Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

Bingchun Liu, Chuanchuan Fu, Arlene Bielefield and Yan Quan Liu
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Bingchun Liu: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Chuanchuan Fu: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Arlene Bielefield: Department of Information and Library Science, Southern Connecticut State University, New Haven, CT 06514, USA
Yan Quan Liu: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China

Energies, 2017, vol. 10, issue 10, 1-15

Abstract: The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021.

Keywords: energy consumption; gated recurrent unit; forecasting scenarios; energy planning; energy consumption; China (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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