PV and Load Forecasting for Photovoltaic Communities Based on a Hybrid Model of LSTM and Transformer
This paper investigates the PV and load forecasting problem for PV communities. A construction of a PV and load forecasting model based on a hybrid model of LSTM and Transformer is proposed. The multi-attention mechanism of Transformer is added to the traditional neural network LSTM model. This hybr...
Uloženo v:
| Vydáno v: | IEEE International Conference on Industrial Technology (Online) s. 1 - 6 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
26.03.2025
|
| Témata: | |
| ISSN: | 2643-2978 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper investigates the PV and load forecasting problem for PV communities. A construction of a PV and load forecasting model based on a hybrid model of LSTM and Transformer is proposed. The multi-attention mechanism of Transformer is added to the traditional neural network LSTM model. This hybrid model optimizes the original data according to the PV and load time series characteristics. The weighted mean square error method is used to design the loss function. In order to prevent model overfitting and improve stability, Adam optimizer and regularization coefficient is introduced. In order to verify the superiority of the model, machine learning and traditional neural network method comparison experiments are carried out in this paper. The experimental results show that the model designed in this paper has the smallest error between the predicted value and the true value. It shows that the model with the introduction of Transformer's multi-attention mechanism in LSTM model can improve the prediction accuracy. |
|---|---|
| AbstractList | This paper investigates the PV and load forecasting problem for PV communities. A construction of a PV and load forecasting model based on a hybrid model of LSTM and Transformer is proposed. The multi-attention mechanism of Transformer is added to the traditional neural network LSTM model. This hybrid model optimizes the original data according to the PV and load time series characteristics. The weighted mean square error method is used to design the loss function. In order to prevent model overfitting and improve stability, Adam optimizer and regularization coefficient is introduced. In order to verify the superiority of the model, machine learning and traditional neural network method comparison experiments are carried out in this paper. The experimental results show that the model designed in this paper has the smallest error between the predicted value and the true value. It shows that the model with the introduction of Transformer's multi-attention mechanism in LSTM model can improve the prediction accuracy. |
| Author | Xu, Chang Li, Xiang Xiao, Zhongyuan Zhang, Xing Wang, Qingyi Cen, Jinglong |
| Author_xml | – sequence: 1 givenname: Jinglong surname: Cen fullname: Cen, Jinglong email: 1350085142@cug.edu.cn organization: School of Automation, China University of Geosciences,Wuhan,China – sequence: 2 givenname: Xing surname: Zhang fullname: Zhang, Xing email: xingzhang2917@163.com organization: Wuhan Second Ship Design & Res Inst,Wuhan,China – sequence: 3 givenname: Qingyi surname: Wang fullname: Wang, Qingyi email: wangqingyi@cug.edu.cn organization: School of Automation, China University of Geosciences,Wuhan,China – sequence: 4 givenname: Zhongyuan surname: Xiao fullname: Xiao, Zhongyuan email: 1228078176@qq.com organization: School of Automation, China University of Geosciences,Wuhan,China – sequence: 5 givenname: Chang surname: Xu fullname: Xu, Chang email: 2380411738@qq.com organization: School of Automation, China University of Geosciences,Wuhan,China – sequence: 6 givenname: Xiang surname: Li fullname: Li, Xiang email: lx_utop@cug.edu.cn organization: School of Automation, China University of Geosciences,Wuhan,China |
