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dc.contributor.authorYe, Jing
dc.contributor.authorDang, Yaoguo
dc.contributor.authorSong, Ding
dc.contributor.authorYang, Yingjie
dc.date.accessioned2019-07-18T10:22:54Z
dc.date.available2019-07-18T10:22:54Z
dc.date.issued2019-05-04
dc.identifier.citationYe, J., Dang, Y., Song, D. and Yang, Y. (2019) A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers, Journal of Cleaner Production, 229, pp.256-267.en
dc.identifier.issn0959-6526
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/18244
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractSince energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectEnergy consumptionen
dc.subjectGrey system theoryen
dc.subjectInterval numbersen
dc.subjectPredictionen
dc.subjectElectricityen
dc.titleA novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbersen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.jclepro.2019.04.336
dc.peerreviewedYesen
dc.funderLeverhulme Trusten
dc.projectidLeverhulme: 2014-IN-020en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019-04-25
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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