Vol. 14 Núm. 2 (2025): Revista de Investigaciones
Artí­culos Originales

Análisis de la variabilidad de la radiación solar global diaria a largo plazo reconstruida en la zona circunlacustre del Titicaca

PDF
Lelia Quispe Huamán
Universidad Nacional de Juliaca
volumen 14 numero 2 2025

Publicado 2025-06-30

Palabras clave

  • Amplitud térmica,
  • modelos empíricos,
  • radiación solar global,
  • reconstrucción,
  • variabilidad,
  • zona circunlacustre
    • ...Más
    Menos

Derechos de autor 2025 Lelia Quispe Huamán

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Cómo citar

Quispe Huamán, L. (2025). Análisis de la variabilidad de la radiación solar global diaria a largo plazo reconstruida en la zona circunlacustre del Titicaca. Revista De Investigaciones, 14(2), 56-68. https://doi.org/10.26788/ri.v14i2.6446

Resumen

La radiación solar global tiene efectos beneficios y perjudiciales, por lo que es esencial conocer su comportamiento; además, medirla se considera una tarea difícil porque existe dificultad para disponer de datos diarios. Esto ha generado el desarrollo de modelos empíricos para su estimación en países emergentes. El objetivo de esta investigación fué analizar la reconstrucción de la radiación solar global en la zona circunlacustre entre 1956 y 2021. Para ello se realizó la validación de tres modelos empíricos, Bristow-Campbell, Chen y Hargreaves-Samani, usando datos medidos del piranómetro Kipp&Zonen de la estación meteorológica Puno entre 2014 y 2021, ya que no existen registros en el resto de la Región. Como resultado se obtuvo el coeficiente de variabilidad temporal porcentual de 0,690 %; 0,472 %; 0,434 %; 0,394 % y 0,400 % en las estaciones de Capachica, Moho, Yunguyo, Juli y Puno respectivamente. También se obtuvo los coeficientes de correlación de 0,922; 0,862 y 0,882, y el porcentaje de error media de la raíz cuadrática fue de 6,494 %; 8,278 % y 7,694 % respectivamente. En conclusión, el coeficiente de variabilidad temporal porcentual se encuentra por debajo del 30 %, esto indica que los valores promedios anuales son relativamente homogéneos; así mismo, el modelo Bristow-Campbell se ajusta mejor a la zona evaluada y resulta beneficioso para reconstruir una base de datos completa de radiación solar global. Finalmente, la base de datos constituye un recurso clave para investigaciones en cambio climático, energía solar y aplicaciones afines de interés científico y regional.

