A Comparative Study of Day-Ahead Wind Power Forecasting Models for a Single Wind Farm Under Strict Chronological Splitting and Unified Hyperparameter Tuning Conditions.
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| Title: | A Comparative Study of Day-Ahead Wind Power Forecasting Models for a Single Wind Farm Under Strict Chronological Splitting and Unified Hyperparameter Tuning Conditions. |
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| Authors: | Liu, Jiacheng1 (AUTHOR), Lu, Yihang2 (AUTHOR) luyh@zjweu.edu.cn, Zou, Guoping1 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2784. 31p. |
| Subject Terms: | *Wind forecasting, *Wind power plants, *Machine learning, *Benchmarking (Management), *Forecasting |
| Abstract: | Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194909233 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comparative Study of Day-Ahead Wind Power Forecasting Models for a Single Wind Farm Under Strict Chronological Splitting and Unified Hyperparameter Tuning Conditions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jiacheng%22">Liu, Jiacheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Yihang%22">Lu, Yihang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luyh@zjweu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Guoping%22">Zou, Guoping</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2784. 31p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Benchmarking+%28Management%29%22">Benchmarking (Management)</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122784 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 2784 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Wind power plants Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Benchmarking (Management) Type: general – SubjectFull: Forecasting Type: general Titles: – TitleFull: A Comparative Study of Day-Ahead Wind Power Forecasting Models for a Single Wind Farm Under Strict Chronological Splitting and Unified Hyperparameter Tuning Conditions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Jiacheng – PersonEntity: Name: NameFull: Lu, Yihang – PersonEntity: Name: NameFull: Zou, Guoping IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 12 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
