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IJMLC 2021 Vol.11(6): 380-386 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.6.1065

Forecasting Household Electricity Consumption Using Time Series Models

Patcharakorn Sokannit and Pasapitch Chujai

Abstract—The present study sought to develop and compare models for forecasting time series data on the household electricity consumption using ARIMA, GARCH and Winters Triple Exponential Smoothing models. Two datasets used in this study were monthly time series data from Provincial Electrical Authority, Punpin district, Suratthani province; specifically, the datasets were concerned with the household electricity consumption fewer than and above 150 units. The selection of the optimal forecasting model was based on the lowest RMSE. The results demonstrated that the GARCH models, namely GARCH (2,0) and GARCH (1,1) respectively, were suitable for the time series data on the household electricity consumption below and above 150 units; the two models could be used to provide only a one-month ahead forecast.

Index Terms—ARIMA, GARCH, triple exponential smoothing (Winter), time series data on household electricity consumption, stationary.

P. Sokannit is with the Faculty of Education, Industry and Technology, King Mongkut's University of Technology Thonburi 10140, Thailand (e-mail: eyepatkorn@gmail.com).
P. Chujai is with the Electrical Technology Education Department, Faculty of Industrial Education and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand (e-mail: pasapitchchujai@gmail.com).

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Cite: Patcharakorn Sokannit and Pasapitch Chujai, "Forecasting Household Electricity Consumption Using Time Series Models," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 380-386, 2021.

Copyright © 2021 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: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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