IJMLC 2019 Vol.9(5): 694-699 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.860

Application of Time Series Analysis in Projecting Philippines’ Electric Consumption

Allemar Jhone P. Delima

Abstract—This paper employed the famous ARIMA(p,d,q) model in forecasting Philippines’ electric consumption for the years 2018 to 2022 using the univariate historical data of the country’s power statistics in megawatt-hour from 2003-2017. The ACF and PACF plots were considered as well as stationarity of the data in assigning appropriate (p,d,q) values. By selecting the model with the lowest Akaike Information Criterion (AIC) value, an optimal model is then identified. The simulation result showed that ARIMA(1,2,1) model appeared to be the statistically appropriate model to forecast the Philippines’ electrical consumption allocated for residential, commercial, and industrial use. A forecasted value of 28,281,111Mwh, 29,786,410Mwh, 31,291,820Mwh, 32,797,232 Mwh, and 34,302,644 Mwh for the residential use in the years 2018, 2019, 2020, 2021, and 2022 respectively are expected. While the forecast for commercial use has the value of 23,866,067Mwh, 24,950,677Mwh, 26,037,098Mwh, 27,123,275Mwh, and 28,209,485Mwh for the same sequence of years. Further, years 2018, 2019, 2020, 2021, and 2022 has forecasted electrical consumption value of 26,963,287Mwh, 28,373,297Mwh, 29,777,292Mwh, 31,183,097Mwh, and 32,588,357Mwh respectively for industrial use.

Index Terms—ARIMA, electricity, electric consumption, forecasting, time series.

Allemar Jhone P. Delima is with the College of Engineering and Information Technology, SSCT, Surigao City, 8400, Philippines (e-mail: allemarjpd@ssct.edu.ph).

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Cite: Allemar Jhone P. Delima, "Application of Time Series Analysis in Projecting Philippines’ Electric Consumption," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 694-699, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net