Short term load forecasting using a hybrid neural network

Yap, Keem Siah, Abidin, Izham Zainal, Lim, Chee Peng and Shah, Mohd Suhairi 2006, Short term load forecasting using a hybrid neural network, in PECon 2006 : Proceedings of the First International Power and Energy Conference, [The Conference], [Putrajaya, Malaysia], pp. 123-128.

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Title Short term load forecasting using a hybrid neural network
Author(s) Yap, Keem Siah
Abidin, Izham Zainal
Lim, Chee PengORCID iD for Lim, Chee Peng
Shah, Mohd Suhairi
Conference name Power and Energy. Conference (1st : 2006 : Putrajaya, Malaysia)
Conference location Putrajaya, Malaysia
Conference dates 28-29 Nov. 2006
Title of proceedings PECon 2006 : Proceedings of the First International Power and Energy Conference
Editor(s) [Unknown]
Publication date 2006
Conference series Power and Energy Conference
Start page 123
End page 128
Total pages 6
Publisher [The Conference]
Place of publication [Putrajaya, Malaysia]
Keyword(s) Gaussian adaptive resonance theory
Generalized regression neural network
Load forecasting
Time series prediction
Summary Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly 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 STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.
ISBN 1424402735
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
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