Conventional ARX and Artificial Neural Networks ARX Models for Prediction of Oil Consumption in Malaysia
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....
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/4387/1/2009_Oct_4-6_IEEE_ISIEA2009_-_ANN_ARX.pdf http://eprints.utp.edu.my/4387/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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. |
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