PDF

Referencias

  1. Adhikari, K. R., Bhattarai, B. K., & Gurung, S. (2013). Estimation of Global Solar Radiation for Four Selected Sites in Nepal Using Sunshine Hours, Temperature and Relative Humidity. Journal of Power and Energy Engineering, 01(03), 1–9. https://doi.org/10.4236/jpee.2013.13003
  2. Alsamamra, H. (2019). Estimation of Global Solar Radiation from Temperature Extremes: A Case Study of Hebron City, Palestine. Journal of Energy and Natural Resources, 8(1), 1–5. https://doi.org/10.11648/j.jenr.20190801.11
  3. Antón, M., Román, R., Sanchez-Lorenzo, A., Calbó, J., & Vaquero, J. M. (2017). Variability analysis of the reconstructed daily global solar radiation under all-sky and cloud-free conditions in Madrid during the period 1887–1950. Atmospheric Research, 191, 94–100. https://doi.org/10.1016/j.atmosres.2017.03.013
  4. Aoun, N., & Bouchouicha, K. (2017). Estimating daily global solar radiation by day of the year in Algeria. European Physical Journal Plus, 132(5), 2–12. https://doi.org/10.1140/epjp/i2017-11495-7
  5. Babar, B., Graversen, R., & Boström, T. (2019). Solar radiation estimation at high latitudes: Assessment of the CMSAF databases, ASR and ERA5. Solar Energy, 182, 397–411. https://doi.org/10.1016/j.solener.2019.02.058
  6. Bhargawa, A., & Singh, A. K. (2019). Solar irradiance, climatic indicators and climate change – An empirical analysis. Advances in Space Research, 64(1), 271–277. https://doi.org/10.1016/j.asr.2019.03.018
  7. Blal, M., Khelifi, S., Dabou, R., Sahouane, N., Slimani, A., Rouabhia, A., Ziane, A., Neçaibia, A., Bouraiou, A., & Tidjar, B. (2020). A prediction models for estimating global solar radiation and evaluation meteorological effect on solar radiation potential under several weather conditions at the surface of Adrar environment. Measurement: Journal of the International Measurement Confederation, 152, 107348. https://doi.org/10.1016/j.measurement.2019.107348
  8. Bristow, K. L., & Campbell, G. S. (1984). On the relationship between incoming solar radiation and daily maximum an minimun temperatre. Agricultural and Forest Meteorology, 13(3), 576.
  9. Calbó, J., González, J. A., & Sanchez-Lorenzo, A. (2017). Building global and diffuse solar radiation series and assessing decadal trends in Girona (NE Iberian Peninsula). Theoretical and Applied Climatology, 129(3–4), 1003–1015. https://doi.org/10.1007/s00704-016-1829-3
  10. Camayo Lapa, B. F., Condezo Hurtado, D. E., Ramos Cadillo, A. Y., Massipe Hernández, J. R., & Camayo Vivas, A. B. (2019). Estimation of global solar radiation, using extreme temperatures, applying the bristow-campbell model in the junín region. Ingeniare, 27(4), 643–651. https://doi.org/10.4067/S0718-33052019000400643
  11. Castillejo-Cuberos, A., & Escobar, R. (2020). Understanding solar resource variability: An in-depth analysis, using Chile as a case of study. Renewable and Sustainable Energy Reviews, 120. https://doi.org/10.1016/j.rser.2019.109664
  12. Chen, J. L., He, L., Yang, H., Ma, M., Chen, Q., Wu, S. J., & Xiao, Z. lin. (2019). Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China. Renewable and Sustainable Energy Reviews, 108(February), 91–111. https://doi.org/10.1016/j.rser.2019.03.033
  13. Chen, R., Ersi, K., Yang, J., Lu, S., & Zhao, W. (2004). Validation of five global radiation models with measured daily data in China. Energy Conversion and Management, 45(11–12), 1759–1769. https://doi.org/10.1016/j.enconman.2003.09.019
  14. Dejollx, C., & Iltis, A. (1991). El lago Titicaca: Síntesis del conocinliento Iimnológico actual. In ORSTOM. lnstitut Français de Recherche Scientifique pour le Développement en Cooperation.
  15. Dhimish, M., & Mather, P. (2019). Exploratory evaluation of solar radiation and ambient temperature in twenty locations distributed in United Kingdom. Urban Climate, 27, 179–192. https://doi.org/10.1016/j.uclim.2018.12.001
