 A comparative study on prediction methods for China's medium- and long-term coal demandBing-Bing Li, Qiao-Mei Liang and Jin-Cheng Wang Energy, 2015, vol. 93, issue P2, 1671-1683 Abstract: Given the dominant position of coal in China's energy structure and in order to ensure a safe and stable energy supply, it is essential to perform a scientific and effective prediction of China's medium- and long-term coal demand. Based on the historical data of coal consumption and related factors such as GDP (Gross domestic product), coal price, industrial structure, total population, energy structure, energy efficiency, coal production and urbanization rate from 1987 to 2012, this study compared the prediction effects of five types of models. These models include the VAR (vector autoregressive model), RBF (radial basis function) neural network model, GA-DEM (genetic algorithm demand estimation model), PSO-DEM (particle swarm optimization demand estimation model) and IO (input–output model). By comparing the results of different models with the corresponding actual coal consumption, it is concluded that with a testing period from 2006 to 2012, the PSO-DEM model has a relatively optimal predicted effect on China's total coal demand, where the MAPE (mean absolute percentage error) is close to or below 2%. Keywords: Coal demand; Forecasting models; Comparison of methods; China (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:93:y:2015:i:p2:p:1671-1683 DOI: 10.1016/j.energy.2015.10.039 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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