eprintid: 522
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/05/22
datestamp: 2023-11-09 15:48:39
lastmod: 2023-11-09 15:48:39
status_changed: 2023-11-09 15:22:40
type: conference_item
metadata_visibility: show
creators_name: Awaludin, I.
creators_name: Ibrahim, R.
creators_name: Rao, K.S.R.
title: Conventional ARX and artificial neural networks ARX models for prediction of oil consumption in Malaysia
ispublished: pub
keywords: Artificial Neural Network; ARX model; Autoregressive exogenous models; Conventional models; Data sets; Energy outlook; Least square methods; Malaysia; Oil consumption; Per capita; Root mean square errors; Training dataset; Unknown parameters; Updating strategy, Industrial electronics; Least squares approximations; Observability; Population statistics, Neural networks
note: cited By 8; Conference of 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 ; Conference Date: 4 October 2009 Through 6 October 2009; Conference Code:79286
abstract: This study investigates prediction of oil consumption in Malaysia. Models of oil consumption are developed and validated with respect to training and validation dataset. Available data for Malaysia is annual data from 1982 to 2006 comprises Population, GDP per Capita, and Oil Consumption data. Prediction time target is year 2020 which is commonly used by several energy outlook reports. Two models are developed in this study, conventional Autoregressive Exogenous (ARX) model and Artificial Neural Network ARX (ANN ARX) model. The difference lies on how those models work to find unknown parameters based on training dataset. Conventional model uses Least Square method to calculate the unknown parameter where ANN ARX model uses weight updating strategy to find the unknown parameter. Performance of each model is measured through Root Mean Square Error (RMSE) value. It is shown that ANN ARX model can perform better than conventional ARX especially with small number of training dataset. © 2009 IEEE.
date: 2009
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-76449102457&doi=10.1109%2fISIEA.2009.5356496&partnerID=40&md5=39d9b76cb2b9b8ac87f77d8e9772783a
id_number: 10.1109/ISIEA.2009.5356496
full_text_status: none
publication: 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings
volume: 1
place_of_pub: Kuala Lumpur
pagerange: 23-28
refereed: TRUE
isbn: 9781424446827
citation:   Awaludin, I. and Ibrahim, R. and Rao, K.S.R.  (2009) Conventional ARX and artificial neural networks ARX models for prediction of oil consumption in Malaysia.  In: UNSPECIFIED.