Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region

In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.

Authors:

• Mohsen Hayati
• Yazdan Shirvany

Keywords:

• Artificial neural networks
• Forecasting
• Multi-layer perceptron.

References:

[1] I. Moghram, S.Rahman, Analysis and evaluation of five short-term load
forecasting techniques , IEEE transaction of power system, 4(4), 1989,
pp. 1484-1491.
[2] Paras Mandal , Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa
Funabashi, A neural network based several-hours ahead electric load
forecasting using similar days approach, International Journal of
Electrical Power & Energy Systems, Volume 28, Issue 6, 2006, pp. 367-
373.
[3] Ayca Kumluca Topalli , Ismet Erkmen and Ihsan Topalli, Intelligent
short term load forecasting in Turkey, International Journal of
Electrical Power & Energy Systems, Volume 28, Issue 7, 2006, pp. 437-
447.
[4] Nahi Kandil , René Wamkeue, Maarouf Saad and Semaan Georges, An
efficient approach for short term load forecasting using artificial neural
networks, International Journal of Electrical Power & Energy
Systems, Volume 28, Issue 8, 2006, pp. 525-530.
[5] S. J. Huang, shih K.R., Short-term load forecasting via ARMA model
identification including non-gaussian process consideration, IEEE
transaction of power system, 18(2), 2003, pp. 673-679.
[6] T. Senjyu , Takara H., Uezato K., Funabasi T., One-hour-ahead load forecasting using neural network, IEEE transaction of power
system,17(1), 2002, pp: 113-118.
[7] J. W. Taylor , Buizza R. , Neural network load forecasting with weather
ensemble predictions, IEEE transaction of power system,17(3), 2002,
pp. 626-632.
[8] M. T. Hagan , Demuth, H.B., Beale, M.H., Neural Network Design,
PWS Publishing Company, Boston, Massachusetts, 1996.
[9] J. M. Zurada, Introduction to Artificial Neural Systems, West Publishing
Company, Saint Paul, Minnesota, 1992.
[10] S. Haykin, Neural networks: A comprehensive foundation (A
comprehensive book with an engineering perspective), Macmillan
Publishing, New York, 1994.
[11] S. Haykin, Neural networks, A comprehensive foundation, Prentice-
Hall, New Jersey, 1999.