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Investigation of One Day Ahead Load Forecasting for Iraqi Power System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 1
Year of Publication: 2017
Authors:
Mohammed Abdulla Abdulsada, Mohanad Azeez Joodi, Firas M. Tuaimah
10.5120/ijca2017913450

Mohammed Abdulla Abdulsada, Mohanad Azeez Joodi and Firas M Tuaimah. Investigation of One Day Ahead Load Forecasting for Iraqi Power System. International Journal of Computer Applications 163(1):24-29, April 2017. BibTeX

@article{10.5120/ijca2017913450,
	author = {Mohammed Abdulla Abdulsada and Mohanad Azeez Joodi and Firas M. Tuaimah},
	title = {Investigation of One Day Ahead Load Forecasting for Iraqi Power System},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {1},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {24-29},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume163/number1/27360-2017913450},
	doi = {10.5120/ijca2017913450},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Power stations must supply the electrical load demands to achieve optimal power system operation. To meet the future load, the power system dispatcher use load forecasting techniques to schedule unit generation resources. In this paper the short term load forecasting (STLF) using feed forward Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) techniques for Iraqi power system (IPS) is presented. The ANN and MLR techniques are used to forecast one day ahead load for summer and winter season. The ANN gives a very small mean absolute percentage error (MAPE) compared with MLR but it takes a longer time for training process.

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Keywords

Short Term Load Forecasting, Artificial Neural Network, Multiple Linear Regression, Mean Absolute Percentage Error.