A CPO-Optimized BiTCN–BiGRU–Attention Network for Short-Term Wind Power Forecasting.
Saved in:
| Title: | A CPO-Optimized BiTCN–BiGRU–Attention Network for Short-Term Wind Power Forecasting. |
|---|---|
| Authors: | Huang, Liusong1,2 (AUTHOR), Jaharadak, Adam Amril bin2,3 (AUTHOR) adam@msu.edu.my, Ahmad, Nor Izzati3 (AUTHOR), Wang, Jie1 (AUTHOR) |
| Source: | Energies (19961073). Feb2026, Vol. 19 Issue 4, p1034. 19p. |
| Subject Terms: | *Wind forecasting, *Optimization algorithms, *Electric power system stability, *Deep learning, *Forecasting, *Recurrent neural networks |
| Abstract: | Short-term wind power prediction is pivotal for maintaining the stability of power grids characterized by high renewable energy penetration. However, wind power time series exhibit complex characteristics, including local turbulence-induced fluctuations and long-term temporal dependencies, which challenge traditional forecasting models. Furthermore, the performance of hybrid deep learning models is often compromised by the difficulty of tuning hyperparameters over non-convex optimization surfaces. To address these challenges, this study proposes a novel framework: CPO—BiTCN—BiGRU—Attention. Adopting a physically motivated "Filter–Memorize–Focus" strategy, the model first employs a Bidirectional Temporal Convolutional Network (BiTCN) with dilated causal convolutions to extract multi-scale local features and denoise raw data. Subsequently, a Bidirectional Gated Recurrent Unit (BiGRU) captures global temporal evolution, while an attention mechanism dynamically weights critical time steps corresponding to ramp events. To mitigate hyperparameter uncertainty, the Crowned Porcupine Optimization (CPO) algorithm is introduced to adaptively tune the network structure, balancing global exploration and local exploitation more effectively than traditional swarm algorithms. Experimental results obtained from real-world wind farm data in Xinjiang, China, demonstrate that the proposed model consistently outperforms State-of-the-Art benchmark models. Compared with the best competing methods, the proposed framework reduces MAE and MAPE by approximately 30–45%, while maintaining competitive RMSE performance, indicating improved average forecasting accuracy and robustness under varying operating conditions. The results confirm that the proposed architecture effectively decouples local noise from global trends, providing a robust and practical solution for short-term wind power forecasting in grid dispatching applications. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 191973454 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A CPO-Optimized BiTCN–BiGRU–Attention Network for Short-Term Wind Power Forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Liusong%22">Huang, Liusong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jaharadak%2C+Adam+Amril+bin%22">Jaharadak, Adam Amril bin</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> adam@msu.edu.my</i><br /><searchLink fieldCode="AR" term="%22Ahmad%2C+Nor+Izzati%22">Ahmad, Nor Izzati</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jie%22">Wang, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Feb2026, Vol. 19 Issue 4, p1034. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+stability%22">Electric power system stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Short-term wind power prediction is pivotal for maintaining the stability of power grids characterized by high renewable energy penetration. However, wind power time series exhibit complex characteristics, including local turbulence-induced fluctuations and long-term temporal dependencies, which challenge traditional forecasting models. Furthermore, the performance of hybrid deep learning models is often compromised by the difficulty of tuning hyperparameters over non-convex optimization surfaces. To address these challenges, this study proposes a novel framework: CPO—BiTCN—BiGRU—Attention. Adopting a physically motivated "Filter–Memorize–Focus" strategy, the model first employs a Bidirectional Temporal Convolutional Network (BiTCN) with dilated causal convolutions to extract multi-scale local features and denoise raw data. Subsequently, a Bidirectional Gated Recurrent Unit (BiGRU) captures global temporal evolution, while an attention mechanism dynamically weights critical time steps corresponding to ramp events. To mitigate hyperparameter uncertainty, the Crowned Porcupine Optimization (CPO) algorithm is introduced to adaptively tune the network structure, balancing global exploration and local exploitation more effectively than traditional swarm algorithms. Experimental results obtained from real-world wind farm data in Xinjiang, China, demonstrate that the proposed model consistently outperforms State-of-the-Art benchmark models. Compared with the best competing methods, the proposed framework reduces MAE and MAPE by approximately 30–45%, while maintaining competitive RMSE performance, indicating improved average forecasting accuracy and robustness under varying operating conditions. The results confirm that the proposed architecture effectively decouples local noise from global trends, providing a robust and practical solution for short-term wind power forecasting in grid dispatching applications. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191973454 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19041034 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1034 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Electric power system stability Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Recurrent neural networks Type: general Titles: – TitleFull: A CPO-Optimized BiTCN–BiGRU–Attention Network for Short-Term Wind Power Forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Liusong – PersonEntity: Name: NameFull: Jaharadak, Adam Amril bin – PersonEntity: Name: NameFull: Ahmad, Nor Izzati – PersonEntity: Name: NameFull: Wang, Jie IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 4 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
