Angström-Prescott empirical model to estimate solar radiation in Norte de Santander, Colombia
Abstract
The document shows the application of the empirical Angström-Prescott model in different places in Norte de Santander, Colombia. The model estimates solar radiation from hours of sunlight, at a site where brightness and solar radiation are measured. The data were obtained from the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM; algorithms were developed in RStudio to process and analyze the information. The model establishes a linear relationship between solar radiation and the hours of sunlight, in a specific geographic location. Therefore, regression analyzes were performed for three different sites, using historical records of brightness and solar radiation, obtaining the R-squared coefficients of: 0.73, 0.78, and 0.42. The models were then extrapolated to nearby regions with solar brightness records, but without solar radiation data, to obtain an estimate of radiation at these locations. Finally, a database was created with monthly average information on solar radiation for various subregions of Norte de Santander, which can be used for the design and implementation of photovoltaic systems.
Keywords
solar radiation;, Angström-Prescott equation;, empirical model;, solar brightness
Author Biography
Wilmer Contreras-Sepúlveda
Ingeniero Electrónico
Migan Giuseppe Galban-Pineda
Ingeniero Electrónico
Luis Fernando Bustos-Márquez
Ingeniero Electrónico, Especialista en Práctica Pedagógica Universitaria
Sergio Basilio Sepúlveda-Mora
Ingeniero Electrónico, Master of Science in Electrical and Computer Engineering
Jhon Jairo Ramírez-Mateus
Ingeniero Electrónico
References
Akinoǧlu, B. G., & Ecevit, A. (1990). A further comparison and discussion of sunshine-based models to estimate global solar radiation. Energy, 15 (10), 865–872. https://doi.org/10.1016/0360-5442(90)90068-D
Almorox, J., Benito, M., & Hontoria, C. (2005). Estimation of monthly Angström-Prescott equation coefficients from measured daily data in Toledo, Spain. Renewable Energy, 30 (6), 931–936. https://doi.org/10.1016/j.renene.2004.08.002
Asilevi, P. J., Quansah, E., Amekudzi, L. K., Annor, T., & Klutse, N. A. B. (2019). Modeling the spatial distribution of Global Solar Radiation (GSR) over Ghana using the Ångström-Prescott sunshine duration model. Scientific African, 4, e00094. https://doi.org/10.1016/j.sciaf.2019.e00094
Basaran, K., Özçift, A., & Kılınç, D. (2019). A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm. Arabian Journal for Science and Engineering, 44 (8), 7159–7171. https://doi.org/10.1007/s13369-019-03841-7
Chelbi, M., Gagnon, Y., & Waewsak, J. (2015). Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia. Energy Conversion and Management, 101, 203–215. https://doi.org/10.1016/j.enconman.2015.04.052
Dumas, A., Andrisani, A., Bonnici, M., Graditi, G., Leanza, G., Madonia, M., & Trancossi, M. (2015). A new correlation between global solar energy radiation and daily temperature variations. Solar Energy, 116, 117–124. https://doi.org/10.1016/j.solener.2015.04.002
Hough, T. P. (Ed.). (2007). Recent developments in solar energy. Nova Science Publishers, Inc.
Hulstrom, R. L. (Ed.). (1989). Solar Resources. The MIT press.
Jamil, B., & Bellos, E. (2019). Development of empirical models for estimation of global solar radiation exergy in India. Journal of Cleaner Production, 207, 1–16. https://doi.org/10.1016/j.jclepro.2018.09.246
Luna-Carlosama, C., Jiménez-García, F., Moreno-Chuquen, R., & Mulcué-Nieto, L. (2020). Potencial de irradiación solar para generar electricidad en el departamento del Putumayo de Colombia. Revista UIS Ingenierías, 19 (3), 153–161. https://doi.org/10.18273/revuin.v19n3-2020015
Maechler, M., Stahel, W., Ruckstuhl, A., Keller, C., Halvorsen, K., Houser, A., Buser, C., Gygax, L., Venables, B., Plate, T., Flckiger, I., Wolbers, M., Keller, M., & Dudoit, S. (2016). sfsmisc: Utilities from “Seminar fuer Statistik” ETH Zurich R package version 1.1-0. doi: https://cran.r-project.org/package=sfsmisc
Mirai Solutions GmbH. (2017). XLConnect: Excel Connector for R (R package version 0.2-13).
Noriega-Angarita, E., Sousa-Santos, V., Quintero-Duran, M., & Gil-Arrieta, C. (2016). Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks. International Journal of Engineering and Technology, 8 (4), 1817–1825. https://doi.org/10.21817/ijet/2016/v8i4/160804234
Paulescu, M., Stefu, N., Calinoiu, D., Paulescu, E., Pop, N., Boata, R., & Mares, O. (2016). Ångström-Prescott equation: Physical basis, empirical models and sensitivity analysis. Renewable and Sustainable Energy Reviews, 62, 495–506. https://doi.org/10.1016/j.rser.2016.04.012
Quej, V. H., Almorox, J., Ibrakhimov, M., & Saito, L. (2016). Empirical models for estimating daily global solar radiation in Yucatán Peninsula, Mexico. Energy Conversion and Management, 110, 448–456. https://doi.org/10.1016/j.enconman.2015.12.050
Smets, A., Jäger, K., Isabella, O., VanSwaaij, R., & Zeman, M. (2016). Solar Energy: The Physics and Engineering of Photovoltaic Conversion, Technologies and Systems 1st ed. UIT Cambridge Ltd.
Unidad de Planeación Minero Energética, UPME. (2015). Integración de las energías renovables no convencionales en Colombia. Recuperado de: http://www.upme.gov.co/Estudios/2015/Integracion_Energias_Renovables/INTEGRACION_ENERGIAS_RENOVANLES_WEB.pdf
Urrego-Ortiz, J., Martínez, J. A., Arias, P. A., & Jaramillo-Duque, Á. (2019). Assessment and day-ahead forecasting of hourly solar radiation in Medellín, Colombia. Energies, 12 (22), 4402. https://doi.org/10.3390/en12224402
Vélez-Pereira, A. M., Vergara-Vásquez, E. L., Barraza-Coronell, W. D., & Agudelo-Yepes, D. C. (2015). Evaluación de un modelo estadístico para estimar la radiación solar en Magdalena, Colombia. TecnoLógicas, 18 (35), 35–44. https://doi.org/10.22430/22565337.196
Wang, J., Wang, E., Yin, H., Feng, L., & Zhao, Y. (2015). Differences between observed and calculated solar radiations and their impact on simulated crop yields. Field Crops Research, 176, 1–10. https://doi.org/10.1016/j.fcr.2015.02.014
Yaniktepe, B., & Genc, Y. A. (2015). Establishing new model for predicting the global solar radiation on horizontal surface. International Journal of Hydrogen Energy, 40 (44), 15278–15283. https://doi.org/10.1016/j.ijhydene.2015.02.064
Zeileis, A., & Grothendieck, G. (2005). zoo: S3 Infrastructure for Regular and Irregular Time Series. Journal of Statistical Software, 14 (6), 1–27. http://arxiv.org/abs/math/0505527