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Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network
Resource type
Authors/contributors
- Liu, Bingchun (Author)
- Fu, Chuanchuan (Author)
- Bielefield, Arlene (Author)
- Liu, Yan Quan (Author)
Title
Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network
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.
Publication
Energies
Date
2017/10
Volume
10
Issue
10
Pages
1453
Citation Key
liuForecastingChinesePrimary2017
Accessed
10/4/19, 4:10 PM
ISSN
1996-1073
Language
English
Library Catalog
Extra
55 citations (Crossref) [2023-10-31]
Citation Key Alias: ISI:000414578400008
tex.article-number: [object Object]
tex.unique-id: [object Object]
Citation
Liu, B., Fu, C., Bielefield, A., & Liu, Y. Q. (2017). Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network. Energies, 10(10), 1453. https://doi.org/10.3390/en10101453
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