Authors : Okpaneje Onyinye Theresa; Ndeche Ikechukwu Emmanuel; Ufot Elizabeth Gabriel; Omile Anthony Nduka

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/ycpjm784

Scribd : https://tinyurl.com/yutfkduj

DOI : https://doi.org/10.38124/ijisrt/26jun1603

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Reliable solar-resource characterisation is essential for photovoltaic (PV) project design, yet satellite and reanalysis products can produce climate-dependent biases that propagate into energy-yield and financial estimates. This study develops a harmonised framework for comparing NASA POWER and PVGIS solar-radiation products across 18 sites representing tropical rainforest (Af), tropical monsoon (Am), tropical savanna (Aw), hot semi-arid (BSh) and hot desert (BWh) climates in Southeast Asia, South Asia and Sub-Saharan Africa. Monthly global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) for 2003-2022 were quality screened, temporally harmonised and evaluated using mean bias error, root mean square error, mean absolute percentage error, Pearson correlation and Willmott’s index of agreement. Dataset-specific irradiance inputs were propagated through a fixed-tilt PV modelling chain comprising irradiance transposition, module-temperature estimation, a five-parameter single-diode model and system-loss derating.

Keywords : Solar Irradiance; Photovoltaic Yield; NASA POWER; PVGIS; Satellite-Reanalysis Comparison; Aerosol Optical Depth; Uncertainty Propagation; Climate Classification.

References :

