@article{(Open Science Index):https://publications.waset.org/pdf/8451,
	  title     = {Application of Neural Networks for 24-Hour-Ahead Load Forecasting},
	  author    = {Fatemeh Mosalman Yazdi},
	  country	= {},
	  institution	= {},
	  abstract     = {One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
short term load forecasting. This paper presents the development of
an artificial neural network based short-term load forecasting model.
The model can forecast daily load profiles with a load time of one
day for next 24 hours. In this method can divide days of year with
using average temperature. Groups make according linearity rate of
curve. Ultimate forecast for each group obtain with considering
weekday and weekend. This paper investigates effects of temperature
and humidity on consuming curve. For forecasting load curve of
holidays at first forecast pick and valley and then the neural network
forecast is re-shaped with the new data. The ANN-based load models
are trained using hourly historical. Load data and daily historical
max/min temperature and humidity data. The results of testing the
system on data from Yazd utility are reported.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {3},
	  number    = {2},
	  year      = {2009},
	  pages     = {248 - 251},
	  ee        = {https://publications.waset.org/pdf/8451},
	  url   	= {https://publications.waset.org/vol/26},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 26, 2009},
	}