Forecasting Direct Normal Irradiation at Djibouti Using Artificial Neural Network

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.

Authors:

• Ahmed Kayad Abdourazak
• Abderafi Souad
• Zejli Driss
• Idriss Abdoulkader Ibrahim

Keywords:

• Artificial neural network
• solar irradiation
• concentrated solar power
• Lavenberg-Marquardt.

References:

[1] Gauri, S., Safiatou, A. N. and Mohamed, Y. S. “Renewable Readiness
Assessement: Djibouti”, International Renewable Energy Agency, IRENA
(2015).
[2] 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.
[3] 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.
[4] Fadare, D. A. “Modelling of solar energy potential in Nigeria using
an artificial neural network model”, Applied Energy, 86, 2009, pp.
1410-1422.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.