1. Aler, R., Valls, J. M., Galván, I. M., & Ruiz-Arias, J. A. (2021). Improving the PVGIS solar radiation model using machine-learning algorithms. Solar Energy, 220, 512–522.
2. Badescu, V. (2008). Modeling solar radiation at the Earth’s surface: Recent advances. Springer.
3. Bardossy, A., & Pegram, G. (2011). Downscaling precipitation using regression and conditional simulation. Journal of Hydrology, 397(1–2), 1–13.
4. Belkilani, K., Jribi, A., Ben Salah, M., & Abdelkarim, M. (2019). Evaluation and performance of global solar-radiation models across Algerian climatic zones. Applied Sciences, 9, 436.
5. Bennett, N. D., Croke, B. F. W., Guariso, G., et al. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40, 1–20.
6. Cano, D., Monget, J. M., Albuisson, M., Guillard, H., Regas, N., & Wald, L. (1986). A method for determining global solar radiation from meteorological satellite data. Solar Energy, 37, 31–39.
7. Dee, D. P., Uppala, S. M., Simmons, A. J., et al. (2011). The ERA-Interim reanalysis: Configuration and performance. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597.
8. De Soto, W., Klein, S. A., & Beckman, W. A. (2006). Improvement and validation of a model for photovoltaic array performance. Solar Energy, 80, 78–88.
9. Dobos, A., Gilman, P., & Kasberg, M. (2014). P50/P90 analysis for solar energy systems using the System Advisor Model. IEEE Photovoltaic Specialist Conference, 668–672.
10. Duffie, J. A., & Beckman, W. A. (2013). Solar engineering of thermal processes (4th ed.). Wiley.
11. Eglen, S. J., Marwick, B., Halchenko, Y. O., et al. (2017). Toward standard practices for sharing reproducible versions of computational research. eLife, 6, e31658.
12. Espinar, B., Ramirez, L., Drews, A., et al. (2009). Analysis of different comparison parameters applied to solar radiation data. Solar Energy, 83(7), 1194–1203.
13. European Commission Joint Research Centre. (2024). PVGIS 5.3: Release features, datasets and temporal coverage. Photovoltaic Geographical Information System documentation.
14. European Commission Joint Research Centre. (2026). Photovoltaic Geographical Information System (PVGIS): Solar-radiation and photovoltaic-performance services.
15. Farahmand, M. Z., & Yaghoubi, M. (2020). Evaluation of satellite-based solar-radiation data for Iran: A review. Renewable and Sustainable Energy Reviews, 126, 109887.
16. Gueymard, C. A. (2012). Clear-sky irradiance predictions for solar resource assessment. Solar Energy, 86(10), 2746–2761.
17. Holmgren, W. F., Hansen, C. W., & Mikofski, M. A. (2018). pvlib python: A Python package for modelling solar-energy systems. Journal of Open Source Software, 3, 884.
18. Hollmann, R., Merchant, C. J., Saunders, R., et al. (2013). The ESA climate change initiative: Satellite data records for essential climate variables. Bulletin of the American Meteorological Society, 94(10), 1541–1552.
19. Huld, T., Müller, R., & Gambardella, A. (2012). A new solar-radiation database for estimating PV performance in Europe and Africa. Solar Energy, 86, 1803–1815.
20. Ineichen, P. (2016). Validation of models that estimate solar irradiance. Solar Energy, 132, 469–476.
21. Iqbal, M. (1983). An introduction to solar radiation. Academic Press.
22. International Energy Agency. (2024). Renewables 2024: Analysis and forecasts to 2030. Paris: IEA.
23. International Renewable Energy Agency. (2023). World Energy Transitions Outlook 2023: 1.5°C pathway. Abu Dhabi: IRENA.
24. Journée, M., & Bertrand, C. (2011). Quality control of solar-radiation data within the RMIB solar-measurement network. Solar Energy, 85, 72–86.
25. Kalnay, E., Kanamitsu, M., Kistler, R., et al. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3), 437–471.
26. Kaufman, Y. J., Tanré, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419, 215–223.
27. Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263.
28. Maghami, M. R., Hizam, H., Gomes, C., Radzi, M. A., Rezadad, M. I., & Hajighorbani, S. (2016). Power loss due to soiling on solar panels: A review. Renewable and Sustainable Energy Reviews, 59, 1307–1316.
29. Müller, R., Pfeifroth, U., Träger-Chatterjee, C., Cremer, R., Trentmann, J., & Hollmann, R. (2015). Surface solar radiation data set – Heliosat (SARAH), Edition 1. CM SAF.
30. Muneer, T., Younes, S., & Kubie, J. (2004). Quality control of solar radiation data. Energy Conversion and Management.
31. NASA POWER. (2026). POWER data methodology, data sources and validation documentation. NASA Langley Research Center.
32. Omar, A. H., Winker, D. M., Kittaka, C., et al. (2009). The CALIPSO automated aerosol classification and lidar ratio selection algorithm. Journal of Atmospheric and Oceanic Technology, 26(10), 1994–2014.
33. Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226–1227.
34. Pérez, R., Ineichen, P., Seals, R., Michalsky, J., & Stewart, R. (1990). Modelling daylight availability and irradiance components from direct and global irradiance. Solar Energy, 44, 271–289.
35. Pérez, R., Ineichen, P., Moore, K., et al. (2002). A new operational model for satellite-derived irradiances. Solar Energy, 73(5), 307–317.
36. Pinker, R. T., & Laszlo, I. (1992). Modeling surface solar irradiance for satellite applications. Journal of Applied Meteorology, 31(2), 194–211.
37. Plesser, H. E. (2018). Reproducibility vs. replicability: A brief history of a confused terminology. Frontiers in Neuroinformatics, 11, 76.
38. Remund, J., Wald, L., Lefèvre, M., et al. (2003). Worldwide solar radiation database for PV applications. Solar Energy, 76(4), 577–589.
39. Rumbayan, M., Abudureyimu, A., & Nagasaka, K. (2012). Mapping solar-energy potential in Indonesia using artificial neural networks and GIS. Renewable and Sustainable Energy Reviews, 16, 1437–1449.
40. Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Computational Biology, 9(10), e1003285.
41. Sengupta, M., Gotseff, P., Stoffel, T., et al. (2018). National solar radiation database. NREL Technical Report.
42. Stackhouse, P. W., Chandler, W. S., Zhang, T., et al. (2018). NASA surface meteorology and solar energy dataset. NASA Technical Report.
43. Stodden, V., Leisch, F., & Peng, R. D. (2016). Implementing reproducible research. CRC Press.
44. Toledano, C., Wiegner, M., Groß, S., et al. (2007). Aerosol optical depth over desert regions. Atmospheric Chemistry and Physics, 7, 1101–1116.
45. Wilcox, S. (2011). National solar radiation database 1991–2010 update. NREL Technical Report.
46. Wilks, D. S. (2011). Statistical methods in the atmospheric sciences (3rd ed.). Academic Press.
47. Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194.
48. World Meteorological Organization. (2018). Guide to instruments and methods of observation (WMO-No. 8). Geneva: WMO.
49. Yang, D. (2016). Solar radiation on inclined surfaces: Corrections and benchmarks. Solar Energy, 136, 288–302.
50. Yang, D., Bright, J. M., & Hollands, K. G. T. (2014). Bias correction methods for solar radiation data. Renewable Energy, 68, 563–573.
51. Zelenka, A., Perez, R., Seals, R., & Renné, D. (1999). Effective accuracy of satellite-derived irradiance. Solar Energy, 63(3), 199–210.
52. Zhang, Y., Rossow, W. B., Stackhouse, P. W., et al. (2013). Comparison of satellite-derived surface radiation products. Journal of Climate, 26(24), 9735–9751.