A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework
Key factors influencing photovoltaic (PV) power generation predictions encompass solar radiation, aerosols, sunshine duration, temperature, humidity, wind direction, wind speed, cloud cover, and so on. The various influencing factors exhibit nonlinear correlation correlations, causing high volatilit...
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Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152500211X |
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| author | Mengji Yang Haiqing Zhang Xi Yu Aicha Sekhari Seklouli Abdelaziz Bouras Yacine Ouzrout |
| author_facet | Mengji Yang Haiqing Zhang Xi Yu Aicha Sekhari Seklouli Abdelaziz Bouras Yacine Ouzrout |
| author_sort | Mengji Yang |
| collection | DOAJ |
| description | Key factors influencing photovoltaic (PV) power generation predictions encompass solar radiation, aerosols, sunshine duration, temperature, humidity, wind direction, wind speed, cloud cover, and so on. The various influencing factors exhibit nonlinear correlation correlations, causing high volatility and discreteness in PV power time series. Firstly, to reduce the redundancy of the input for the prediction model and the computational time complexity, while enhancing the robustness and stability of the prediction model, nonlinear correlation search algorithm based on time window extending and time window shrinking strategies have been proposed. Key sequences from nonlinear correlation analysis are used in the next time series prediction model. Afterward, a novel dual-branch architecture that has synthesized the Structured Global Convolution (SGC) and iTransformer branches has been proposed which is called DBSGCformer. This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. We perform comprehensive experiments to investigate DBSGCformer’s potential in tackling complex multivariate time series forecasting challenges. Experiments conducted on two PV power datasets and five additional real-world datasets demonstrate that DBSGCformer significantly improves the accuracy of PV power forecasting and exhibits strong generalizability. |
| format | Article |
| id | doaj-art-c418a94aead545b7b5eaff8ece3ddaba |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-c418a94aead545b7b5eaff8ece3ddaba2025-08-20T02:36:12ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-08-0116911066010.1016/j.ijepes.2025.110660A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning frameworkMengji Yang0Haiqing Zhang1Xi Yu2Aicha Sekhari Seklouli3Abdelaziz Bouras4Yacine Ouzrout5Univ Lyon, Univ Lyon 2, INSA Lyon, Université Claude Bernard Lyon 1, DISP-UR4570, Bron, France; Stirling College, Chengdu University, Chengdu 610106, ChinaSchool of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China; Correspondence to: 24 Xuefu Road, 610225, Chengdu, China.Stirling College, Chengdu University, Chengdu 610106, ChinaUniv Lyon, Univ Lyon 2, INSA Lyon, Université Claude Bernard Lyon 1, DISP-UR4570, Bron, FranceDepartment of Computer Science, Qatar University, Doha, QatarUniv Lyon, Univ Lyon 2, INSA Lyon, Université Claude Bernard Lyon 1, DISP-UR4570, Bron, FranceKey factors influencing photovoltaic (PV) power generation predictions encompass solar radiation, aerosols, sunshine duration, temperature, humidity, wind direction, wind speed, cloud cover, and so on. The various influencing factors exhibit nonlinear correlation correlations, causing high volatility and discreteness in PV power time series. Firstly, to reduce the redundancy of the input for the prediction model and the computational time complexity, while enhancing the robustness and stability of the prediction model, nonlinear correlation search algorithm based on time window extending and time window shrinking strategies have been proposed. Key sequences from nonlinear correlation analysis are used in the next time series prediction model. Afterward, a novel dual-branch architecture that has synthesized the Structured Global Convolution (SGC) and iTransformer branches has been proposed which is called DBSGCformer. This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. We perform comprehensive experiments to investigate DBSGCformer’s potential in tackling complex multivariate time series forecasting challenges. Experiments conducted on two PV power datasets and five additional real-world datasets demonstrate that DBSGCformer significantly improves the accuracy of PV power forecasting and exhibits strong generalizability.http://www.sciencedirect.com/science/article/pii/S014206152500211XPhotovoltaic power predictionNonlinear correlation search algorithmStructured global convolutionHybrid prediction modeliTransformer |
| spellingShingle | Mengji Yang Haiqing Zhang Xi Yu Aicha Sekhari Seklouli Abdelaziz Bouras Yacine Ouzrout A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework International Journal of Electrical Power & Energy Systems Photovoltaic power prediction Nonlinear correlation search algorithm Structured global convolution Hybrid prediction model iTransformer |
| title | A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| title_full | A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| title_fullStr | A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| title_full_unstemmed | A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| title_short | A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| title_sort | composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework |
| topic | Photovoltaic power prediction Nonlinear correlation search algorithm Structured global convolution Hybrid prediction model iTransformer |
| url | http://www.sciencedirect.com/science/article/pii/S014206152500211X |
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