An Ensemble Model of Attention-Enhanced N-BEATS and XGBoost for District Heating Load Forecasting.
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| Title: | An Ensemble Model of Attention-Enhanced N-BEATS and XGBoost for District Heating Load Forecasting. |
|---|---|
| Authors: | Yu, Shaohua1 (AUTHOR), Yang, Xiaole2 (AUTHOR), Ye, Hengrui2,3 (AUTHOR), Tang, Daogui3,4 (AUTHOR) guerrero@ieee.org, Arasteh, Hamidreza1,4 (AUTHOR) tangdaogui@gmail.com, Guerrero, Josep M.2,4 (AUTHOR) |
| Source: | Energies (19961073). Aug2025, Vol. 18 Issue 15, p3984. 22p. |
| Subjects: | Heating load, Deep learning, Ensemble learning, Energy consumption, Heating from central stations, Machine learning |
| Abstract: | Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R2 of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R2 over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187312636 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Ensemble Model of Attention-Enhanced N-BEATS and XGBoost for District Heating Load Forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Shaohua%22">Yu, Shaohua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xiaole%22">Yang, Xiaole</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Hengrui%22">Ye, Hengrui</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Daogui%22">Tang, Daogui</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> guerrero@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Arasteh%2C+Hamidreza%22">Arasteh, Hamidreza</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> tangdaogui@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Guerrero%2C+Josep+M%2E%22">Guerrero, Josep M.</searchLink><relatesTo>2,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Aug2025, Vol. 18 Issue 15, p3984. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heating+load%22">Heating load</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Heating+from+central+stations%22">Heating from central stations</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R2 of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R2 over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI 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: 10.3390/en18153984 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3984 Subjects: – SubjectFull: Heating load Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Heating from central stations Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: An Ensemble Model of Attention-Enhanced N-BEATS and XGBoost for District Heating Load Forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Shaohua – PersonEntity: Name: NameFull: Yang, Xiaole – PersonEntity: Name: NameFull: Ye, Hengrui – PersonEntity: Name: NameFull: Tang, Daogui – PersonEntity: Name: NameFull: Arasteh, Hamidreza – PersonEntity: Name: NameFull: Guerrero, Josep M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 15 Titles: – TitleFull: Energies (19961073) Type: main |
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