{"references": ["Gauri, S., Safiatou, A. N. and Mohamed, Y. S. \"Renewable Readiness\nAssessement: Djibouti\", International Renewable Energy Agency, IRENA\n(2015).", "Abdourazak, A. K., Abderafi, S., Zejli, D. and Ibrahim, I. A.\n\"Potentialities of using linear Fresnel technology for solar energy\ndevelopment in Djibouti\", In Renewable and Sustainable Energy\nConference. IRSEC 2014, IEEE International, 2014, pp. 672-675, IEEE.", "Koca, A., Oztop, H. F., Varol, Y. and Koca, G. O. \"Estimation of solar\nirradiation using artificial neural networks with different input parameters\nfor Mediterranean region of Anatolia in Turkey\", Expert Systems with\nApplications, 38, 2011, pp. 8756-8762.", "Fadare, D. A. \"Modelling of solar energy potential in Nigeria using\nan artificial neural network model\", Applied Energy, 86, 2009, pp.\n1410-1422.", "Dorvloa, A. S. S., Jervase, J. A. and Al-Lawati, A. \"Solar irradiation\nestimation using artificial neural networks\", Applied Energy, 71, 2002,\npp. 307-319.", "Amit, K. Y. and Chandel, S. S. \"Solar radiation prediction using Artificial\nNeural Network techniques: A review\", Renewable and Sustainable\nEnergy Reviews, 33, 2014, pp. 772-781.", "Md. Shafiqul, I., Md. Monirul, K. and Nafis, K. \"Artificial Neural\nNetworks based Prediction of Insolation on Horizontal Surfaces for\nBangladesh\", Procedia Technology, 10, 2013, pp. 482-491.", "Ermis, K., Midilli, A., Dincer, I. and Rosen, M. A. \"Artificial neural\nnetwork analysis of world green energy use\", Energy Policy, 35, 2007,\npp. 1731-1743.", "GPS Coordinates and Google Map, \"coordonnees-gps.fr/\". Accessed on\n12/2015.\n[10] Solar and Wind Energy Resource Assessment (SWERA), DNI DLR\n(Germain Aerospace Centre) high Resolution, \"maps.nrel.gov/swera\".\nAccessed on 12/2015.[11] Ouammi, A., Zejli, D., Dagdougui, H. and Benchrifa, R. \"Artificial\nneural network analysis of Moroccan solar potential\", Renewable and\nSustainable Energy Reviews, 16(7), 2012, pp. 4876-4889.\n[12] Mubiri, J. \"Predicting total solar irradiation values using artificial neural\nnetworks\", Renewable Energy, 33(10), 2008, pp. 2329-2332.\n[13] Ouammi, A., Sacile, R., Zejli, D., Mimet, A. and Benchrifa, R.\n\"Sustainability of a wind power plant: application to different Moroccan\nsites\", Energy, 35, 2010, pp. 4226-4236.\n[14] Adnan S., Erol A. and Mehmet O. \"Estimation of solar potential\nin Turkey by artificial neural networks using meteorological and\ngeographical data\", Energy Conversion and Management, 45(18-19),\n2004, pp. 3033-3052.\n[15] Fernando R. M., Enio B. P. and Ricardo A. G. \"Solar radiation\nforecastion using Artificial Neural Networks\", International Journal of\nEnergy Science, 2(6), 2012, pp. 217-227."]}

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.