  16. Eladawy, M. L., Morsy, M., Korany, M. H., Abdel Basset, H., & El-Adawy, M. (2022). Spatiotemporal variations of global solar radiation: Case Study Egypt. Alexandria Engineering Journal, 61(11), 8625–8639. https://doi.org/10.1016/j.aej.2022.01.066
  17. Fan, J., Wang, X., Cai, H., Wu, L., Zhang, F., Zeng, W., & Zou, H. (2019). Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. Renewable and Sustainable Energy Reviews, 100(August 2018), 186–212. https://doi.org/10.1016/j.rser.2018.10.018
  18. Feng, Y., Chen, D., & Zhao, X. (2018). Estimated long-term variability of direct and diffuse solar radiation in North China during 1959–2016. Theoretical and Applied Climatology, 137(1–2), 153–163. https://doi.org/10.1007/s00704-018-2579-1
  19. Feng, Y., Gong, D., Jiang, S., Zhao, L., & Cui, N. (2020). National-scale development and calibration of empirical models for predicting daily global solar radiation in China. Energy Conversion and Management, 203. https://doi.org/10.1016/j.enconman.2019.112236
  20. Feng, Y., Gong, D., Zhang, Q., Jiang, S., Zhao, L., & Cui, N. (2019). Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation. Energy Conversion and Management, 198, 111780. https://doi.org/10.1016/j.enconman.2019.111780
  21. Fröhlich, C. (2006). Solar irradiance variability since 1978. Space Science Reviews, 125(1–4), 53–65. https://doi.org/10.1007/s11214-006-9046-5
  22. Gueymard, C. A., & Wilcox, S. M. (2011). Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data. Solar Energy, 85(5), 1068–1084. https://doi.org/10.1016/j.solener.2011.02.030
  23. Hargreaves, G. H., & Samani, Z. A. (1985). Reference Crop Evapotranspiration From Ambient Air Temperature. American Society of Agricultural Engineers, 1, 96–99.
  24. Jamieson, P. D., Porter, J. R., & Wilson, D. R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27(4), 337–350. https://doi.org/10.1016/0378-4290(91)90040-3
  25. Jerez, S., Tobin, I., Vautard, R., Montávez, J. P., López-Romero, J. M., Thais, F., Bartok, B., Christensen, O. B., Colette, A., Déqué, M., Nikulin, G., Kotlarski, S., Van Meijgaard, E., Teichmann, C., & Wild, M. (2015). The impact of climate change on photovoltaic power generation in Europe. Nature Communications, 6. https://doi.org/10.1038/ncomms10014
  26. Jimenez, V. A., Will, A., & Rodríguez, S. (2017). Estimación De Radiación Solar Horaria Utilizando Modelos Empíricos y Redes Neuronales Artificiales. Ciencia y Tecnología, 1(17), 29–43. https://doi.org/10.18682/cyt.v1i17.608
  27. Kariuki, B. W., & Sato, T. (2018). Interannual and spatial variability of solar radiation energy potential in Kenya using Meteosat satellite. Renewable Energy, 116, 88–96. https://doi.org/10.1016/j.renene.2017.09.069
  28. Kumar, D. (2019). Hyper-temporal variability analysis of solar insolation with respect to local seasons. Remote Sensing Applications: Society and Environment, 15. https://doi.org/10.1016/j.rsase.2019.100241
  29. Li, M. F., Tang, X. P., Wu, W., & Liu, H. Bin. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139–148. https://doi.org/10.1016/j.enconman.2013.03.004
  30. Lima, F. J. L. de, Martins, F. R., Costa, R. S., Gonçalves, A. R., dos Santos, A. P. P., & Pereira, E. B. (2019). The seasonal variability and trends for the surface solar irradiation in northeastern region of Brazil. Sustainable Energy Technologies and Assessments, 35, 335–346. https://doi.org/10.1016/j.seta.2019.08.006
  31. Lohmann, S., Schillings, C., Mayer, B., & Meyer, R. (2006). Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data. Solar Energy, 80(11), 1390–1401. https://doi.org/10.1016/j.solener.2006.03.004
  32. Madhlopa, A. (2022). Solar receivers for thermal power generation (Vol. 4, Issue 1). Charlotte Cockle.
  33. Maleki, S. A. M., Hizam, H., & Gomes, C. (2017). Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: Models re-visited. Energies, 10(1), 1–28. https://doi.org/10.3390/en10010134
