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dc.contributor.authorXie, Naimingen
dc.contributor.authorYuan, Chaoqingen
dc.contributor.authorYang, Yingjieen
dc.date.accessioned2016-03-21T10:26:12Z
dc.date.available2016-03-21T10:26:12Z
dc.date.issued2015
dc.identifier.citationXie, N. Yuan, C. and Yang, Y. (2015) Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. International Journal of Electrical Power & Energy Systems, 66, pp.1-8en
dc.identifier.issn0142-0615
dc.identifier.urihttp://hdl.handle.net/2086/11657
dc.description.abstractThis paper applies novel models to forecast the developing trends of China’s energy production and consumption under the influence of China’s energy saving policy. An optimized single variable discrete grey forecasting model is adopted to forecast the total amount of energy production and consumption while a novel Markov approach based on quadratic programming model is proposed to forecast the trends of energy production and consumption structures. The proposed models are used to simulate China’s energy production and consumption during 2006–2011 and forecast its trends in 2015 and 2020. Results demonstrate that proposed models can effectively simulate and forecast the total amounts and structures of energy production and consumption. And by comparing with regression model, results show proposed model is a little better than regression in simulating and forecasting the case. Although the growth rate of energy consumption in China has decreased under the energy saving policy, total amount of energy consumption and the proportions of natural gas and other energies keep growing while the self-sufficiency rate of crude oil and natural gas continues to drop.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofseries66;
dc.subjectGrey Systemsen
dc.titleForecasting China's energy demand and self-sufficiency rate by grey forecasting model and Markov modelen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.ijepes.2014.10.028
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderLeverhulme Trusten
dc.projectidIN-2014-020en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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