Oil demand forecasting for India using artificial neural network

Jebaraj, S. and Iniyan, S. (2015) Oil demand forecasting for India using artificial neural network. International Journal of Global Energy Issues, 38 (4-6). pp. 322-341. ISSN 09547118

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Abstract

Energy is a vital input for the growth of any nation. Since oil resource has become a vital factor for future developments of a country, a system of models has to be developed to provide forecasts of oil demands in various sectors. This analysis utilises regression techniques, double moving average method, double exponential smoothing method, triple exponential smoothing method, Autoregressive Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) model (univariate and multivariate) for oil demand forecasts in India. Model validation is done to select the best forecasting model. It is found that the ANN model gives better results in most of the cases. Hence, it is suggested that the ANN model can be used for forecasting oil demands in India. It is also predicted that the total oil demand for the years 2020 and 2030 will be 415,373 and 720,688 thousand tonnes, respectively. Copyright © 2015 Inderscience Enterprises Ltd.

Item Type: Article
Additional Information: cited By 1
Uncontrolled Keywords: Autoregressive moving average model; Forecasting; Petroleum industry, Artificial neural network models; Autoregressive integrated moving average models; Demand forecasting; Double exponential smoothing methods; Exponential smoothing method; Forecasting models; Model simulation; Oil consumption, Neural networks, artificial neural network; demand analysis; demand-side management; economic growth; energy resource; numerical model; oil industry, India
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:18
Last Modified: 09 Nov 2023 16:18
URI: https://khub.utp.edu.my/scholars/id/eprint/6317

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