Optimized wind power prediction and energy storage scheduling using genetic algorithm and backpropagation neural network
As renewable energy continues to rise in the global energy mix, wind energy is gradually increasing its share in the power system as a clean, renewable form of energy. However, the volatility and uncertainty of wind power bring new challenges to power system operation, making the need for its effici...
Uložené v:
| Vydané v: | International journal of renewable energy development Ročník 14; číslo 1; s. 146 - 157 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Diponegoro University
01.01.2025
|
| Predmet: | |
| ISSN: | 2252-4940 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | As renewable energy continues to rise in the global energy mix, wind energy is gradually increasing its share in the power system as a clean, renewable form of energy. However, the volatility and uncertainty of wind power bring new challenges to power system operation, making the need for its efficient prediction and intelligent dispatch more and more urgent. Based on this, a method combining genetic algorithm and backpropagation neural network is proposed for wind power prediction and energy storage scheduling. In this study, the improved genetic algorithm-backpropagation algorithm was generated by optimizing the weights and thresholds of the backpropagation neural network through the genetic algorithm, and optimizing the crossover and mutation processes of the genetic algorithm using similar block-order single-point crossover operator and shift mutation operator. Moreover, the improved genetic algorithm-backpropagation Neural Network wind energy prediction model was successfully constructed. Subsequently, the improved genetic algorithm was applied to search for the parameters of support vector machine and an improved genetic algorithm-support vector machine photovoltaic power generation prediction model was established. The experimental results showed that the average absolute percentage error of the improved genetic algorithm backpropagation neural network algorithm was 2.4%, and the accuracy was significantly higher than that of the traditional backpropagation neural network algorithm. The maximum photovoltaic prediction error of the autoregressive integral moving average model was about 80MW, while the photovoltaic prediction error of the improved genetic algorithm support vector machine photovoltaic prediction model was only about 12kW. In addition, the average absolute percentage error of the improved genetic algorithm support vector machine photovoltaic prediction model was only 1.53%, which was only 0.2% higher than the support vector machine prediction model. This study not only improves the stability of the power grid but also provides a practical and feasible method for realizing the large-scale application of clean energy, making a positive contribution to the sustainable development of the energy industry. |
|---|---|
| AbstractList | As renewable energy continues to rise in the global energy mix, wind energy is gradually increasing its share in the power system as a clean, renewable form of energy. However, the volatility and uncertainty of wind power bring new challenges to power system operation, making the need for its efficient prediction and intelligent dispatch more and more urgent. Based on this, a method combining genetic algorithm and backpropagation neural network is proposed for wind power prediction and energy storage scheduling. In this study, the improved genetic algorithm-backpropagation algorithm was generated by optimizing the weights and thresholds of the backpropagation neural network through the genetic algorithm, and optimizing the crossover and mutation processes of the genetic algorithm using similar block-order single-point crossover operator and shift mutation operator. Moreover, the improved genetic algorithm-backpropagation Neural Network wind energy prediction model was successfully constructed. Subsequently, the improved genetic algorithm was applied to search for the parameters of support vector machine and an improved genetic algorithm-support vector machine photovoltaic power generation prediction model was established. The experimental results showed that the average absolute percentage error of the improved genetic algorithm backpropagation neural network algorithm was 2.4%, and the accuracy was significantly higher than that of the traditional backpropagation neural network algorithm. The maximum photovoltaic prediction error of the autoregressive integral moving average model was about 80MW, while the photovoltaic prediction error of the improved genetic algorithm support vector machine photovoltaic prediction model was only about 12kW. In addition, the average absolute percentage error of the improved genetic algorithm support vector machine photovoltaic prediction model was only 1.53%, which was only 0.2% higher than the support vector machine prediction model. This study not only improves the stability of the power grid but also provides a practical and feasible method for realizing the large-scale application of clean energy, making a positive contribution to the sustainable development of the energy industry. |
