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Solar resources estimation combining digital terrain models and satellite images techniques

Author

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  • Bosch, J.L.
  • Batlles, F.J.
  • Zarzalejo, L.F.
  • López, G.

Abstract

One of the most important steps to make use of any renewable energy is to perform an accurate estimation of the resource that has to be exploited. In the designing process of both active and passive solar energy systems, radiation data is required for the site, with proper spatial resolution. Generally, a radiometric stations network is used in this evaluation, but when they are too dispersed or not available for the study area, satellite images can be utilized as indirect solar radiation measurements. Although satellite images cover wide areas with a good acquisition frequency they usually have a poor spatial resolution limited by the size of the image pixel, and irradiation must be interpolated to evaluate solar irradiation at a sub-pixel scale. When pixels are located in flat and homogeneous areas, correlation of solar irradiation is relatively high, and classic interpolation can provide a good estimation. However, in complex topography zones, data interpolation is not adequate and the use of Digital Terrain Model (DTM) information can be helpful. In this work, daily solar irradiation is estimated for a wide mountainous area using a combination of Meteosat satellite images and a DTM, with the advantage of avoiding the necessity of ground measurements. This methodology utilizes a modified Heliosat-2 model, and applies for all sky conditions; it also introduces a horizon calculation of the DTM points and accounts for the effect of snow covers. Model performance has been evaluated against data measured in 12 radiometric stations, with results in terms of the Root Mean Square Error (RMSE) of 10%, and a Mean Bias Error (MBE) of +2%, both expressed as a percentage of the mean value measured.

Suggested Citation

  • Bosch, J.L. & Batlles, F.J. & Zarzalejo, L.F. & López, G., 2010. "Solar resources estimation combining digital terrain models and satellite images techniques," Renewable Energy, Elsevier, vol. 35(12), pages 2853-2861.
  • Handle: RePEc:eee:renene:v:35:y:2010:i:12:p:2853-2861
    DOI: 10.1016/j.renene.2010.05.011
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    References listed on IDEAS

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    1. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
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    Citations

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    1. Yichao Xu & Xinying Wang & Zhiqiang Jiang & Yi Liu & Li Zhang & Yukun Li, 2023. "An Improved Fineness Flood Risk Analysis Method Based on Digital Terrain Acquisition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3973-3998, August.
    2. Niu, Yinsen & Song, Jifeng & Zou, Lianglin & Yan, Zixuan & Lin, Xilong, 2024. "Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold," Renewable Energy, Elsevier, vol. 226(C).
    3. Martínez-Chico, M. & Batlles, F.J. & Bosch, J.L., 2011. "Cloud classification in a mediterranean location using radiation data and sky images," Energy, Elsevier, vol. 36(7), pages 4055-4062.
    4. Bessafi, Miloud & Oree, Vishwamitra & Khoodaruth, Abdel & Chabriat, Jean-Pierre, 2020. "Impact of decomposition and kriging models on the solar irradiance downscaling accuracy in regions with complex topography," Renewable Energy, Elsevier, vol. 162(C), pages 1992-2003.
    5. Moreno, A. & Gilabert, M.A. & Camacho, F. & Martínez, B., 2013. "Validation of daily global solar irradiation images from MSG over Spain," Renewable Energy, Elsevier, vol. 60(C), pages 332-342.
    6. Riveros-Rosas, D. & Arancibia-Bulnes, C.A. & Bonifaz, R. & Medina, M.A. & Peón, R. & Valdes, M., 2015. "Analysis of a solarimetric database for Mexico and comparison with the CSR model," Renewable Energy, Elsevier, vol. 75(C), pages 21-29.
    7. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    8. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    9. Hernández-Escobedo, Q. & Fernández-García, A. & Manzano-Agugliaro, F., 2017. "Solar resource assessment for rural electrification and industrial development in the Yucatan Peninsula (Mexico)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1550-1561.
    10. Pillot, Benjamin & Muselli, Marc & Poggi, Philippe & Haurant, Pierrick & Hared, Idriss, 2013. "The first disaggregated solar atlas of Djibouti: A decision-making tool for solar systems integration in the energy scheme," Renewable Energy, Elsevier, vol. 57(C), pages 57-69.
    11. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    12. Rehman, Naveed ur & Katebi, Milad & Shaikh, Faraz & Al Karim, Miftah, 2020. "Solar resource assessment of modern parking machines in an urban environment," Renewable Energy, Elsevier, vol. 149(C), pages 1406-1413.
    13. Janjai, Serm & Masiri, Itsara & Laksanaboonsong, Jarungsaeng, 2013. "Satellite-derived solar resource maps for Myanmar," Renewable Energy, Elsevier, vol. 53(C), pages 132-140.
    14. Sarmiento, Nilsa & Belmonte, Silvina & Dellicompagni, Pablo & Franco, Judith & Escalante, Karina & Sarmiento, Joaquín, 2019. "A solar irradiation GIS as decision support tool for the Province of Salta, Argentina," Renewable Energy, Elsevier, vol. 132(C), pages 68-80.
    15. Alonso-Montesinos, J. & Martínez-Durbán, M. & del Sagrado, J. & del Águila, I.M. & Batlles, F.J., 2016. "The application of Bayesian network classifiers to cloud classification in satellite images," Renewable Energy, Elsevier, vol. 97(C), pages 155-161.
    16. Luis Ramirez Camargo & Judith Franco & Nilsa María Sarmiento Babieri & Silvina Belmonte & Karina Escalante & Raphaela Pagany & Wolfgang Dorner, 2016. "Technical, Economical and Social Assessment of Photovoltaics in the Frame of the Net-Metering Law for the Province of Salta, Argentina," Energies, MDPI, vol. 9(3), pages 1-21, February.
    17. Bessafi, Miloud & Oree, Vishwamitra & Khoodaruth, Abdel & Jumaux, Guillaume & Bonnardot, François & Jeanty, Patrick & Delsaut, Mathieu & Chabriat, Jean-Pierre & Dauhoo, Muhammad Zaid, 2018. "Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography," Renewable Energy, Elsevier, vol. 126(C), pages 584-593.
    18. Alonso-Montesinos, J. & Batlles, F.J., 2015. "Solar radiation forecasting in the short- and medium-term under all sky conditions," Energy, Elsevier, vol. 83(C), pages 387-393.

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