Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks
Date
2016
2016
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Abstract
This paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality "El Carmen de Bolivar" located in this region. To obtain the model, the average data of daily temperature, relative humidity and solar radiation from the last ten years, reported by weather stations in this city were used. Six neural networks were designed with six variants of input variables (temperature, humidity and month) and the output variable (solar radiation). The best result was obtained using all input variables. In the training process, the correlation index (R) between solar radiation estimated by the model and the recorded data was 0.8. In validating the correlation index was 0.77.
Referencias:
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[1] M. Van der Hoeven, World Energy Outlook 2014. Paris, 2014. [2] A. Vélez-Pereira, E. Vásquez, W. Coronell, and Et.al, “Determinación de un modelo paramétrico para estimar la radiación solar,” Ingenium, vol. 7, no. 18, pp. 11–17, 2013. [3] A. Angstrom, “Solar and terrestrial radiation,” J. R. Meteorol. Soc., vol. 50, no. 210, pp. 121–126, 1924. [4] H. Li, Y. Lian, X. Wang, W. Ma, and L. Zhao, “Solar constant values for estimating solar radiation,” Energy, vol. 36, no. 3, pp. 1785– 1789, Mar. 2011. [5] M. Yorukoglu and A. N. Celik, “A critical review on the estimation of daily global solar radiation from sunshine duration,” Energy Convers. Manag., vol. 47, no. 15–16, pp. 2441–2450, Sep. 2006. [6] H. Li, W. Ma, Y. Lian, and X. Wang, “Estimating daily global solar radiation by day of year in China,” Appl. Energy, vol. 87, no. 10, pp. 3011–3017, Oct. 2010. [7] Z. Jin, W. Yezheng, and Y. Gang, “General formula for estimation of monthly average daily global solar radiation in China,” Energy Convers. Manag., vol. 46, no. 2, pp. 257–268, Jan. 2005. [8] J. R. Trapero, N. Kourentzes, and A. Martin, “Short-term solar irradiation forecasting based on Dynamic Harmonic Regression,” Energy, vol. 84, pp. 289–295, May 2015. [9] M. El-Metwally, “Simple new methods to estimate global solar radiation based on meteorological data in Egypt,” Atmos. Res., vol. 69, no. 3–4, pp. 217–239, Jan. 2004. [10] V. Badescu, C. A. Gueymard, S. Cheval, C. Oprea, M. Baciu, A. Dumitrescu, F. Iacobescu, I. Milos, and C. Rada, “Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models,” Renew. Sustain. Energy Rev., vol. 16, no. 3, pp. 1636–1656, Apr. 2012. [11] J. Almorox, C. Hontoria, and M. Benito, “Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain),” Appl. Energy, vol. 88, no. 5, pp. 1703–1709, May 2011. [12] G. Winter Althaus, B. Gonzalez Landin, A. Pulido Alonso, B. Galvan Gonzalez, M. Maarouf, J. Gonzalez Guerra, and M. Cruz Perez, “Predicción de la demanda de la energía eléctrica a largo plazo: un reto en ingeniería computacional,” Dyna Ing. e Ind., vol. 90, no. 3, pp. 582–584, 2015. [13] J. Garcia Martin, “Clasificación del tratamiento térmico de aceros con ensayos no destructivos por corrientes inducidas mediante redes neuronales,” Dyna Ing. e Ind., vol. 89, no. 3, pp. 526–532, 2014. [14] A. K. Yadav, H. Malik, and S. S. Chandel, “Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models,” Renew. Sustain. Energy Rev., vol. 31, pp. 509–519, Mar. 2014. [15] P. L. Zervas, H. Sarimveis, J. A. Palyvos, and N. C. G. Markatos, “Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques,” Renew. Energy, vol. 33, no. 8, pp. 1796–1803, Aug. 2008. [16] S. Rehman and M. Mohandes, “Artificial neural network estimation of global solar radiation using air temperature and relative humidity,” Energy Policy, vol. 36, no. 2, pp. 571–576, Feb. 2008. [17] Y. Jiang, “Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models,” Energy Policy, vol. 36, no. 10, pp. 3833–3837, Oct. 2008. [18] N. D. Kaushika, R. K. Tomar, and S. C. Kaushik, “Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations,” Sol. Energy, vol. 103, pp. 327–342, May 2014. [19] M. Benghanem, A. Mellit, and S. N. Alamri, “ANN-based modelling and estimation of daily global solar radiation data: A case study,” Energy Convers. Manag., vol. 50, no. 7, pp. 1644–1655, Jul. 2009. [20] S. Sayago, M. Bocco, G. Ovando, and Et.al, “Radiación solar horaria: modelos de estimación a partir de variables meteorológicas básicas,” Av. en Energías Renov. y Medio Ambient., vol. 15, no. 1, pp. 51–57, 2011. [21] J. Bonilla, J. Ramírez, and O. Ramírez, “Metodología para el diseño de un modelo univariado de red neuronal para el pronóstico de la temperatura mínima en la zona de Mosquera (Cundinamarca, Colombia),” Meteorol. Colomb., vol. 1, no. 10, pp. 111–120, 2006. [22] N. Obregón, F. Monsalve, and S. Garavito, “Predicción de series de tiempo hídricas mediante modelos de redes neuronales artificiales y modelos de árboles de decisión,” in Memorias del Seminario Internacional La Hidroinformática en la Gestión Integrada de los Recursos Hídricos, 2003. [23] A. González-Rodríguez, “Modelo para la predicción de la radiación solar a partir de redes neuronales artificiales,” Escuela de Ingeniería de Antioquia, 2014. [24] J. R. G. Sarduy, K. G. Di Santo, and M. A. Saidel, “Linear and non-linear methods for prediction of peak load at University of São Paulo,” Measurement, vol. 78, pp. 187–201, Jan. 2016.
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