VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).
<p>VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).</p>
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852016264920170496 |
|---|---|
| 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 | 2025-09-25T17:36:41Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0329821.g020 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/VMD-CNN-LSTM_unoptimized_and_VMD-KOA-CNN-LSTM_Comparison_of_the_model_prediction_results_with_the_actual_power_Station_site1_/30211569 |
| 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 (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_566ea38d6784c48e65f306dea01ba01a |
| identifier_str_mv | 10.1371/journal.pone.0329821.g020 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30211569 |
| 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 (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).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<p>VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).</p>2025-09-25T17:36:41ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0329821.g020https://figshare.com/articles/figure/VMD-CNN-LSTM_unoptimized_and_VMD-KOA-CNN-LSTM_Comparison_of_the_model_prediction_results_with_the_actual_power_Station_site1_/30211569CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302115692025-09-25T17:36:41Z |
| spellingShingle | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). 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 (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| title_full | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| title_fullStr | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| title_full_unstemmed | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| title_short | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| title_sort | VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1). |
| 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 |