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A novel economy reflecting short-term load forecasting approach

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Title: A novel economy reflecting short-term load forecasting approach
Authors: Lin, Cheng-Ting1, Chou, Li-Der cld@csie.ncu.edu.tw
Source: Energy Conversion & Management. Jan2013, Vol. 65, p331-342. 12p.
Subjects: Financial crises, Economic forecasting, Bankruptcy, Lehman Brothers, Economic demand, Prediction theory, Stock exchanges, Stock price indexes
Abstract: Abstract: The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF. [Copyright &y& Elsevier]
Copyright of Energy Conversion & Management 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.)
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DbLabel: Engineering Source
An: 83928475
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  Data: A novel economy reflecting short-term load forecasting approach
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Cheng-Ting%22">Lin, Cheng-Ting</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chou%2C+Li-Der%22">Chou, Li-Der</searchLink><i> cld@csie.ncu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Energy+Conversion+%26+Management%22">Energy Conversion & Management</searchLink>. Jan2013, Vol. 65, p331-342. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Financial+crises%22">Financial crises</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+forecasting%22">Economic forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Bankruptcy%22">Bankruptcy</searchLink><br /><searchLink fieldCode="DE" term="%22Lehman+Brothers%22">Lehman Brothers</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+demand%22">Economic demand</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+theory%22">Prediction theory</searchLink><br /><searchLink fieldCode="DE" term="%22Stock+exchanges%22">Stock exchanges</searchLink><br /><searchLink fieldCode="DE" term="%22Stock+price+indexes%22">Stock price indexes</searchLink>
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  Data: Abstract: The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Energy Conversion & Management 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.enconman.2012.08.001
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 331
    Subjects:
      – SubjectFull: Financial crises
        Type: general
      – SubjectFull: Economic forecasting
        Type: general
      – SubjectFull: Bankruptcy
        Type: general
      – SubjectFull: Lehman Brothers
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      – SubjectFull: Economic demand
        Type: general
      – SubjectFull: Prediction theory
        Type: general
      – SubjectFull: Stock exchanges
        Type: general
      – SubjectFull: Stock price indexes
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      – TitleFull: A novel economy reflecting short-term load forecasting approach
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            NameFull: Lin, Cheng-Ting
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            NameFull: Chou, Li-Der
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            – D: 01
              M: 01
              Text: Jan2013
              Type: published
              Y: 2013
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              Value: 65
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            – TitleFull: Energy Conversion & Management
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