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: firstname.lastname@example.org).
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: email@example.com).
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).