View in EDS PDF Full Text

Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm.

Saved in:
Bibliographic Details
Title: Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm.
Authors: Shabri, Ani1 ani@utm.my, Samsudin, Ruhaidah2 ruhaidah@utm.my
Source: International Journal of Electrical & Computer Engineering (2088-8708). Dec2025, Vol. 15 Issue 6, p5854-5862. 9p.
Subjects: Load forecasting (Electric power systems), Bees algorithm, Data analysis, Seasonal temperature variations, Forecasting, Fluctuations (Physics)
Geographic Terms: Malaysia
Abstract: Nonlinear and seasonal fluctuations present significant challenges in predicting electricity load. To address this, a combination weighted forecast model (CWFM) based on individual prediction models is proposed. The artificial bee colony (ABC) algorithm is used to optimize the weighted coefficients. To evaluate the model's performance, the novel CWFM and three benchmark models are applied to forecast electricity load in Malaysia and Thailand. Performance is assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results indicate that the proposed combined model outperforms the single models, demonstrating improved accuracy and better capturing seasonal variations in electricity load. The ABC algorithm helps in finding the optimal combination of weights, ensuring that the model adapts effectively to different forecasting scenarios. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & 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
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 190950316
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shabri%2C+Ani%22">Shabri, Ani</searchLink><relatesTo>1</relatesTo><i> ani@utm.my</i><br /><searchLink fieldCode="AR" term="%22Samsudin%2C+Ruhaidah%22">Samsudin, Ruhaidah</searchLink><relatesTo>2</relatesTo><i> ruhaidah@utm.my</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Dec2025, Vol. 15 Issue 6, p5854-5862. 9p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Load+forecasting+%28Electric+power+systems%29%22">Load forecasting (Electric power systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Seasonal+temperature+variations%22">Seasonal temperature variations</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Fluctuations+%28Physics%29%22">Fluctuations (Physics)</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Malaysia%22">Malaysia</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nonlinear and seasonal fluctuations present significant challenges in predicting electricity load. To address this, a combination weighted forecast model (CWFM) based on individual prediction models is proposed. The artificial bee colony (ABC) algorithm is used to optimize the weighted coefficients. To evaluate the model's performance, the novel CWFM and three benchmark models are applied to forecast electricity load in Malaysia and Thailand. Performance is assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results indicate that the proposed combined model outperforms the single models, demonstrating improved accuracy and better capturing seasonal variations in electricity load. The ABC algorithm helps in finding the optimal combination of weights, ensuring that the model adapts effectively to different forecasting scenarios. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190950316
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.11591/ijece.v15i6.pp5854-5862
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 5854
    Subjects:
      – SubjectFull: Load forecasting (Electric power systems)
        Type: general
      – SubjectFull: Bees algorithm
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Seasonal temperature variations
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Fluctuations (Physics)
        Type: general
      – SubjectFull: Malaysia
        Type: general
    Titles:
      – TitleFull: Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shabri, Ani
      – PersonEntity:
          Name:
            NameFull: Samsudin, Ruhaidah
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 20888708
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708)
              Type: main
ResultId 1