Macroeconomics and Monetary Economics - 1
Abstract
Demand and supply side assessment are the two foremost important components of energy
management and planning. Unfortunately, for the past twenty years Pakistan is confronting extremely
serious issues with energy management such as electricity followed by institutional incompetence and
lack of policy response. This is due to the fact that the country neither has long term energy plans nor
short-term solutions to deal with energy crisis. This study outlines overall consumption of electricity
and forecasting its various components. The interminable crisis of electricity affects all sectors of
economy. The study deals with this particular aspect and applies Holt-winter and ARIMA models for
the forecasting. The outcomes of both the models suggest that ARIMA model is more reliable for
forecasting as compared to Holt-winter model. Estimated results affirm the tendency of increasing
demand in all the indices which show an alarming position in future. Household sector will have the
highest energy demand in 2030, followed by industrial sector. Thus, due to the ever increasing demand
of electricity energy, government should initiate different renewable sources of power production such
as hydal and solar energy to overcome the shortfall of electricity energy as well as sustainability in
economy.
References
of neural network, time series and ANOVA. Applied Mathematics and Computation, 186(2), pp. 1753-
1761.
Aqeel, A. & Butt, M.S. (2001). The relationship between energy consumption and economic growth in
Pakistan. Asia-Pacific Development Journal, 8(2), pp. 101-110.
Hussain, A.; Rahman, M. & Memon, J.A. (2016). Forecasting electricity consumption in Pakistan: The
way forward. Energy Policy, 90, pp. 73-80.
As' ad, M. (2012). Finding the best ARIMA model to forecast daily peak electricity demand.
Ediger, V.Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey.
Energy Policy, 35(3), pp. 1701-1708.
Erdogdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case
study of Turkey.,Energy policy, 35(2), pp. 1129-1146.
Erdogdu, E. (2010). Natural gas demand in Turkey. Applied Energy, 87(1), pp. 211-219.
HDIP (2015-16). Hydrocarbon Developemnt institute of Pakistan. Pakistan Energy Yearbook.
IRG. (2010). Pakistan Integrated Energy model. International Resources Group.
Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential
smoothing. Journal of the Operational Research Society, 54(8), pp. 799-805.
Janjua, M.A. (2007). Pakistan’s external trade: Does exchange rate misalignment matter for Pakistan.
The Lahore Journal of Economics, 12, pp. 126-152.
Brandt, J.A. & Bessler, D.A. (1983). Price forecasting and evaluation: An application in agriculture.
Journal of Forecasting, 2(3), pp. 237-248.
Khan, T. (2011). Identifying an appropriate forecasting model for forecasting total import of
Bangladesh. Statistics in Transition new series, 12(1), pp. 179-192.
Zaman, M.; Shaheen, F.; Haider, A. & Qamar, S. (2015). Examining relationship between electricity
consumption and its major determinants in Pakistan. International Journal of Energy Economics and
Policy, 5(4), pp. 998-1009.
Nasir, M. & Rehman, F.U. (2011). Environmental Kuznets curve for carbon emissions in Pakistan: an
empirical investigation. Energy Policy, 39(3), pp. 1857-1864.
Nasir, M.; Tariq, M.S. & Arif, A. (2008). Residential demand for electricity in Pakistan. The Pakistan
Development Review, pp. 457-467.
Shahbaz, M. & Lean, H.H. (2012). The dynamics of electricity consumption and economic growth: A
revisit study of their causality in Pakistan. Energy, 39(1), pp. 146-153.
NBP. (2008). National Bank of Pakistan.
Shaikh, S.A.; Mirjat, N.H.; Korejo, W.S.; Walasai, G.D.; Larik, A.S. & Hussain, A. (2017). Electricity
Demand Forecasting: A Pakistans Perspective. Asian Journal of Engineering, Sciences & Technology,
7(2).
PC. (2013). Planning Commission.
PEPCO. (2013). Pakistan Electric Power Company.
PES. (2006-07). Pakistan Economy of Survery.
PES. (2012). Pakistan Economy Survey.
PIP. (2015). Petroleum Institute Pakistan. Pakistan energy Outlook.
PIPB. (2008). Supply and Demand of Electricity in Pakistan. Private Power and Infrastructure Board.
PWDP. (2015). Pakistan Public Works Department.
Siddiqui, R. (2004). Energy and economic growth in Pakistan. The Pakistan Development Review, pp.
175-200.
Saab, S.; Badr, E. & Nasr, G. (2001). Univariate modeling and forecasting of energy consumption: the
case of electricity in Lebanon. Energy, 26(1), pp. 1-14.
SESRIC. (2014). The Statistical, Economic and Social Research and Training Centre for Islamic
countries.
Sims, C.A. (1986). Are forecasting models usable for policy analysis? Quarterly Review, (Win), pp. 2-
16.
Kumar, U. & Jain, V.K. (2010). ARIMA forecasting of ambient air pollutants (O 3, NO, NO 2 and CO).
Stochastic Environmental Research and Risk Assessment, 24(5), pp. 751-760.
Perwez, U. & Sohail, A. (2014). Forecasting of Pakistan’s net electricity energy consumption on the
basis of energy pathway scenarios. Energy Procedia, 61, pp. 2403-2411.
Lepojevic, V. & Andelkovic-Pesic, M. (2011). Forecasting electricity consumption by using holtwinters
and seasonal regression models. Economics and Organization, 8(4), pp. 421-431.
Ho, S.L. & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis.
Computers & industrial engineering, 35(1-2), pp. 213-216.
Zaman, A. (2012). Methodological mistakes and econometric consequences.
Published
How to Cite
Issue
Section
License
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.
