Flowchart of algorithm principles.
<div><p>The accurate prediction of wind power is imperative for maintaining grid stability. In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. However, due to insufficient exp...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852017216265912320 |
|---|---|
| author | Wenjuan Zhou (115285) |
| author2 | Feng Huang (62988) Bing Wei (2588902) Liang Li (46069) Shixi Dai (22138931) Xin Xie (311744) Youyuan Peng (20862463) Hong You (251402) |
| author2_role | author author author author author author author |
| author_facet | Wenjuan Zhou (115285) Feng Huang (62988) Bing Wei (2588902) Liang Li (46069) Shixi Dai (22138931) Xin Xie (311744) Youyuan Peng (20862463) Hong You (251402) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wenjuan Zhou (115285) Feng Huang (62988) Bing Wei (2588902) Liang Li (46069) Shixi Dai (22138931) Xin Xie (311744) Youyuan Peng (20862463) Hong You (251402) |
| dc.date.none.fl_str_mv | 2025-08-28T17:28:56Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330464.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Flowchart_of_algorithm_principles_/30003764 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reduce computation time maintaining grid stability experimental results demonstrate connected neural network computational efficiency deteriorate complex working conditions characterizes equipment performance blade deflection angle hidden coupling model delivering higher accuracy wind turbine operation dynamic synergistic coefficient higher prediction accuracy enhance prediction accuracy traditional informer model informer model based wind turbine data screen key multi power prediction experiments wind power prediction prediction accuracy informer model wind power wind speed prediction framework accurate prediction maintenance data informer ). dynamic correlations power output xlink "> study proposes source features new design lasso algorithm insufficient exploration health matrix health assessment assessment yields |
| dc.title.none.fl_str_mv | Flowchart of algorithm principles. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The accurate prediction of wind power is imperative for maintaining grid stability. In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. However, due to insufficient exploration of dynamic coupling among multi-source features and inadequate data health status perception, both prediction accuracy and computational efficiency deteriorate under complex working conditions.This study proposes a prediction framework for the Informer model based on multi-source feature interaction optimization (MFIO-Informer). Integrating physical feature collaborative analysis with data health status perception has been shown to enhance prediction accuracy and reduce computation time. First, the Lasso algorithm and Pearson correlation coefficient method are applied to screen key multi-source features from wind turbine operation and maintenance data, quantifying their dynamic correlations with power output. Secondly, a fully-connected neural network (FNN) is employed to establish a hidden coupling model of wind speed, blade deflection angle, and power for extracting the Dynamic Synergistic Coefficient (DSC), which characterizes equipment performance. Subsequently, a health assessment of wind turbine data is conducted, leveraging historical power data and DSC. This assessment yields a health matrix, which is instrumental in optimizing the encoding, decoding, and embedding vector prediction processes of the Informer model. Finally, power prediction experiments are conducted on two public wind power datasets using the proposed MFIO-Informer model.The experimental results demonstrate that, in comparison with the traditional Informer model, the MFIO-Informer model attains approximately 20% higher prediction accuracy and 54.85% faster prediction speed.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_623e593f327ad3de2575b5e4316b2fa6 |
| identifier_str_mv | 10.1371/journal.pone.0330464.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30003764 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Flowchart of algorithm principles.Wenjuan Zhou (115285)Feng Huang (62988)Bing Wei (2588902)Liang Li (46069)Shixi Dai (22138931)Xin Xie (311744)Youyuan Peng (20862463)Hong You (251402)BiotechnologyAstronomical and Space Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedreduce computation timemaintaining grid stabilityexperimental results demonstrateconnected neural networkcomputational efficiency deterioratecomplex working conditionscharacterizes equipment performanceblade deflection anglehidden coupling modeldelivering higher accuracywind turbine operationdynamic synergistic coefficienthigher prediction accuracyenhance prediction accuracytraditional informer modelinformer model basedwind turbine datascreen key multipower prediction experimentswind power predictionprediction accuracyinformer modelwind powerwind speedprediction frameworkaccurate predictionmaintenance datainformer ).dynamic correlationspower outputxlink ">study proposessource featuresnew designlasso algorithminsufficient explorationhealth matrixhealth assessmentassessment yields<div><p>The accurate prediction of wind power is imperative for maintaining grid stability. In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. However, due to insufficient exploration of dynamic coupling among multi-source features and inadequate data health status perception, both prediction accuracy and computational efficiency deteriorate under complex working conditions.This study proposes a prediction framework for the Informer model based on multi-source feature interaction optimization (MFIO-Informer). Integrating physical feature collaborative analysis with data health status perception has been shown to enhance prediction accuracy and reduce computation time. First, the Lasso algorithm and Pearson correlation coefficient method are applied to screen key multi-source features from wind turbine operation and maintenance data, quantifying their dynamic correlations with power output. Secondly, a fully-connected neural network (FNN) is employed to establish a hidden coupling model of wind speed, blade deflection angle, and power for extracting the Dynamic Synergistic Coefficient (DSC), which characterizes equipment performance. Subsequently, a health assessment of wind turbine data is conducted, leveraging historical power data and DSC. This assessment yields a health matrix, which is instrumental in optimizing the encoding, decoding, and embedding vector prediction processes of the Informer model. Finally, power prediction experiments are conducted on two public wind power datasets using the proposed MFIO-Informer model.The experimental results demonstrate that, in comparison with the traditional Informer model, the MFIO-Informer model attains approximately 20% higher prediction accuracy and 54.85% faster prediction speed.</p></div>2025-08-28T17:28:56ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0330464.g001https://figshare.com/articles/figure/Flowchart_of_algorithm_principles_/30003764CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300037642025-08-28T17:28:56Z |
| spellingShingle | Flowchart of algorithm principles. Wenjuan Zhou (115285) Biotechnology Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reduce computation time maintaining grid stability experimental results demonstrate connected neural network computational efficiency deteriorate complex working conditions characterizes equipment performance blade deflection angle hidden coupling model delivering higher accuracy wind turbine operation dynamic synergistic coefficient higher prediction accuracy enhance prediction accuracy traditional informer model informer model based wind turbine data screen key multi power prediction experiments wind power prediction prediction accuracy informer model wind power wind speed prediction framework accurate prediction maintenance data informer ). dynamic correlations power output xlink "> study proposes source features new design lasso algorithm insufficient exploration health matrix health assessment assessment yields |
| status_str | publishedVersion |
| title | Flowchart of algorithm principles. |
| title_full | Flowchart of algorithm principles. |
| title_fullStr | Flowchart of algorithm principles. |
| title_full_unstemmed | Flowchart of algorithm principles. |
| title_short | Flowchart of algorithm principles. |
| title_sort | Flowchart of algorithm principles. |
| topic | Biotechnology Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reduce computation time maintaining grid stability experimental results demonstrate connected neural network computational efficiency deteriorate complex working conditions characterizes equipment performance blade deflection angle hidden coupling model delivering higher accuracy wind turbine operation dynamic synergistic coefficient higher prediction accuracy enhance prediction accuracy traditional informer model informer model based wind turbine data screen key multi power prediction experiments wind power prediction prediction accuracy informer model wind power wind speed prediction framework accurate prediction maintenance data informer ). dynamic correlations power output xlink "> study proposes source features new design lasso algorithm insufficient exploration health matrix health assessment assessment yields |