STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting.
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| Title: | STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting. |
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| Authors: | Tian, Chenyu1 (AUTHOR), Xia, Min1,2 (AUTHOR), Yuan, Shi1 (AUTHOR), Wang, Liwen2 (AUTHOR), Zhuang, Wei1 (AUTHOR) zw@nuist.edu.cn |
| Source: | Energies (19961073). Mar2026, Vol. 19 Issue 5, p1214. 17p. |
| Subject Terms: | *Wind forecasting, *Transformer models, *Time series analysis, *Forecasting, *Graph neural networks, *Electric power system reliability |
| Abstract: | The accuracy of short-term wind power forecasting (STWPF) is crucial for the stable operation of power systems. To address the issue of insufficient capture of spatio-temporal dependencies in existing models, which leads to low prediction accuracy, this paper proposes a novel Transformer-based spatio-temporal graph convolutional (STGCformer) model. The time series decomposition module (TSDM) captures periodic fluctuations and long-term variations within the data by performing seasonal trend decomposition. The spatio-temporal graph convolutional (STGC) architecture combines a Graph Attention Network (GAT) with convolutional layers (Convs) to capture both spatial and temporal dependencies, jointly processing the spatio-temporal characteristics inherent in wind power data. The Transformer's attention mechanism simultaneously handles both short-term and long-term fluctuations. Extensive experimental results show that STGCformer achieves the best prediction accuracy across multiple time steps (24, 48, 72, 96 h), with the average absolute error (MAE) and mean absolute percentage error (MAPE) at 48 h being 41.383 and 3.862, respectively. This model provides a new methodological framework for STWPF. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 192640939 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tian%2C+Chenyu%22">Tian, Chenyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Min%22">Xia, Min</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Shi%22">Yuan, Shi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Liwen%22">Wang, Liwen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhuang%2C+Wei%22">Zhuang, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zw@nuist.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 5, p1214. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br />*<searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+reliability%22">Electric power system reliability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The accuracy of short-term wind power forecasting (STWPF) is crucial for the stable operation of power systems. To address the issue of insufficient capture of spatio-temporal dependencies in existing models, which leads to low prediction accuracy, this paper proposes a novel Transformer-based spatio-temporal graph convolutional (STGCformer) model. The time series decomposition module (TSDM) captures periodic fluctuations and long-term variations within the data by performing seasonal trend decomposition. The spatio-temporal graph convolutional (STGC) architecture combines a Graph Attention Network (GAT) with convolutional layers (Convs) to capture both spatial and temporal dependencies, jointly processing the spatio-temporal characteristics inherent in wind power data. The Transformer's attention mechanism simultaneously handles both short-term and long-term fluctuations. Extensive experimental results show that STGCformer achieves the best prediction accuracy across multiple time steps (24, 48, 72, 96 h), with the average absolute error (MAE) and mean absolute percentage error (MAPE) at 48 h being 41.383 and 3.862, respectively. This model provides a new methodological framework for STWPF. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192640939 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19051214 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1214 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Electric power system reliability Type: general Titles: – TitleFull: STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tian, Chenyu – PersonEntity: Name: NameFull: Xia, Min – PersonEntity: Name: NameFull: Yuan, Shi – PersonEntity: Name: NameFull: Wang, Liwen – PersonEntity: Name: NameFull: Zhuang, Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 5 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
