@article{(Open Science Index):https://publications.waset.org/pdf/9998947,
	  title     = {Two Day Ahead Short Term Load Forecasting Neural Network Based },
	  author    = {Firas M. Tuaimah},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.

The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.
},
	    journal   = {International Journal of Energy and Power Engineering},
	  volume    = {8},
	  number    = {7},
	  year      = {2014},
	  pages     = {1151 - 1155},
	  ee        = {https://publications.waset.org/pdf/9998947},
	  url   	= {https://publications.waset.org/vol/91},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 91, 2014},
	}