An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance.
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| Title: | An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance. |
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| Authors: | Singla, Pardeep1 (AUTHOR) pradeepsingla7@gmail.com, Duhan, Manoj1 (AUTHOR), Saroha, Sumit2 (AUTHOR) |
| Source: | International Journal of Green Energy. 2023, Vol. 20 Issue 10, p1073-1085. 13p. |
| Subject Terms: | Forecasting, Standard deviations, Deep learning |
| Geographic Terms: | Jaipur (India) |
| Abstract: | An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Green Energy is the property of Taylor & Francis Ltd 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.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 164198970 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singla%2C+Pardeep%22">Singla, Pardeep</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pradeepsingla7@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Duhan%2C+Manoj%22">Duhan, Manoj</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saroha%2C+Sumit%22">Saroha, Sumit</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Green+Energy%22">International Journal of Green Energy</searchLink>. 2023, Vol. 20 Issue 10, p1073-1085. 13p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Jaipur+%28India%29%22">Jaipur (India)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Green Energy is the property of Taylor & Francis Ltd 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: 10.1080/15435075.2022.2143272 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1073 Subjects: – SubjectFull: Forecasting Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Jaipur (India) Type: general Titles: – TitleFull: An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singla, Pardeep – PersonEntity: Name: NameFull: Duhan, Manoj – PersonEntity: Name: NameFull: Saroha, Sumit IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 15435075 Numbering: – Type: volume Value: 20 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Green Energy Type: main |
| ResultId | 1 |
