TY - JOUR
T1 - Predicting primary energy consumption using hybrid arima and ga-svr based on eemd decomposition
AU - Kao, Yu Sheng
AU - Nawata, Kazumitsu
AU - Huang, Chi Yo
N1 - Funding Information:
Funding: This article was subsidized by the Taiwan Normal University (NTNU), Taiwan and the Ministry of Science and Technology, Taiwan under Grant numbers T10807000105 and MOST 106-2221-E-003-019-MY3.
Funding Information:
This article was subsidized by the Taiwan Normal University (NTNU), Taiwan and the Ministry of Science and Technology, Taiwan under Grant numbers T10807000105 and MOST 106-2221-E-003-019-MY3.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Autoregressive integrated moving average (ARIMA)
KW - Energy consumption
KW - Ensemble empirical mode decomposition (EEMD)
KW - Forecasting
KW - Genetic algorithm (GA)
KW - Support vector regression (SVR)
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U2 - 10.3390/math8101722
DO - 10.3390/math8101722
M3 - Article
AN - SCOPUS:85092901409
SN - 2227-7390
VL - 8
SP - 1
EP - 19
JO - Mathematics
JF - Mathematics
IS - 10
M1 - 1722
ER -