Volume 2 Number 2 (Apr. 2010)
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IJCEE 2010 Vol.2 (2): 334-338 ISSN: 1793-8163
DOI: 10.7763/IJCEE.2010.V2.157

The Comparision of Mid Term Load Forecasting between Multi-Regional and Whole Country Area Using Artificial Neural Network

P. Bunnoon, K. Chalermyanont and C. Limsakul

Abstract—Load forecasting is very important for operation of electricity companies such as for operation, unit commitment, and planning. This research presents a comparison of mid term load forecasting between multiregional area model (6 neural network models of north, northeast, centre, east, south-east, south-west areas in Thailand) with the factors based on regional area and the whole country area with the factors based on whole country area. The data information composes of the peak load, energy consumption, humidity, rainfall, wind speed, consumer price index, and industrial index recorded from year 1997 to 2007 which are given from many resources in the country. This study shows the results in energy consumption demand forecasting and peak load demand forecasting, case study in Electricity Generating Authority of Thailand (EGAT). Artificial Neural network(ANN) is used with have feed forward back propagation algorithm and LM algorithm.. The experimental results show that the multi-regional area forecasting model can reduce the error and improve the forecast accuracy effectively more than that of the whole country area forecasting model in mid term load forecast.

Index Terms—Peak load; Energy consumption; Neural network; Forecasting; Multi regional; Whole country.

P. Bunnoon is with Department of Electrical Engineering, Prince of Songkla University,Thailand (e-mail:add2002k@hotmail.com)
Ph.D.Prog.K. Chalermyanont is with Department of Electrical Engineering, Prince of Songkla University, Thailand (e-mail: kusumal.c@psu.ac.th).
C. Limsakul is with Department of Electrical Engineering, Prince of Songkla University, Thailand (e-mail: chusak.l@psu.ac.th).

Cite: P. Bunnoon, K. Chalermyanont and C. Limsakul, "The Comparision of Mid Term Load Forecasting between Multi-Regional and Whole Country Area Using Artificial Neural Network," International Journal of Computer  and
Electrical Engineering
vol. 2, no. 2, pp. 334-338, 2010.

General Information

ISSN: 1793-8163 (Print)
Abbreviated Title: Int. J. Comput. Electr. Eng.
Frequency: Quarterly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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