| BookMark | eNo10N9KwzAYBfAoCs65NxDMC3Sm-ZpkudTi3KDDgcXbkTRfNLImklZhb-_wz9WBc_HjcC7JWUwRCbkp2bwsmb5d1-tWggQ154yL-bGSArg4ITOt9AKgFLzUgp2SCZcVFFyrxQWZDcM7Yww4g6qSE-K2L9RER5tkHF2mjJ0ZxhBfqU-Zbt_SmL7SfjSho3Xq-88YxoADvTcDOpoiNXR1sDk4ukkO9zR52jy3mx-xzSYOR6XHfEXOvdkPOPvLKWmXD229Kpqnx3V91xRBw1hUvEN0bsFUxf1xudCguLBcOGUdl1h10nlr0RuLUnknvAXHkYEoAUvsYEquf9mAiLuPHHqTD7v_X-AbpllaGg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICIT63637.2025.10965325 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798331521950 |
| EISSN | 2643-2978 |
| EndPage | 6 |
| ExternalDocumentID | 10965325 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i93t-42ceedd80742f195593725b25d7bd26e4c6dfbbefabe67fd5fb3d2e03513e1ec3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Apr 30 05:50:36 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-42ceedd80742f195593725b25d7bd26e4c6dfbbefabe67fd5fb3d2e03513e1ec3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10965325 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-March-26 |
| PublicationDateYYYYMMDD | 2025-03-26 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-March-26 day: 26 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE International Conference on Industrial Technology (Online) |
| PublicationTitleAbbrev | ICIT |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003203446 |
| Score | 1.9033605 |
| Snippet | This paper investigates the PV and load forecasting problem for PV communities. A construction of a PV and load forecasting model based on a hybrid model of... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Attention mechanisms Correlation Data models Load forecasting Load modeling Long short term memory multiple attention mechanisms Neural networks prediction Predictive models time Series Time series analysis Transformers |
| Title | PV and Load Forecasting for Photovoltaic Communities Based on a Hybrid Model of LSTM and Transformer |
| URI | https://ieeexplore.ieee.org/document/10965325 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA62eNCLr4pvcvC6tU02SfdqsbRQy4JL6a3kMcGC7Eq7Ffz3ZrbbqgcP3kJIQkiYfJlkvm8IuVc6-FUBKSMV8Cg4KNJEWggb2WBXiRYdYysi7XSsJpPebJakNVm94sIAQBV8Bm0sVn_5rrBrfCoLFp5IwZlokIZSakPW2j2ocIbqdbKO4QpNH0b9USa55Cq4gUy0t71_5VGpYGRw9M8JHJPWNyGPpjuoOSF7kJ-Swx9agmfEpVOqc0fHhXYUE25avcKQZhpupTR9LcoiHESlXlhac0JQSZU-BhBztMippsNPJG9RTI72RgtPxy_ZczVitr3bwrJFssFT1h9GdQqFaJHwMooZTsyh4A3zXRSb44oJw4RTxjEJsZXOGwNeG5DKO-ENdwzwd5FDFyw_J828yOGCUB3gLlisj4W3MQhtuOeyw5MAgkmPeX5JWrhe8_eNSMZ8u1RXf9RfkwPcFQznYvKGNMvlGm7Jvv0oF6vlXbW1X6Lmo_I |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5aBfXiq-LbOXjd2iabbPdqsbS4LQWX0lvJEwuyK-1W8N-b2W6rHjx4CyEJIWHyZZL5viHkPpLer_JIGUQej7yDIlQgOdeB9nYVS95UuiTSjpNoOGxPJvGoIquXXBhrbRl8ZhtYLP_yTa6X-FTmLTwWnFG-TXZ4GNLWiq61eVJhFPXrRBXF5Rs_9Dv9VDDBIu8IUt5Y9_-VSaUEku7hP6dwROrflDwYbcDmmGzZ7IQc_FATPCVmNAaZGUhyaQBTbmq5wKBm8PdSGL3mRe6PokLONFSsENRShUcPYwbyDCT0PpG-BZge7Q1yB8lLOihHTNe3Wzuvk7T7lHZ6QZVEIZjFrAhCihMzKHlDXQvl5lhEuaLcRMpQYUMtjFPKOqmsiJzhTjFDLf4vMtuymp2RWpZn9pyA9IDnbdaF3OnQcqmYY6LJYg-DcZs6dkHquF7T95VMxnS9VJd_1N-RvV46SKZJf_h8RfZxhzC4i4prUivmS3tDdvVHMVvMb8tt_gJYDKc5 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+International+Conference+on+Industrial+Technology+%28Online%29&rft.atitle=PV+and+Load+Forecasting+for+Photovoltaic+Communities+Based+on+a+Hybrid+Model+of+LSTM+and+Transformer&rft.au=Cen%2C+Jinglong&rft.au=Zhang%2C+Xing&rft.au=Wang%2C+Qingyi&rft.au=Xiao%2C+Zhongyuan&rft.date=2025-03-26&rft.pub=IEEE&rft.eissn=2643-2978&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICIT63637.2025.10965325&rft.externalDocID=10965325 |