VMD-CNN-LSTM model architecture.
<div><p>This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable operation of the power system. Inn...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852016264989376512 |
|---|---|
| author | Jiangli Yu (4601986) |
| author2 | Gaoyi Liang (22311429) Lei Wang (6656) Huiyuan He (22311432) Yuxin Liu (505575) Qi Liu (33068) Xinjie Cui (498116) Hao Wang (39217) |
| author2_role | author author author author author author author |
| author_facet | Jiangli Yu (4601986) Gaoyi Liang (22311429) Lei Wang (6656) Huiyuan He (22311432) Yuxin Liu (505575) Qi Liu (33068) Xinjie Cui (498116) Hao Wang (39217) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jiangli Yu (4601986) Gaoyi Liang (22311429) Lei Wang (6656) Huiyuan He (22311432) Yuxin Liu (505575) Qi Liu (33068) Xinjie Cui (498116) Hao Wang (39217) |
| dc.date.none.fl_str_mv | 2025-09-25T17:36:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0329821.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/VMD-CNN-LSTM_model_architecture_/30211512 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified mean absolute error decomposed modal components solid data foundation reduce data complexity photovoltaic power stations photovoltaic power generation term power prediction traditional prediction model lstm combined model kepler optimization algorithm kepler algorithm prediction accuracy data preprocessing power system power changes lstm leverages xlink "> three work subsequent predictions study focuses strongly proves stable operation significant optimization result correction reliable method proposed method innovatively introduce innovative idea grid connection great significance frequency characteristics fluctuating problems evaluation indicators entire process dynamic trends deeply integrated |
| dc.title.none.fl_str_mv | VMD-CNN-LSTM model architecture. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable operation of the power system. Innovatively introduce the Kepler algorithm into this field, deeply analyze historical data, and mine the nonlinear relationships among various factors to lay a solid data foundation for subsequent predictions. The VMD-CNN-LSTM combined model is constructed. It is a model combining variational mode decomposition (VMD), convolutional neural network (CNN) and long short term memory network (LSTM), VMD adaptively decomposes the original power sequence based on frequency characteristics to reduce data complexity. CNN accurately extracts spatial features from the decomposed modal components; LSTM leverages its expertise in processing time series data to capture the dynamic trends of power changes, and the three work in synergy. Meanwhile, the Kepler optimization algorithm (KOA) is deeply integrated with this model to optimize the entire process of the model from data preprocessing to result correction. Verified by examples, compared with the traditional prediction model, the proposed method has significant optimization in evaluation indicators such as root mean square error and mean absolute error, which strongly proves its effectiveness and superiority. It provides an innovative idea and reliable method for the short-term power prediction of photovoltaic power stations and is of great significance for promoting the grid connection of photovoltaic power generation and the optimization of the power system.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_d5f7946d4f8975e6fcdbdce612dba03f |
| identifier_str_mv | 10.1371/journal.pone.0329821.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30211512 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | VMD-CNN-LSTM model architecture.Jiangli Yu (4601986)Gaoyi Liang (22311429)Lei Wang (6656)Huiyuan He (22311432)Yuxin Liu (505575)Qi Liu (33068)Xinjie Cui (498116)Hao Wang (39217)BiotechnologyPlant BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedmean absolute errordecomposed modal componentssolid data foundationreduce data complexityphotovoltaic power stationsphotovoltaic power generationterm power predictiontraditional prediction modellstm combined modelkepler optimization algorithmkepler algorithmprediction accuracydata preprocessingpower systempower changeslstm leveragesxlink ">three worksubsequent predictionsstudy focusesstrongly provesstable operationsignificant optimizationresult correctionreliable methodproposed methodinnovatively introduceinnovative ideagrid connectiongreat significancefrequency characteristicsfluctuating problemsevaluation indicatorsentire processdynamic trendsdeeply integrated<div><p>This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable operation of the power system. Innovatively introduce the Kepler algorithm into this field, deeply analyze historical data, and mine the nonlinear relationships among various factors to lay a solid data foundation for subsequent predictions. The VMD-CNN-LSTM combined model is constructed. It is a model combining variational mode decomposition (VMD), convolutional neural network (CNN) and long short term memory network (LSTM), VMD adaptively decomposes the original power sequence based on frequency characteristics to reduce data complexity. CNN accurately extracts spatial features from the decomposed modal components; LSTM leverages its expertise in processing time series data to capture the dynamic trends of power changes, and the three work in synergy. Meanwhile, the Kepler optimization algorithm (KOA) is deeply integrated with this model to optimize the entire process of the model from data preprocessing to result correction. Verified by examples, compared with the traditional prediction model, the proposed method has significant optimization in evaluation indicators such as root mean square error and mean absolute error, which strongly proves its effectiveness and superiority. It provides an innovative idea and reliable method for the short-term power prediction of photovoltaic power stations and is of great significance for promoting the grid connection of photovoltaic power generation and the optimization of the power system.</p></div>2025-09-25T17:36:17ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0329821.g002https://figshare.com/articles/figure/VMD-CNN-LSTM_model_architecture_/30211512CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302115122025-09-25T17:36:17Z |
| spellingShingle | VMD-CNN-LSTM model architecture. Jiangli Yu (4601986) Biotechnology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified mean absolute error decomposed modal components solid data foundation reduce data complexity photovoltaic power stations photovoltaic power generation term power prediction traditional prediction model lstm combined model kepler optimization algorithm kepler algorithm prediction accuracy data preprocessing power system power changes lstm leverages xlink "> three work subsequent predictions study focuses strongly proves stable operation significant optimization result correction reliable method proposed method innovatively introduce innovative idea grid connection great significance frequency characteristics fluctuating problems evaluation indicators entire process dynamic trends deeply integrated |
| status_str | publishedVersion |
| title | VMD-CNN-LSTM model architecture. |
| title_full | VMD-CNN-LSTM model architecture. |
| title_fullStr | VMD-CNN-LSTM model architecture. |
| title_full_unstemmed | VMD-CNN-LSTM model architecture. |
| title_short | VMD-CNN-LSTM model architecture. |
| title_sort | VMD-CNN-LSTM model architecture. |
| topic | Biotechnology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified mean absolute error decomposed modal components solid data foundation reduce data complexity photovoltaic power stations photovoltaic power generation term power prediction traditional prediction model lstm combined model kepler optimization algorithm kepler algorithm prediction accuracy data preprocessing power system power changes lstm leverages xlink "> three work subsequent predictions study focuses strongly proves stable operation significant optimization result correction reliable method proposed method innovatively introduce innovative idea grid connection great significance frequency characteristics fluctuating problems evaluation indicators entire process dynamic trends deeply integrated |