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Functional Time series (FTS) Forecasting of Electricity Consumption in Pakistan

by Farah Yasmeen, Muhammad Sharif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 7
Year of Publication: 2015
Authors: Farah Yasmeen, Muhammad Sharif
10.5120/ijca2015905518

Farah Yasmeen, Muhammad Sharif . Functional Time series (FTS) Forecasting of Electricity Consumption in Pakistan. International Journal of Computer Applications. 124, 7 ( August 2015), 15-19. DOI=10.5120/ijca2015905518

@article{ 10.5120/ijca2015905518,
author = { Farah Yasmeen, Muhammad Sharif },
title = { Functional Time series (FTS) Forecasting of Electricity Consumption in Pakistan },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 7 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number7/22115-2015905518/ },
doi = { 10.5120/ijca2015905518 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:46.061179+05:30
%A Farah Yasmeen
%A Muhammad Sharif
%T Functional Time series (FTS) Forecasting of Electricity Consumption in Pakistan
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 7
%P 15-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electricity is one of the most important sources for economic and social development of a country. The growth in energy consumption is basically linked with the growth in economy. Energy demand increases due to different reasons, including higher Gross Domestic Product (GDP) growth, higher per capita consumption, the population growth and rapid development of industrial & commercial sectors. In this study, the monthly electricity consumption for the period of January 1990 through December 2011 in Pakistan is analysed using functional time series (FTS) technique. Electricity consumption model reveals a significant trend due to socio-economic factors. The monthly behavior of forecast values reveals that the electricity consumption is more for summer season and this demand will be increased in future. Forecast model and the forecast values show that the electricity consumption is increasing with the passage of time. The growing energy consumption in the country may be due to economic growth, urbanization process in the region, population growth and industrialization.

References
  1. Ali, M, Iqbal, J.M. and Sharif, M. 2013, Relationship between extreme temperature and electricity demand in Pakistan, International Journal of Energy and Environmental Engineering (IEEE), pages 4-36.
  2. Khan, A.M., Ahmed, U. 2009, Energy Demand in Pakistan: A Disaggregate analysis, Pakistan Institute of Development Economics, Islamabad. Available at athttp://mpra.ub.uni- muenchen.de/15056.
  3. Khan, M.A. and Qayyum, A. 2009, The Demand for Electricity in Pakistan. OPEC Energy Review, pages 70-96.
  4. Alter,N. and Shabib, H.S. 2011, An Empirical Analysis of Electricity Demand in Pakistan” International Journal of Energy Economics and Policy 1(4), 2011, pages 116-139 ISSN: 2146-4553.
  5. Zachariadis,T. and Pashourtidou,N. 2006, An Empirical Analysis of Electricity Consumption in Cyprus, Economics Research Centre University of Cyprus, Discussion Paper .
  6. Zahang.G.,Patuwo,E.B. and Hu,Y.M. 1998, Forecasting with artificial neural networks:The state of the art”,International Journal of Forecasting Vol.14 , pages 435–62 .
  7. Granger,C.W.J. 1993, Strategies for modelling nonlinear time series relationships, The Economic Record 69 (206), pages 233–238.
  8. Yasmeen, F. and Sharif, M. 2014, Forecasting Electricity Consumption for Pakistan, International Journal of Emerging Technology and Advanced Engineering (IJETAE) 4(4).
  9. Mati,A.A.,Eng,M.,Gajoga,G.B.,Jimoh,B.,Adegobye,A. and Dajab,D.D. 2009, Electricity Demand Forecasting in Nigeria using Time Series Model, The Pacific Journal of Science and Technology 10(2). pages 479- 485.
  10. Aman,S., Ping,W.H. and Mubin,M. 2011, Modelling and forecasting electricity consumption of Malaysian large steel mills, Scientific Research and Essays 6(8), pages 1817-1830.
  11. Hall,P.B., Muller,G.H. and Wang,L.J .2006, Properties of Principal Component Methods For Functional and Longitudinal Analysis, Institute of Mathematical Statistics, The Annals of Statistics, Vol. 34(3), pages 1493–1517.
  12. Hyndman, R.J. and Ullah, M. S. 2007, Robust forecasting of mortality and fertility rates: A functional data approach, Computational Statistics & Data Analysis 51 , 4942 – 4956.
  13. Erbas, B., Hyndman, R.J and Gertig, D.M., 2007, Forecasting age-specific breast cancer mortality using functional data models, Statistics in Medicine 26, 458-470.
  14. Yasmeen, F., Hyndman, R. J. and Erbas, B. 2010, Forecasting age-related changes in breast cancer mortality among white and black US women: A functional data approach, Cancer Epidemiology 34(5), pages 54F.
  15. Yasmeen, F. and Zaheer, S. 2014. Functional time series models to estimate future age-specific breast cancer incidence rates for women in Karachi, Pakistan, Journal of Health Science, 2 David Publishing Company, pages 213-221.
  16. Yasmeen,,F and Mughal, S. 2014, Functional Time Series Models and t.he APC models: A comparative study on the lung cancer incidence rates in Denmark Journal of US-China Medical Science, David Publishing Company, 11 (3) pages. 121-128
  17. Yasmeen,F. Fatima, H. and Mahmood, Z. 2014, An FDA approach to forecast age-specific fertility rates of Pakistan region-wise, Computer Science and Applications, Ethan Publishing Company 1(6) pages. 341-348 www. ethanpublishing.com
Index Terms

Computer Science
Information Sciences

Keywords

Functional Time Series Functional Data Electricity Consumption Principal Component forecast