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Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption.

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Title: Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption.
Authors: Zhou Cheng1, Chen XiYang2 chenxiyang@mail.hust.edu.cn
Source: Journal of Applied Mathematics. 2014, p1-12. 12p.
Subjects: Adaptive computing systems, Energy consumption, Weather forecasting, Statistical smoothing, Statistical correlation
Geographic Terms: China
Abstract: To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individualmodels (BP, GM(1, 1), triple exponential smoothingmodel, and polynomial trend extrapolationmodel) and combination forecastingmodels to predict China's energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel's performances rank in line with ACD. Then ACD is firstly proposed to choose individualmodels for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based onACDshows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China's energy consumption will be up to 5.7988 billion tons of standard coal in 2018. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell 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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  Data: Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption.
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  Data: <searchLink fieldCode="AR" term="%22Zhou+Cheng%22">Zhou Cheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen+XiYang%22">Chen XiYang</searchLink><relatesTo>2</relatesTo><i> chenxiyang@mail.hust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Mathematics%22">Journal of Applied Mathematics</searchLink>. 2014, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+computing+systems%22">Adaptive computing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+smoothing%22">Statistical smoothing</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individualmodels (BP, GM(1, 1), triple exponential smoothingmodel, and polynomial trend extrapolationmodel) and combination forecastingmodels to predict China's energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel's performances rank in line with ACD. Then ACD is firstly proposed to choose individualmodels for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based onACDshows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China's energy consumption will be up to 5.7988 billion tons of standard coal in 2018. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell 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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1155/2014/845807
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Adaptive computing systems
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Weather forecasting
        Type: general
      – SubjectFull: Statistical smoothing
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption.
        Type: main
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          Name:
            NameFull: Zhou Cheng
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          Name:
            NameFull: Chen XiYang
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            – D: 01
              M: 01
              Text: 2014
              Type: published
              Y: 2014
          Identifiers:
            – Type: issn-print
              Value: 1110757X
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            – TitleFull: Journal of Applied Mathematics
              Type: main
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