| Author | Li, Zongze Wu, Peng |
| Author_xml | – sequence: 1 givenname: Peng orcidid: 0009-0006-2843-5411 surname: Wu fullname: Wu, Peng organization: Hebei Suntien New Energy Technology Co., Ltd., Zhangjiakou 075000, China – sequence: 2 givenname: Zongze orcidid: 0009-0000-3020-8097 surname: Li fullname: Li, Zongze organization: Hebei Suntien New Energy Technology Co., Ltd., Zhangjiakou 075000, China |
| BookMark | eNo9kMtuwjAQRb2gUinlA7rLD0D9TrKsUB9ISGzYWxNnEgwhjuwgSr--IVTdzJWuZo5G54lMWt8iIS-MLjWTQr26Q8ByySlXS02lkBMy5VzxhcwlfSTzGA-UUiYky6Weku9t17uT-8Eyubi2TDp_wZB0A8LZ3vk2gaHEFkN9TWLvA9SYRLvH8ty4tk7O8TbrYaF3NoGm9sH1-9N4VYA9dsF3UMNIavEcoBmiv_hwfCYPFTQR5385I7uP993qa7HZfq5Xb5uFFVT3C8k0L8osLSykqixSwWnOkWklKpaVlYVM6ZRpm0rJFZWYgVWQUaEVpKikmJH1HVt6OJguuBOEq_HgzFj4UBsIw-8NGsrzihVZlVbKSptCIQEthYKhyItcqYHF7iwbfIwBq38eo2aUb0b55ibfjPLFL1Rrf5Q |
| Cites_doi | 10.1049/gtd2.12855 10.47852/bonviewAIA3202624 10.7500/AEPS20180302002 10.1177/1748006X211021690 10.47852/bonviewAIA3202434 10.1007/s12555-021-0724-6 10.1109/TSTE.2022.3153609 10.1109/TSTE.2021.3068043 10.1049/gtd2.12332 10.1007/s001910050066 10.47852/bonviewJDSIS3202870 10.1007/s11042-022-12017-9 10.1109/TIA.2021.3057356 10.1177/1464419321994986 10.1109/TIA.2020.2974426 10.1049/iet-rpg.2019.1178 10.3837/tiis.2021.07.007 10.1007/s11801-024-3114-5 10.4018/IJAEIS.2020070102 10.1002/we.2816 10.3390/quantum3020021 10.1080/00207543.2023.2280186 10.13652/j.spjx.1003.5788.2022.90072 10.1093/jigpal/jzz054 10.1109/TSG.2020.3004488 10.1049/rpg2.12187 10.1049/rpg2.12384 10.1049/iet-ipr.2019.0761 10.1177/02783649221082115 10.1038/s41586-021-04223-6 10.1016/j.neucom.2020.11.016 10.35833/MPCE.2019.000021 10.1016/j.ijhydene.2021.04.130 10.1007/s11356-022-21414-4 10.1007/s00521-021-06619-x 10.1039/d2ay01874h 10.1002/er.5988 10.1049/iet-cps.2019.0035 10.1109/TSTE.2020.3042385 10.1049/rpg2.12014 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.61435/ijred.2025.60434 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ开放获取期刊资源库 url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EndPage | 157 |
| ExternalDocumentID | oai_doaj_org_article_029f1b8f7f5c4c7ab4aec0ab1e39b955 10_61435_ijred_2025_60434 |
| GroupedDBID | 5VS 7XC 8FE 8FG 8FH AAFWJ AAYXX ABJCF ABUWG ACIWK ADBBV AEGXH AEUYN AFFHD AFKRA AFPKN AFRAH ALMA_UNASSIGNED_HOLDINGS ATCPS BANNL BCNDV BENPR BGLVJ BHPHI BPHCQ BVBZV CCPQU CITATION EDH EOJEC GROUPED_DOAJ HCIFZ ITG ITH KQ8 L6V M7S OBODZ OK1 PATMY PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS PYCSY RNS |
| ID | FETCH-LOGICAL-c306t-4162bd87bca75db732092e1653f18dfca856716c7442504e8ac5a80365a7e543 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001389842000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2252-4940 |
| IngestDate | Mon Oct 20 20:59:27 EDT 2025 Sat Nov 29 04:05:30 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-4162bd87bca75db732092e1653f18dfca856716c7442504e8ac5a80365a7e543 |
| ORCID | 0009-0006-2843-5411 0009-0000-3020-8097 |
| OpenAccessLink | https://doaj.org/article/029f1b8f7f5c4c7ab4aec0ab1e39b955 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_029f1b8f7f5c4c7ab4aec0ab1e39b955 crossref_primary_10_61435_ijred_2025_60434 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-01-01 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | International journal of renewable energy development |
| PublicationYear | 2025 |
| Publisher | Diponegoro University |
| Publisher_xml | – name: Diponegoro University |
