relation: https://khub.utp.edu.my/scholars/439/
title: The Taguchi-neural networks approach to forecast electricity consumption
creator: Purwanto, D.
creator: Agustiawan, H.
creator: Romlie, M.F.
description: 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. "Trial and error" 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. © 2008 IEEE.
date: 2008
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Purwanto, D. and Agustiawan, H. and Romlie, M.F.  (2008) The Taguchi-neural networks approach to forecast electricity consumption.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-51849155634&doi=10.1109%2fCCECE.2008.4564882&partnerID=40&md5=19f88e00301bcef95ee666f9c085958b
relation: 10.1109/CCECE.2008.4564882
identifier: 10.1109/CCECE.2008.4564882