Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models.
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| Title: | Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models. |
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| Authors: | Seman, Laio Oriel1 (AUTHOR) laioseman@gmail.com, Stefenon, Stefano Frizzo2,3 (AUTHOR) stefano.stefenon@isel.pt, Yow, Kin-Choong3 (AUTHOR) Kin-Choong.Yow@uregina.ca, Coelho, Leandro dos Santos4,5 (AUTHOR) leandro.coelho@ufpr.br, Mariani, Viviana Cocco4,6 (AUTHOR) viviana.mariani@ufpr.br |
| Source: | Renewable Energy: An International Journal. Feb2026, Vol. 257, pN.PAG-N.PAG. 1p. |
| Subject Terms: | *Photovoltaic power systems, *Energy industry forecasting, Forecasting, Long short-term memory, Artificial neural networks, Prediction models, Electric power system management, Pattern recognition systems |
| Abstract: | Accurate short and medium-term forecasting is important for mitigating uncertainty and enabling efficient energy grid management. While traditional machine learning and deep learning models offer improved accuracy, they often lack interpretability. To address these limitations, this study proposes a hybrid forecasting framework, called FNO-BiLSTM-NAM, that combines a Fourier Neural Operator (FNO) to extract spectral–temporal features, a Bidirectional Long Short-Term Memory (BiLSTM) network to model sequential dependencies, and a Neural Additive Model (NAM) to quantify feature-wise contributions. The model incorporates multi-scenario forecasting to support energy operators under different uncertainty levels. Experiments conducted on a dataset from a 5 MW PhotoVoltaic (PV) plant demonstrate the superiority of the model. For a 6-hour forecast horizon, the proposed FNO-BiLSTM-NAM model achieved a mean absolute error of 0.0712 and mean squared error of 0.0092, outperforming benchmark models across short- to medium-term horizons. Furthermore, the spectral analysis of the FNO revealed low-pass filtering behavior, highlighting the ability of the model to suppress high-frequency noise. Comparative experiments with five machine and deep learning baseline models confirm the robustness and generalization capacity of the framework. These results underscore the potential of the proposed model for enhancing PV energy forecasting accuracy while maintaining transparency across dynamic operating conditions. • Innovative preprocessing uses anomaly detection and SHAP feature engineering. • FNO–BiLSTM–NAM delivers highly accurate PV forecasting for a multi-step horizon. • Captures long- and short-term dynamics using a combination of FNO and BiLSTM. • Enables interpretability by isolating and quantifying each feature's contribution. • Outperforms state-of-the-art forecasting methods across multiple error metrics. [ABSTRACT FROM AUTHOR] |
| Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science 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: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 190372841 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Seman%2C+Laio+Oriel%22">Seman, Laio Oriel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> laioseman@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Stefenon%2C+Stefano+Frizzo%22">Stefenon, Stefano Frizzo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> stefano.stefenon@isel.pt</i><br /><searchLink fieldCode="AR" term="%22Yow%2C+Kin-Choong%22">Yow, Kin-Choong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> Kin-Choong.Yow@uregina.ca</i><br /><searchLink fieldCode="AR" term="%22Coelho%2C+Leandro+dos+Santos%22">Coelho, Leandro dos Santos</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> leandro.coelho@ufpr.br</i><br /><searchLink fieldCode="AR" term="%22Mariani%2C+Viviana+Cocco%22">Mariani, Viviana Cocco</searchLink><relatesTo>4,6</relatesTo> (AUTHOR)<i> viviana.mariani@ufpr.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Renewable+Energy%3A+An+International+Journal%22">Renewable Energy: An International Journal</searchLink>. Feb2026, Vol. 257, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</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="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+system+management%22">Electric power system management</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate short and medium-term forecasting is important for mitigating uncertainty and enabling efficient energy grid management. While traditional machine learning and deep learning models offer improved accuracy, they often lack interpretability. To address these limitations, this study proposes a hybrid forecasting framework, called FNO-BiLSTM-NAM, that combines a Fourier Neural Operator (FNO) to extract spectral–temporal features, a Bidirectional Long Short-Term Memory (BiLSTM) network to model sequential dependencies, and a Neural Additive Model (NAM) to quantify feature-wise contributions. The model incorporates multi-scenario forecasting to support energy operators under different uncertainty levels. Experiments conducted on a dataset from a 5 MW PhotoVoltaic (PV) plant demonstrate the superiority of the model. For a 6-hour forecast horizon, the proposed FNO-BiLSTM-NAM model achieved a mean absolute error of 0.0712 and mean squared error of 0.0092, outperforming benchmark models across short- to medium-term horizons. Furthermore, the spectral analysis of the FNO revealed low-pass filtering behavior, highlighting the ability of the model to suppress high-frequency noise. Comparative experiments with five machine and deep learning baseline models confirm the robustness and generalization capacity of the framework. These results underscore the potential of the proposed model for enhancing PV energy forecasting accuracy while maintaining transparency across dynamic operating conditions. • Innovative preprocessing uses anomaly detection and SHAP feature engineering. • FNO–BiLSTM–NAM delivers highly accurate PV forecasting for a multi-step horizon. • Captures long- and short-term dynamics using a combination of FNO and BiLSTM. • Enables interpretability by isolating and quantifying each feature's contribution. • Outperforms state-of-the-art forecasting methods across multiple error metrics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.renene.2025.124738 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Photovoltaic power systems Type: general – SubjectFull: Energy industry forecasting Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Electric power system management Type: general – SubjectFull: Pattern recognition systems Type: general Titles: – TitleFull: Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Seman, Laio Oriel – PersonEntity: Name: NameFull: Stefenon, Stefano Frizzo – PersonEntity: Name: NameFull: Yow, Kin-Choong – PersonEntity: Name: NameFull: Coelho, Leandro dos Santos – PersonEntity: Name: NameFull: Mariani, Viviana Cocco IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09601481 Numbering: – Type: volume Value: 257 Titles: – TitleFull: Renewable Energy: An International Journal Type: main |
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