@article{(Open Science Index):https://publications.waset.org/pdf/10006771,
	  title     = {Forecasting Direct Normal Irradiation at Djibouti Using Artificial Neural Network},
	  author    = {Ahmed Kayad Abdourazak and  Abderafi Souad and  Zejli Driss and  Idriss Abdoulkader Ibrahim},
	  country	= {},
	  institution	= {},
	  abstract     = {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
	    journal   = {International Journal of Energy and Power Engineering},
	  volume    = {11},
	  number    = {4},
	  year      = {2017},
	  pages     = {393 - 396},
	  ee        = {https://publications.waset.org/pdf/10006771},
	  url   	= {https://publications.waset.org/vol/124},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 124, 2017},