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Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran

Maryam Doroodi (Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Islamic Republic of Iran, and Massey Business School, Massey University, Albany, New Zealand)
Alireza Mokhtar (Massey Business School, Massey University, Albany, New Zealand)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 6 March 2019

Issue publication date: 8 August 2019

182

Abstract

Purpose

The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant correlation with greenhouse gas emissions.

Design/methodology/approach

After studying the correlation between energy consumption rates in different sectors of energy consumption and some energy carriers with greenhouse gas distribution (CO2, SO2, NOX and SPM), the most effective factors on pollution emission will be first identified and then predicted for the next 20 years (2015 to 2004). Furthermore, to determine the appropriate method for forecasting, two approaches titled “trend analysis” and “double exponential smoothing” will be applied on data, collected from 1967 to 2014, and their capabilities in anticipating will be compared to each other contributing MSD, MAD, MAPE indices and also the actual and projected time series comparison. After predicting the energy consumption in the sectors and energy carriers, the growth rate of consumption in the next 20 years is also calculated.

Findings

Correlation study shows that four energy sectors (industry sector, agriculture, transportation and household-general-commercial) and two energy carriers (electricity and natural gas) have shown remarkable correlation with greenhouse gas emissions. To predict the energy consumption in mentioned sectors and carriers, it is proven that double exponential smoothing method is more capable in predicting. The study shows that among the demand sectors, the industry will account for the highest consumption rate. Electricity will experience the highest rate among the energy careers. In fact, producing this amount of electricity causes emissions of greenhouse gases.

Research limitations/implications

Access to the data and categorized data was one of the main limitations.

Practical implications

By identifying the sectors and energy carriers that have the highest consumption growth rate in the next 20 years, it can be said that greenhouse gas emissions, which show remarkable correlation with these sectors and carriers, will also increase dramatically. So, their stricter control seems to be necessary. On the other hand, to control a particular greenhouse gas, it is possible to focus on the amount of energy consumed in the sectors and carriers that have a significant correlation with this pollutant. These results will lead to more targeted policies to reduce greenhouse gas emissions.

Social implications

The tendency of communities toward industrialization along with population growth will doubtlessly lead to more consumption of fossil fuels. An immediate aftermath of burning fuels is greenhouse gas emission resulting in destructive effects on the environment and ecosystems. Identifying the factors affecting the pollutants resulted from consumption of fossil fuels is significant in controlling the emissions.

Originality/value

Such analyses help policymakers make more informed and targeted decisions to reduce greenhouse gas emissions and make safer and more appropriate policies and investment.

Keywords

Citation

Doroodi, M. and Mokhtar, A. (2020), "Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran", International Journal of Energy Sector Management, Vol. 13 No. 3, pp. 486-499. https://doi.org/10.1108/IJESM-08-2018-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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