Predicting primary energy consumption using hybrid arima and ga-svr based on eemd decomposition

Yu Sheng Kao, Kazumitsu Nawata, Chi Yo Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Forecasting energy consumption is not easy because of the nonlinear nature of the time series for energy consumptions, which cannot be accurately predicted by traditional forecasting methods. Therefore, a novel hybrid forecasting framework based on the ensemble empirical mode decomposition (EEMD) approach and a combination of individual forecasting models is proposed. The hybrid models include the autoregressive integrated moving average (ARIMA), the support vector regression (SVR), and the genetic algorithm (GA). The integrated framework, the so-called EEMD-ARIMA-GA-SVR, will be used to predict the primary energy consumption of an economy. An empirical study case based on the Taiwanese consumption of energy will be used to verify the feasibility of the proposed forecast framework. According to the empirical study results, the proposed hybrid framework is feasible. Compared with prediction results derived from other forecasting mechanisms, the proposed framework demonstrates better precisions, but such a hybrid system can also be seen as a basis for energy management and policy definition.

Original languageEnglish
Article number1722
Pages (from-to)1-19
Number of pages19
Issue number10
Publication statusPublished - 2020 Oct


  • Autoregressive integrated moving average (ARIMA)
  • Energy consumption
  • Ensemble empirical mode decomposition (EEMD)
  • Forecasting
  • Genetic algorithm (GA)
  • Support vector regression (SVR)

ASJC Scopus subject areas

  • General Mathematics


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