The Taguchi-neural networks approach to forecast electricity consumption

Author

Purwanto, Dedy ; Agustiawan, Herman ; Romlie, Mohd Fakhizan

Author_Institution

Dept. of Electr. & Electron. Eng., Univ. Teknol. Petronas, Tronoh

fYear

2008

fDate

4-7 May 2008

Abstract

Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. ldquoTrial and errorrdquo commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed to optimize the accuracy of NN based prediction. This hybrid approach results in the optimal network parameters. Those are: 1 for the history length, 1 day for sampling time, and 8 nodes for hidden neurons. The method is used to predict electricity consumption in Universiti Teknologi PETRONAS (UTP), Malaysia. From the preliminary results it is found that the combined method seems to be a convincing approach.

Keywords

Taguchi methods; load forecasting; neural nets; optimisation; power consumption; power engineering computing; Taguchi-neural network; electricity consumption forecasting; optimal network parameter; Bayesian methods; Chaos; Energy consumption; History; Load forecasting; Neural networks; Neurons; Optimization methods; Sampling methods; Testing; Taguchi’s method; history length; sampling time;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on

Conference_Location

Niagara Falls, ON

ISSN

0840-7789

Print_ISBN

978-1-4244-1642-4

Electronic_ISBN

0840-7789

Type

conf

DOI

10.1109/CCECE.2008.4564882

Filename

4564882