Academic Journal
An Integrative Hybrid Feature Fusion Framework Using XGBoost--BiLSTM for Enhanced Solar Power Forecasting Accuracy.
| Τίτλος: | An Integrative Hybrid Feature Fusion Framework Using XGBoost--BiLSTM for Enhanced Solar Power Forecasting Accuracy. |
|---|---|
| Συγγραφείς: | Rane, Digambar, Chaudhari, Sachin |
| Πηγή: | International Journal of Computer Information Systems & Industrial Management Applications; 2026 Supplement, Vol. 18, p1346-1366, 21p |
| Θεματικοί όροι: | Long short-term memory, Energy industry forecasting, Forecasting, Ensemble learning, Machine learning, Statistical accuracy |
| Περίληψη: | This paper proposes an Integrative Hybrid Feature Fusion Framework that integrates the temporal sequence learning and the structural data processing capacity of Bidirectional Long Short-Term Memory (BiLSTM) network and eXtreme Gradient Boosting (XGBoost), respectively. The proposed approach begins with 24 hours sliding window strategy and robust data pretreatment of solar energy terms including ambient temperature, wind speed, and Global Horizontal Irradiance (GHI). As a result of the architectures, the preprocessed data is split into two parallel blocks. The XGBoost branch implements dense projection, leaf index embedding, and tree-based modeling to produce a 64-dimensional feature vector that can capture intricate feature interactions, even in their non-linear form. During this process, the deep temporal relationship between sequential data is extracted by the BiLSTM branch and then a 64-dimensional context vector is output. The representations are then combined into a 128-dimensional representation and input to a fully connected fusion prediction head, yielding the final normalized regression output. The inverse transformation is then applied to produce the system prediction of the final Direct Current (DC) power (kW) which is fully assessed with the use of a number of standard error measures. Inspired by the complementary strengths of both approaches, the framework presents a hybrid method that yields a significant improvement on the forecast accuracy of solar power. Taking advantage of the complementary attributes of both approaches, the structure is adapted into a hybrid method which effectively merges static structural abstractions with dynamic temporal conditions, resulting in improved forecast accuracy of solar power. All the suggested models had excellent predicting accuracy with the Mean Absolute Error (MAE) of 0.912 KW, Mean Squared Error (MSE) of 2.579 KW, Root Mean Squared Error (RMSE) of 1.643 KW, and Mean Absolute Percentage Error (MAPE) of 1.948%. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Information Systems & Industrial Management Applications is the property of Cerebration Science Publishing Co., Limited 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 195446266 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06616210938 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Integrative Hybrid Feature Fusion Framework Using XGBoost--BiLSTM for Enhanced Solar Power Forecasting Accuracy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rane%2C+Digambar%22">Rane, Digambar</searchLink><br /><searchLink fieldCode="AR" term="%22Chaudhari%2C+Sachin%22">Chaudhari, Sachin</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Computer Information Systems & Industrial Management Applications; 2026 Supplement, Vol. 18, p1346-1366, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+industry+forecasting%22">Energy industry forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes an Integrative Hybrid Feature Fusion Framework that integrates the temporal sequence learning and the structural data processing capacity of Bidirectional Long Short-Term Memory (BiLSTM) network and eXtreme Gradient Boosting (XGBoost), respectively. The proposed approach begins with 24 hours sliding window strategy and robust data pretreatment of solar energy terms including ambient temperature, wind speed, and Global Horizontal Irradiance (GHI). As a result of the architectures, the preprocessed data is split into two parallel blocks. The XGBoost branch implements dense projection, leaf index embedding, and tree-based modeling to produce a 64-dimensional feature vector that can capture intricate feature interactions, even in their non-linear form. During this process, the deep temporal relationship between sequential data is extracted by the BiLSTM branch and then a 64-dimensional context vector is output. The representations are then combined into a 128-dimensional representation and input to a fully connected fusion prediction head, yielding the final normalized regression output. The inverse transformation is then applied to produce the system prediction of the final Direct Current (DC) power (kW) which is fully assessed with the use of a number of standard error measures. Inspired by the complementary strengths of both approaches, the framework presents a hybrid method that yields a significant improvement on the forecast accuracy of solar power. Taking advantage of the complementary attributes of both approaches, the structure is adapted into a hybrid method which effectively merges static structural abstractions with dynamic temporal conditions, resulting in improved forecast accuracy of solar power. All the suggested models had excellent predicting accuracy with the Mean Absolute Error (MAE) of 0.912 KW, Mean Squared Error (MSE) of 2.579 KW, Root Mean Squared Error (RMSE) of 1.643 KW, and Mean Absolute Percentage Error (MAPE) of 1.948%. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Computer Information Systems & Industrial Management Applications is the property of Cerebration Science Publishing Co., Limited 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: 10.70917/ijcisim-2026-2466 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1346 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Energy industry forecasting Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Statistical accuracy Type: general Titles: – TitleFull: An Integrative Hybrid Feature Fusion Framework Using XGBoost--BiLSTM for Enhanced Solar Power Forecasting Accuracy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rane, Digambar – PersonEntity: Name: NameFull: Chaudhari, Sachin IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 01 Text: 2026 Supplement Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21507988 Numbering: – Type: volume Value: 18 Titles: – TitleFull: International Journal of Computer Information Systems & Industrial Management Applications Type: main |
| ResultId | 1 |