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Article

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

1
Department of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
2
Department of Industrial Education, National Taiwan Normal University, Taipei 106, Taiwan
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1722; https://doi.org/10.3390/math8101722
Received: 6 September 2020 / Revised: 25 September 2020 / Accepted: 27 September 2020 / Published: 7 October 2020
(This article belongs to the Special Issue Quantitative Methods for Economics and Finance)
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. View Full-Text
Keywords: ensemble empirical mode decomposition (EEMD); autoregressive integrated moving average (ARIMA); support vector regression (SVR); genetic algorithm (GA); energy consumption; forecasting ensemble empirical mode decomposition (EEMD); autoregressive integrated moving average (ARIMA); support vector regression (SVR); genetic algorithm (GA); energy consumption; forecasting
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MDPI and ACS Style

Kao, Y.-S.; Nawata, K.; Huang, C.-Y. Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition. Mathematics 2020, 8, 1722. https://doi.org/10.3390/math8101722

AMA Style

Kao Y-S, Nawata K, Huang C-Y. Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition. Mathematics. 2020; 8(10):1722. https://doi.org/10.3390/math8101722

Chicago/Turabian Style

Kao, Yu-Sheng, Kazumitsu Nawata, and Chi-Yo Huang. 2020. "Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition" Mathematics 8, no. 10: 1722. https://doi.org/10.3390/math8101722

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