Time series machine learning: Implementing ARIMA and hybrid ARIMA-ANN for electricity forecasting modeling

Wahyu Wibowo*, Sarirazty Dwijantari, Alia Hartati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The aims of this paper are to develop a linear and nonlinear model in time series to forecast electricity consumption of the lowest household category in East Java, Indonesia. The installed capacity in the lowest household customer category has various power, i.e. 450 VA, 900 VA, 1300 VA, and 2200 VA. ARIMA models are family of linear model for time series analysis and forecasting for both stationary and non-stationary, seasonal and non-seasonal time series data. A nonlinear time series model is proposed by hybrid ARIMA-ANN, a Radial Basis Function using orthogonal least squares. The criteria used to choose the best forecasting model are the Mean Absolute Percentage Error and the Root Mean Square Error. The ARIMA best model are ARIMA ([1, 2], 1, 0) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ARIMA (1, 0, 0) (0, 1, 0)12 respectively. The ANN architecture optimum are ANN (2, 12, 1), ANN (1, 12, 1), ANN (1, 12, 1), and ANN (1, 12, 1). The best models are ARIMA ([1, 2], 1, 0) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ANN (1, 12, 1), and ANN (1, 12, 1) in each category respectively. Hence, the result shows that a complex model is not always better than a simpler model. Additionally, a better hybrid ANN model is relied on the choice of a weighted input constant of RBF.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 3rd International Conference, SCDS 2017, Proceedings
EditorsAzlinah Mohamed, Bee Wah Yap, Michael W. Berry
PublisherSpringer Verlag
Pages126-139
Number of pages14
ISBN (Print)9789811072413
DOIs
Publication statusPublished - 2017
Event3rd International Conference on Soft Computing in Data Science, SCDS 2017 - Yogyakarta, Indonesia
Duration: 27 Nov 201728 Nov 2017

Publication series

NameCommunications in Computer and Information Science
Volume788
ISSN (Print)1865-0929

Conference

Conference3rd International Conference on Soft Computing in Data Science, SCDS 2017
Country/TerritoryIndonesia
CityYogyakarta
Period27/11/1728/11/17

Keywords

  • ARIMA
  • Electricity consumption
  • Forecasting
  • Hybrid ARIMA-ANN

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