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Energies 2017, 10(10), 1453; https://doi.org/10.3390/en10101453

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

1
Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
2
Department of Information and Library Science, Southern Connecticut State University, New Haven, CT 06514, USA
*
Author to whom correspondence should be addressed.
Academic Editor: John Ringwood
Received: 17 July 2017 / Revised: 28 August 2017 / Accepted: 7 September 2017 / Published: 21 September 2017
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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. View Full-Text
Keywords: energy consumption; gated recurrent unit; forecasting scenarios; energy planning; energy consumption; China energy consumption; gated recurrent unit; forecasting scenarios; energy planning; energy consumption; China
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Liu, B.; Fu, C.; Bielefield, A.; Liu, Y.Q. Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network. Energies 2017, 10, 1453.

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