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Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

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  • Yu-Sheng Kao

    (Department of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Kazumitsu Nawata

    (Department of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Chi-Yo Huang

    (Department of Industrial Education, National Taiwan Normal University, Taipei 106, Taiwan)

Abstract

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.

Suggested Citation

  • Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2020. "Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1722-:d:424470
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    References listed on IDEAS

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