The new hybrid approaches to forecasting short-term electricity load.
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| Title: | The new hybrid approaches to forecasting short-term electricity load. |
|---|---|
| Authors: | Fan, Guo-Feng1 (AUTHOR) guofengtongzhi@163.com, Liu, Yan-Rong1 (AUTHOR), Wei, Hui-Zhen1 (AUTHOR), Yu, Meng1 (AUTHOR), Li, Yin-He1 (AUTHOR) |
| Source: | Electric Power Systems Research. Dec2022, Vol. 213, pN.PAG-N.PAG. 1p. |
| Subjects: | Load forecasting (Electric power systems), Hilbert-Huang transform, Cumulative distribution function, Forecasting, Random forest algorithms, Statistical hypothesis testing |
| Geographic Terms: | New South Wales |
| Abstract: | • The EEMD-RF-SVR-RR hybrid model was used for modeling. • Coupling regression was used to weight both the RF-SVR model and the RR-RF model. • The K-S (Kolmogorov-Smirnov) based on cumulative distribution function was used to test the significance of the model. Electric load forecasting has a great impact on dispatching work and production scheme of power system. And accurate forecasting is helpful to the security and stability of power system. This paper proposes a hybrid model based on ensemble empirical mode decomposition (EEMD), random forest (RF), support vector regression (SVR) and ridge regression (RR) algorithm, namely EEMD-RF- SVR-RR model. EEMD is employed to solve the problem of data fluctuation. RF, SVR, and RR make the model have strong anti-noise ability, nonlinear mapping and stability. Numerical experiments were carried out with New South Wales(NSW, Australia). The results have showed that the forecasting accuracy of the model for different types in this paper is better than other models. So the feasibility and effectiveness of this method in short-term load forecasting are verified. [ABSTRACT FROM AUTHOR] |
| Copyright of Electric Power Systems Research is the property of Elsevier B.V. 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 159576580 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The new hybrid approaches to forecasting short-term electricity load. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fan%2C+Guo-Feng%22">Fan, Guo-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guofengtongzhi@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yan-Rong%22">Liu, Yan-Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Hui-Zhen%22">Wei, Hui-Zhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Meng%22">Yu, Meng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yin-He%22">Li, Yin-He</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Electric+Power+Systems+Research%22">Electric Power Systems Research</searchLink>. Dec2022, Vol. 213, pN.PAG-N.PAG. 1p. – 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="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink><br /><searchLink fieldCode="DE" term="%22Cumulative+distribution+function%22">Cumulative distribution function</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+South+Wales%22">New South Wales</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • The EEMD-RF-SVR-RR hybrid model was used for modeling. • Coupling regression was used to weight both the RF-SVR model and the RR-RF model. • The K-S (Kolmogorov-Smirnov) based on cumulative distribution function was used to test the significance of the model. Electric load forecasting has a great impact on dispatching work and production scheme of power system. And accurate forecasting is helpful to the security and stability of power system. This paper proposes a hybrid model based on ensemble empirical mode decomposition (EEMD), random forest (RF), support vector regression (SVR) and ridge regression (RR) algorithm, namely EEMD-RF- SVR-RR model. EEMD is employed to solve the problem of data fluctuation. RF, SVR, and RR make the model have strong anti-noise ability, nonlinear mapping and stability. Numerical experiments were carried out with New South Wales(NSW, Australia). The results have showed that the forecasting accuracy of the model for different types in this paper is better than other models. So the feasibility and effectiveness of this method in short-term load forecasting are verified. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Electric Power Systems Research is the property of Elsevier B.V. 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: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.epsr.2022.108759 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Load forecasting (Electric power systems) Type: general – SubjectFull: Hilbert-Huang transform Type: general – SubjectFull: Cumulative distribution function Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Statistical hypothesis testing Type: general – SubjectFull: New South Wales Type: general Titles: – TitleFull: The new hybrid approaches to forecasting short-term electricity load. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fan, Guo-Feng – PersonEntity: Name: NameFull: Liu, Yan-Rong – PersonEntity: Name: NameFull: Wei, Hui-Zhen – PersonEntity: Name: NameFull: Yu, Meng – PersonEntity: Name: NameFull: Li, Yin-He IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 03787796 Numbering: – Type: volume Value: 213 Titles: – TitleFull: Electric Power Systems Research Type: main |
| ResultId | 1 |
