NDLI: Conventional ARX and Artificial Neural networks ARX models for prediction of oil consumption in Malaysia

Content Provider	IEEE Xplore Digital Library
Author	Awaludin, I. Ibrahim, R. Rama Rao, K.S.
Copyright Year	2009
Description	Author affiliation: Electrical and Electronics, Engineering Dept., Universiti Teknologi, PETRONAS, Bandar Seri Iskandar, Malaysia (Ibrahim, R.; Rama Rao, K.S.) \|\| Electrical and Electronics, Engineering, UTP Malaysia/ Computer Engineering, Department POLBAN Indonesia (Awaludin, I.)
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.
Starting Page	23
Ending Page	28
File Size	421983
Page Count	6
File Format	PDF
ISBN	9781424446810
DOI	10.1109/ISIEA.2009.5356496
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption ANN ARX Economic indicators Government Artificial neural networks Prediction ARX Predictive models Gallium nitride Petroleum Industrial electronics Production Natural gas
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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