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Short term power load forecasting using a modified generalized regression neural network

conference contribution
posted on 2008-12-01, 00:00 authored by K S Yap, Chee Peng LimChee Peng Lim
Short Term Load Forecasting is very important from the power systems grid operation point of view. The short term time frame may consist of half hourly prediction up to monthly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the short term load forecasting using a Modified Generalized Regression Neural Network (MGRNN). The experiments are based on the power load data from Jan 1997 to Jan 1999 of East Slovakian Electricity Corporation. Simulation results show that MGRNN has comparable prediction accuracy compared to benchmark result archived by Support Vector Regression.

History

Pagination

180 - 184

Publisher

ACTA Press

Location

Langkawi, Malaysia

Place of publication

Zurich, Switzerland

Start date

2008-04-02

End date

2008-04-04

ISBN-13

9780889867321

Publication classification

EN.1 Other conference paper

Title of proceedings

Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008

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