  34. Manara, V., Brunetti, M., Celozzi, A., Maugeri, M., Sanchez-Lorenzo, A., & Wild, M. (2016). Detection of dimming/brightening in Italy from homogenized all-sky and clear-sky surface solar radiation records and underlying causes (1959-2013). Atmospheric Chemistry and Physics, 16(17), 11145–11161. https://doi.org/10.5194/acp-16-11145-2016
  35. Martínez Bencardino, C. (2012). Estadística y Muestreo (ECOE ediciones, Ed.; 3° ed.).
  36. Ordoñez-Palacios, L. E., León-Vargas, D. A., Bucheli-Guerrero, V. A., & Ordoñez-Eraso, H. A. (2020). Solar Radiation Prediction on Photovoltaic Systems Using Machine Learning Techniques. Revista Facultad de Ingenieria, 29(54), 11751. https://doi.org/10.19053/01211129.v29.n54.2020.11751
  37. Peñalva, J. J., Lozano, D. A., Murillo, J. C., & Ortega, F. M. (2022). Global solar radiation time series forecasting using different architectures of the multilayer perceptron model. Journal of Physics: Conference Series, 2180(1). https://doi.org/10.1088/1742-6596/2180/1/012017
  38. Perez, R., David, M., Hoff, T. E., Kleissl, J., & Perez, M. (2016). Spatial and Temporal Variability of Solar Energy. Renewable Energy, 1(1), 1–44. https://doi.org/10.1561/2700000006
  39. Pfeifroth, U., Sanchez-Lorenzo, A., Manara, V., Trentmann, J., & Hollmann, R. (2018). Trends and Variability of Surface Solar Radiation in Europe Based On Surface- and Satellite-Based Data Records. Journal of Geophysical Research: Atmospheres, 123(3), 1735–1754. https://doi.org/10.1002/2017JD027418
  40. Prieto, J. I., & García, D. (2022). Global solar radiation models: A critical review from the point of view of homogeneity and case study. Renewable and Sustainable Energy Reviews, 155, 111856. https://doi.org/10.1016/j.rser.2021.111856
  41. Rodríguez Benítez, F. J., Arbizu Barrena, C., Santos Alamillos, F. J., Tovar Pescador, J., & Pozo Vázquez, D. (2018). Analysis of the intra-day solar resource variability in the Iberian Peninsula. Solar Energy, 171, 374–387. https://doi.org/10.1016/j.solener.2018.06.060
  42. Roelandts, R. (2020). Solar radiation. In Advanced Remote Sensing, (Second Edi, Vol. 134, Issue 5 C2, pp. 157–191). https://doi.org/10.1016/S0151-9638(07)89237-4
  43. Saffaripour, M. H., Mehrabian, M. A., & Bazargan, H. (2013). Predicting solar radiation fluxes for solar energy system applications. International Journal of Environmental Science and Technology, 10(4), 761–768. https://doi.org/10.1007/s13762-013-0179-2
  44. Samadianfard, S., Majnooni-Heris, A., Qasem, S. N., Kisi, O., Shamshirband, S., & Chau, K. wing. (2019). Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Engineering Applications of Computational Fluid Mechanics, 13(1), 142–157. https://doi.org/10.1080/19942060.2018.1560364
  45. Servicio Nacional de Meteorología e Hidrología (SENAMHI). (2003). Atlas de Energia Solar del Perú (p. 72).
  46. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). Earth’s global energy budget. Bulletin of the American Meteorological Society, 90(3), 311–323. https://doi.org/10.1175/2008BAMS2634.1
  47. Viswanathan, B. (Balasubramanian). (2016). Solar Energy: Fundamentals. In Energy Sources (p. 502). Elsevier.
  48. Wild, M., Ohmura, A., Schär, C., Müller, G., Folini, D., Schwarz, M., Hakuba, M. Z., & Sanchez-Lorenzo, A. (2017). The Global Energy Balance Archive (GEBA) version 2017: A database for worldwide measured surface energy fluxes. Earth System Science Data Discussions, 1–24. https://doi.org/10.5194/essd-2017-28
  49. Woli, P., & Paz, J. O. (2012). Evaluation of various methods for estimating global solar radiation in the southeastern United States. Journal of Applied Meteorology and Climatology, 51(5), 972–985. https://doi.org/10.1175/JAMC-D-11-0141.1
  50. Yang, L., Jiang, J., Liu, T., Li, Y., Zhou, Y., & Gao, X. (2018). Projections of future changes in solar radiation in China based on CMIP5 climate models. Global Energy Interconnection, 1(4), 452–459. https://doi.org/10.14171/j.2096-5117.gei.2018.04.005