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Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

by Shuyu Dai 1,2,*, Dongxiao Niu 1,2 and Yan Li 1,2
1
School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(5), 678; https://doi.org/10.3390/app8050678
Received: 6 March 2018 / Revised: 15 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
(This article belongs to the Section Energy)
For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm), is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product), industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm) model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an empirical analysis for energy consumption prediction. Four models, ISFLA-LSSVM, SFLA-LSSVM (Least Squares Support Vector Machine Optimized by Shuffled Frog Leaping Algorithm), LSSVM (Least Squares Support Vector Machine), and BP(Back Propagation) neural network (Back Propagation neural network), are selected to compare with the EEMD-ISFLA-LSSVM model based on the evaluation indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), which fully prove the practicability of the EEMD-ISFLA-LSSVM model for energy consumption forecasting in China. Finally, the EEMD-ISFLA-LSSVM model is adopted to forecast the energy consumption in China from 2018 to 2022, and, according to the forecasting results, it can be seen that China’s energy consumption from 2018 to 2022 will have a trend of significant growth. View Full-Text
Keywords: energy consumption forecasting; improved shuffled frog leaping algorithm; least squares support vector machine; ensemble empirical mode decomposition energy consumption forecasting; improved shuffled frog leaping algorithm; least squares support vector machine; ensemble empirical mode decomposition
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MDPI and ACS Style

Dai, S.; Niu, D.; Li, Y. Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm. Appl. Sci. 2018, 8, 678.

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