Published March 1, 2017
| Version 10006771
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Forecasting Direct Normal Irradiation at Djibouti Using Artificial Neural Network
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In this paper Artificial Neural Network (ANN) is used
to predict the solar irradiation in Djibouti for the first Time that
is useful to the integration of Concentrating Solar Power (CSP)
and sites selections for new or future solar plants as part of solar
energy development. An ANN algorithm was developed to establish
a forward/reverse correspondence between the latitude, longitude,
altitude and monthly solar irradiation. For this purpose the German
Aerospace Centre (DLR) data of eight Djibouti sites were used as
training and testing in a standard three layers network with the back
propagation algorithm of Lavenber-Marquardt. Results have shown a
very good agreement for the solar irradiation prediction in Djibouti
and proves that the proposed approach can be well used as an
efficient tool for prediction of solar irradiation by providing so helpful
information concerning sites selection, design and planning of solar
plants.
to predict the solar irradiation in Djibouti for the first Time that
is useful to the integration of Concentrating Solar Power (CSP)
and sites selections for new or future solar plants as part of solar
energy development. An ANN algorithm was developed to establish
a forward/reverse correspondence between the latitude, longitude,
altitude and monthly solar irradiation. For this purpose the German
Aerospace Centre (DLR) data of eight Djibouti sites were used as
training and testing in a standard three layers network with the back
propagation algorithm of Lavenber-Marquardt. Results have shown a
very good agreement for the solar irradiation prediction in Djibouti
and proves that the proposed approach can be well used as an
efficient tool for prediction of solar irradiation by providing so helpful
information concerning sites selection, design and planning of solar
plants.
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References
- Gauri, S., Safiatou, A. N. and Mohamed, Y. S. "Renewable Readiness Assessement: Djibouti", International Renewable Energy Agency, IRENA (2015).
- Abdourazak, A. K., Abderafi, S., Zejli, D. and Ibrahim, I. A. "Potentialities of using linear Fresnel technology for solar energy development in Djibouti", In Renewable and Sustainable Energy Conference. IRSEC 2014, IEEE International, 2014, pp. 672-675, IEEE.
- Koca, A., Oztop, H. F., Varol, Y. and Koca, G. O. "Estimation of solar irradiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey", Expert Systems with Applications, 38, 2011, pp. 8756-8762.
- Fadare, D. A. "Modelling of solar energy potential in Nigeria using an artificial neural network model", Applied Energy, 86, 2009, pp. 1410-1422.
- Dorvloa, A. S. S., Jervase, J. A. and Al-Lawati, A. "Solar irradiation estimation using artificial neural networks", Applied Energy, 71, 2002, pp. 307-319.
- Amit, K. Y. and Chandel, S. S. "Solar radiation prediction using Artificial Neural Network techniques: A review", Renewable and Sustainable Energy Reviews, 33, 2014, pp. 772-781.
- Md. Shafiqul, I., Md. Monirul, K. and Nafis, K. "Artificial Neural Networks based Prediction of Insolation on Horizontal Surfaces for Bangladesh", Procedia Technology, 10, 2013, pp. 482-491.
- Ermis, K., Midilli, A., Dincer, I. and Rosen, M. A. "Artificial neural network analysis of world green energy use", Energy Policy, 35, 2007, pp. 1731-1743.
- GPS Coordinates and Google Map, "coordonnees-gps.fr/". Accessed on 12/2015. [10] Solar and Wind Energy Resource Assessment (SWERA), DNI DLR (Germain Aerospace Centre) high Resolution, "maps.nrel.gov/swera". Accessed on 12/2015.[11] Ouammi, A., Zejli, D., Dagdougui, H. and Benchrifa, R. "Artificial neural network analysis of Moroccan solar potential", Renewable and Sustainable Energy Reviews, 16(7), 2012, pp. 4876-4889. [12] Mubiri, J. "Predicting total solar irradiation values using artificial neural networks", Renewable Energy, 33(10), 2008, pp. 2329-2332. [13] Ouammi, A., Sacile, R., Zejli, D., Mimet, A. and Benchrifa, R. "Sustainability of a wind power plant: application to different Moroccan sites", Energy, 35, 2010, pp. 4226-4236. [14] Adnan S., Erol A. and Mehmet O. "Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data", Energy Conversion and Management, 45(18-19), 2004, pp. 3033-3052. [15] Fernando R. M., Enio B. P. and Ricardo A. G. "Solar radiation forecastion using Artificial Neural Networks", International Journal of Energy Science, 2(6), 2012, pp. 217-227.