forgetting the Spanish Cooperation Agency (AECID) for financing of my stay at this research center.

References

  1. Amillo, A.G., Huld, T., Müller, R.: A new database of global and direct solar radiation using the eastern meteosat satellite, models and validation. Remote Sens. 6, 8165–8189 (2014).  https://doi.org/10.3390/rs6098165 CrossRefGoogle Scholar
  2. Ba, M.B., Nicholson, S.E.: Satellite-derived surface radiation budget over the african continent. Part II: climatologies of the various components. J. Clim. 14, 60–76 (2001).  https://doi.org/10.1175/1520-0442(2001)014<0060:SDSRBO>2.0.CO;2
  3. Diabaté, L., Blanc, P., Wald, L.: Solar radiation climate in Africa. Sol. Energy 76, 733–744 (2004).  https://doi.org/10.1016/j.solener.2004.01.002 CrossRefGoogle Scholar
  4. Huld, T., Müller, R., Gambardella, A.: A new solar radiation database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012).  https://doi.org/10.1016/j.solener.2012.03.006 CrossRefGoogle Scholar
  5. Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F.: World map of the Köppen-Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).  https://doi.org/10.1127/0941-2948/2006/0130 CrossRefGoogle Scholar
  6. Lefèvre, M., Wald, L., Diabaté, L.: Using reduced data sets ISCCP-B2 from the Meteosat satellites to assess surface solar irradiance. Sol. Energy 81, 240–253 (2007).  https://doi.org/10.1016/j.solener.2006.03.008 CrossRefGoogle Scholar
  7. Zarzalejo, L.F.: Estimación de la radiación global horaria a partir de imagenes de satelite. Desarrollo de modelo empíricos. Universidad Complutense de Madrid (2005)Google Scholar
  8. METEOTEST: Meteonorm [WWW Document] (2016). http://meteonorm.com. Accessed 9 Feb 2016
  9. Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. Wiley, Hoboken (2003)zbMATHGoogle Scholar
  10. NASA-SSE: Langley Research Center Atmospheric Science Data Center Surface meteorological and Solar Energy (SSE) web portal supported by the NASA LaRC POWER Project [WWW Document] (2016). https://eosweb.larc.nasa.gov/sse/. Accessed 8 Feb 2016
  11. Núñez, E., Steyerberg, E.W., Núñez, J.: Estrategias para la elaboración de modelos estadísticos de regresión. Rev. Esp. Cardiol. 64, 501–507 (2011).  https://doi.org/10.1016/j.recesp.2011.01.019 CrossRefGoogle Scholar
  12. Obrecht, D.: Météorologie solaire et images satellitaires : cartographie du rayonnement solaire, détermination de l’albédo des sols et évaluation de l’ennuagement (1990)Google Scholar
  13. Perez, R., Perez, R., Seals, R., Zelenka, A.: Comparing satellite remote sensing and ground network measurements for the production of site/time specific irradiance data. Sol. Energy 60, 89–96 (1997).  https://doi.org/10.1016/S0038-092X(96)00162-4 CrossRefGoogle Scholar
  14. Perpiña Castillo, C., Batista e Silva, F., Lavalle, C.: An assessment of the regional potential for solar power generation in EU-28. Energy Policy 88, 86–99 (2016).  https://doi.org/10.1016/j.enpol.2015.10.004 CrossRefGoogle Scholar
  15. Posselt, R., Mueller, R., Trentmann, J., Stockli, R., Liniger, M.A.: A surface radiation climatology across two Meteosat satellite generations. Remote Sens. Environ. 142, 103–110 (2014).  https://doi.org/10.1016/j.rse.2013.11.007 CrossRefGoogle Scholar
  16. PVGIS: Photovoltaic Geographical Information System - Interactive Maps [WWW Document] (2016). http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php?map=africa. Accessed 2 Jan 2016
  17. Remund, J., Müller, S., Kunz, S., Huguenin-Landl, B., Studer, C., Klauser, D., Schilter, C., Lehnherr, R.: Meteonorm: Global Meteorological Databases. Handbook Part I : Software v7 (2015)Google Scholar
  18. Rigollier, C., Lefèvre, M., Wald, L.: The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Sol. Energy 77, 159–169 (2004).  https://doi.org/10.1016/j.solener.2004.04.017 CrossRefGoogle Scholar
  19. Šúri, M., Huld, T.A., Dunlop, E.D., Ossenbrink, H.A.: Potential of solar electricity generation in the European Union member states and candidate countries. Sol. Energy 81, 1295–1305 (2007).  https://doi.org/10.1016/j.solener.2006.12.007 CrossRefGoogle Scholar
  20. Walther, B.A., Moore, J.L.: The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28(6), 815–829 (2005)CrossRefGoogle Scholar
  21. WRDC: World Radiation Data Centre Online Archive (2016) [WWW Document]. http://wrdc-mgo.nrel.gov/. Accessed 30 Jan 2016
  22. Youm, I., Sarr, J., Sall, M., Kane, M.M.: Renewable energy activities in Senegal: a review. Renew. Sustain. Energy Rev. 4, 75–89 (2000).  https://doi.org/10.1016/S1364-0321(99)00009-X CrossRefGoogle Scholar
  23. Zarzalejo, L.F., Polo, J., Martín, L., Ramírez, L., Espinar, B.: A new statistical approach for deriving global solar radiation from satellite images. Sol. Energy 83, 480–484 (2009).  https://doi.org/10.1016/j.solener.2008.09.006 CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Ousmane Wane
    • 1
    Email author
  • A. A. Navarro
    • 2
  • L. Ramírez
    • 2
  • R. X. Valenzuela
    • 2
  • José M. Vindel
    • 2
  • F. Ferrera Cobos
    • 2
  • Cheikh M. F. Kébé
    • 1
  • L. F. Zarzalejo
    • 2
  1. 1.CIFRES, Ecole Supérieure Polytechnique - UCADDakar-FannSenegal
  2. 2.CIEMAT, Energy Department – Renewable Energy DivisionMadridSpain

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