An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Syed Aziz Ur Rehman, Yanpeng Cai, Rizwan Fazal, Gordhan Das Walasai and Nayyar Hussain Mirjat
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Syed Aziz Ur Rehman: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
Yanpeng Cai: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
Rizwan Fazal: Pakistan Institute of Development Economics (PIDE), Quaid-e-Azam University Campus, P.O. Box 1091, Islamabad 44000, Pakistan
Gordhan Das Walasai: Department of Mechanical Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67480, Pakistan
Nayyar Hussain Mirjat: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan

Energies, 2017, vol. 10, issue 11, 1-23

Abstract: Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

Keywords: autoregressive integrated moving average; energy forecasting; Holt-Winter; long-range energy alternate planning; Pakistan (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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