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Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China.

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Title: Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China.
Authors: Wu, Lifeng1 wlf6666@126.com, Gao, Xiaohui1, Xiao, Yanli1 84536065@qq.com, Yang, Yingjie2, Chen, Xiangnan1
Source: Energy. Aug2018, Vol. 157, p327-335. 9p.
Subjects: Energy consumption forecasting, Gray forecasting model, Electric power consumption forecasting, Electric power, Economics
Geographic Terms: Shandong Sheng (China)
Abstract: The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province. [ABSTRACT FROM AUTHOR]
Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 130690780
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Lifeng%22">Wu, Lifeng</searchLink><relatesTo>1</relatesTo><i> wlf6666@126.com</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Xiaohui%22">Gao, Xiaohui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Yanli%22">Xiao, Yanli</searchLink><relatesTo>1</relatesTo><i> 84536065@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yingjie%22">Yang, Yingjie</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiangnan%22">Chen, Xiangnan</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Aug2018, Vol. 157, p327-335. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Energy+consumption+forecasting%22">Energy consumption forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+forecasting+model%22">Gray forecasting model</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+consumption+forecasting%22">Electric power consumption forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power%22">Electric power</searchLink><br /><searchLink fieldCode="DE" term="%22Economics%22">Economics</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Shandong+Sheng+%28China%29%22">Shandong Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.energy.2018.05.147
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 327
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      – SubjectFull: Energy consumption forecasting
        Type: general
      – SubjectFull: Gray forecasting model
        Type: general
      – SubjectFull: Electric power consumption forecasting
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      – SubjectFull: Electric power
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      – SubjectFull: Economics
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      – SubjectFull: Shandong Sheng (China)
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      – TitleFull: Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China.
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            NameFull: Wu, Lifeng
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            NameFull: Gao, Xiaohui
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            NameFull: Yang, Yingjie
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              Text: Aug2018
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              Y: 2018
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