Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Oil Demand Forecasting in Malaysia in Transportation Sector using Artificial Neural Network (ANN)

Abd Rashid, Muhammad Afiq (2015) Oil Demand Forecasting in Malaysia in Transportation Sector using Artificial Neural Network (ANN). IRC, Universiti Teknologi PETRONAS. (Unpublished)

[img]
Preview
PDF
Download (1103Kb) | Preview

Abstract

Energy industry in Malaysia is one of critical sector that plays an important role in contributing the nation economic growth. The main energy source in Malaysia is from the petroleum and natural gas while the sector that consumed the most energy is transportation sector. Since both of these are the main energy source and consumer, a forecasting model is required to be developed to provide the oil demand forecast in transportation sector. This research analyses different forecasting models including time series regression technique, Auto Regressive Integrated Moving Average (ARIMA), double moving average method, double exponential smoothing method, triple exponential smoothing method and Artificial Neural Network (ANN) model (Univariate and Multivariate) to predict the future oil demand in transportation sector in Malaysia. In order to select the best forecasting model, the model validation is done using the error analysis technique such as Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Correlation Coefficient (R2). Based on the model validation result, it is found that the Artificial Neural Network gives the least error in all of the error analysis techniques. Thus, Artificial Neural Network model is used to forecast the oil demand in transportation sector in Malaysia. The oil demand forecasted by the model in transportation sector in Malaysia for the year 2020, 2025 and 2030 are 559.44, 581.779 and 609.941 kg of oil equivalent respectively

Item Type: Final Year Project
Academic Subject : Academic Department - Mechanical Engineering - Petroleum
Subject: T Technology > TJ Mechanical engineering and machinery
Divisions: Engineering > Mechanical
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 07 Oct 2015 10:19
Last Modified: 25 Jan 2017 09:35
URI: http://utpedia.utp.edu.my/id/eprint/15711

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...