A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM.
Saved in:
| Title: | A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM. |
|---|---|
| Authors: | Çeçen, Mehmet1,2 (AUTHOR), Rivera, Marco2 (AUTHOR), Yavuz, Mehmet3,4 (AUTHOR) mehmet.yavuz@manas.edu.kg |
| Source: | Wind Energy. May2026, Vol. 29 Issue 5, p1-17. 17p. |
| Subjects: | Wind forecasting, Long short-term memory, Meteorological databases, Renewable energy sources, Energy consumption, Machine learning |
| Geographic Terms: | Turkey |
| Abstract: | The global demand for electrical energy continues to rise steadily, with Türkiye experiencing particularly significant growth in energy consumption. To address this increasing demand sustainably, renewable energy sources (RES) have become the primary focus, with wind energy (WE) leading the transition. This study presents a comprehensive case study of wind energy production forecasting for a specific region in Türkiye, utilizing advanced machine learning (ML) methodologies. The study employs long short‐term memory (LSTM) and enhanced attention bidirectional long short‐term memory (EABiLSTM) models to predict wind energy production using real‐time generation data and comprehensive meteorological parameters. The methodology encompasses rigorous data preprocessing techniques, hyperparameter optimization, including normalization, temporal feature engineering, and advanced validation strategies. A comprehensive hourly dataset forms the foundation of this analysis, providing robust temporal coverage for training and validation. Meteorological data are sourced from the NASA Power project, while wind power plant production data are obtained from the Energy Markets Operation Corporation of Türkiye (EPIAS) transparency platform, ensuring reliable generation records. The performance evaluation employs multiple metrics, including mean absolute error (MAE), coefficient of determination (R2$$ {R}^2 $$), and root mean square error (RMSE), to assess forecasting accuracy. The prediction accuracy and reliability of the proposed EABiLSTM method were validated through temporal stability tests, seasonal robustness evaluation, and uncertainty quantification analysis. The effectiveness of the proposed methodologies is demonstrated through comparative analysis between standard LSTM and EABiLSTM models. The regional case study approach provides practical insights for wind energy operators and grid planners, contributing to renewable energy optimization strategies and supporting the country's sustainable energy transition goals. [ABSTRACT FROM AUTHOR] |
| Copyright of Wind Energy is the property of Wiley-Blackwell 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193226020 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Çeçen%2C+Mehmet%22">Çeçen, Mehmet</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rivera%2C+Marco%22">Rivera, Marco</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yavuz%2C+Mehmet%22">Yavuz, Mehmet</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> mehmet.yavuz@manas.edu.kg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wind+Energy%22">Wind Energy</searchLink>. May2026, Vol. 29 Issue 5, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+databases%22">Meteorological databases</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The global demand for electrical energy continues to rise steadily, with Türkiye experiencing particularly significant growth in energy consumption. To address this increasing demand sustainably, renewable energy sources (RES) have become the primary focus, with wind energy (WE) leading the transition. This study presents a comprehensive case study of wind energy production forecasting for a specific region in Türkiye, utilizing advanced machine learning (ML) methodologies. The study employs long short‐term memory (LSTM) and enhanced attention bidirectional long short‐term memory (EABiLSTM) models to predict wind energy production using real‐time generation data and comprehensive meteorological parameters. The methodology encompasses rigorous data preprocessing techniques, hyperparameter optimization, including normalization, temporal feature engineering, and advanced validation strategies. A comprehensive hourly dataset forms the foundation of this analysis, providing robust temporal coverage for training and validation. Meteorological data are sourced from the NASA Power project, while wind power plant production data are obtained from the Energy Markets Operation Corporation of Türkiye (EPIAS) transparency platform, ensuring reliable generation records. The performance evaluation employs multiple metrics, including mean absolute error (MAE), coefficient of determination (R2$$ {R}^2 $$), and root mean square error (RMSE), to assess forecasting accuracy. The prediction accuracy and reliability of the proposed EABiLSTM method were validated through temporal stability tests, seasonal robustness evaluation, and uncertainty quantification analysis. The effectiveness of the proposed methodologies is demonstrated through comparative analysis between standard LSTM and EABiLSTM models. The regional case study approach provides practical insights for wind energy operators and grid planners, contributing to renewable energy optimization strategies and supporting the country's sustainable energy transition goals. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wind Energy is the property of Wiley-Blackwell 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=193226020 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/we.70114 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Meteorological databases Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Turkey Type: general Titles: – TitleFull: A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Çeçen, Mehmet – PersonEntity: Name: NameFull: Rivera, Marco – PersonEntity: Name: NameFull: Yavuz, Mehmet IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10954244 Numbering: – Type: volume Value: 29 – Type: issue Value: 5 Titles: – TitleFull: Wind Energy Type: main |
| ResultId | 1 |