| References | 22 23 24 25 26 27 28 29 30 31 10 32 11 33 12 34 13 35 14 36 15 37 16 38 17 39 18 19 0 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – ident: 21 doi: 10.1049/gtd2.12855 – ident: 12 doi: 10.47852/bonviewAIA3202624 – ident: 19 doi: 10.7500/AEPS20180302002 – ident: 2 doi: 10.1177/1748006X211021690 – ident: 1 doi: 10.47852/bonviewAIA3202434 – ident: 26 doi: 10.1007/s12555-021-0724-6 – ident: 36 doi: 10.1109/TSTE.2022.3153609 – ident: 31 doi: 10.1109/TSTE.2021.3068043 – ident: 14 doi: 10.1049/gtd2.12332 – ident: 4 doi: 10.1007/s001910050066 – ident: 7 doi: 10.47852/bonviewJDSIS3202870 – ident: 15 doi: 10.1007/s11042-022-12017-9 – ident: 24 doi: 10.1109/TIA.2021.3057356 – ident: 27 doi: 10.1177/1464419321994986 – ident: 34 doi: 10.1109/TIA.2020.2974426 – ident: 10 doi: 10.1049/iet-rpg.2019.1178 – ident: 17 doi: 10.3837/tiis.2021.07.007 – ident: 30 doi: 10.1007/s11801-024-3114-5 – ident: 0 doi: 10.4018/IJAEIS.2020070102 – ident: 22 doi: 10.1002/we.2816 – ident: 32 doi: 10.3390/quantum3020021 – ident: 6 doi: 10.1080/00207543.2023.2280186 – ident: 38 doi: 10.13652/j.spjx.1003.5788.2022.90072 – ident: 8 doi: 10.1093/jigpal/jzz054 – ident: 25 doi: 10.1109/TSG.2020.3004488 – ident: 28 doi: 10.1049/rpg2.12187 – ident: 35 doi: 10.1049/rpg2.12384 – ident: 11 doi: 10.1049/iet-ipr.2019.0761 – ident: 18 doi: 10.1177/02783649221082115 – ident: 33 doi: 10.1038/s41586-021-04223-6 – ident: 16 doi: 10.1016/j.neucom.2020.11.016 – ident: 23 doi: 10.35833/MPCE.2019.000021 – ident: 37 doi: 10.1016/j.ijhydene.2021.04.130 – ident: 39 doi: 10.1007/s11356-022-21414-4 – ident: 5 doi: 10.1007/s00521-021-06619-x – ident: 13 doi: 10.1039/d2ay01874h – ident: 3 doi: 10.1002/er.5988 – ident: 29 doi: 10.1049/iet-cps.2019.0035 – ident: 20 doi: 10.1109/TSTE.2020.3042385 – ident: 9 doi: 10.1049/rpg2.12014 |
| SSID | ssj0001341946 |
| Score | 2.2788844 |
| Snippet | As renewable energy continues to rise in the global energy mix, wind energy is gradually increasing its share in the power system as a clean, renewable form of... |
| SourceID | doaj crossref |
| SourceType | Open Website Index Database |
| StartPage | 146 |
| SubjectTerms | backpropagation neural network energy storage scheduling genetic algorithm support vector machine wind power prediction |
| Title | Optimized wind power prediction and energy storage scheduling using genetic algorithm and backpropagation neural network |
| URI | https://doaj.org/article/029f1b8f7f5c4c7ab4aec0ab1e39b955 |
| Volume | 14 |
| WOSCitedRecordID | wos001389842000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ开放获取期刊资源库 issn: 2252-4940 databaseCode: DOA dateStart: 20120101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: false ssIdentifier: ssj0001341946 providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: East & South Asia Database issn: 2252-4940 databaseCode: BVBZV dateStart: 20120201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/eastsouthasia omitProxy: false ssIdentifier: ssj0001341946 providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database issn: 2252-4940 databaseCode: M7S dateStart: 20120201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: http://search.proquest.com omitProxy: false ssIdentifier: ssj0001341946 providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database issn: 2252-4940 databaseCode: PATMY dateStart: 20120201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/environmentalscience omitProxy: false ssIdentifier: ssj0001341946 providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Central issn: 2252-4940 databaseCode: BENPR dateStart: 20120201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.proquest.com/central omitProxy: false ssIdentifier: ssj0001341946 providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database issn: 2252-4940 databaseCode: PIMPY dateStart: 20120201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/publiccontent omitProxy: false ssIdentifier: ssj0001341946 providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQMMCAeIryqDwwIYUmjh3HY0FULJQOHcoU2Y5dCvShUB7i13PnFFQmFlYriU7fXXx39t13hJxlGFMLyyLuSxFxxVmkvPFRohPDpBLYtxeGTchuNx8MVG9p1BfWhNX0wDVwrZgpn5jcSy8st1Ibrp2NtUlcqowSgb0Uop6lZCqcriBNWejSAXtlKMPiSjPD-KA1eqwc0oQycZHFPOW_nNISd39wMp1tsrWIDmm7lmqHrLjJLtlc4gzcIx938JOPR5-upO-QT9MZjjmjswovXBBkqmHRhY4-ipWPsF9QyGDBo2DjOcU69yEFq8HmRaqfh9NqNH8Yh7eMtk-wocIWE9RFkesSpJnUleL7pN-57l_dRIvxCZGFPGAeQajFTJlLY7UUpZEpixVzSSZSn-SltzoXGWRLVnKOPGYu11boHDya0NIJnh6Q1cl04g4JlbF0eWksZDsQrnirZOpVwgz3WeJi6xrk_Bu-YlaTZBSQXASsi4B1gVgXAesGuUSAfx5EfuuwAFovFlov_tL60X985JhsoFj1gcoJWZ1Xr-6UrNu3-eilagaDapK1Xrt_e_8FuXXTTQ |
| linkProvider | Directory of Open Access Journals |
| 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%3Ajournal&rft.genre=article&rft.atitle=Optimized+wind+power+prediction+and+energy+storage+scheduling+using+genetic+algorithm+and+backpropagation+neural+network&rft.jtitle=International+journal+of+renewable+energy+development&rft.au=Peng+Wu&rft.au=Zongze+Li&rft.date=2025-01-01&rft.pub=Diponegoro+University&rft.issn=2252-4940&rft.volume=14&rft.issue=1&rft.spage=146&rft.epage=157&rft_id=info:doi/10.61435%2Fijred.2025.60434&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_029f1b8f7f5c4c7ab4aec0ab1e39b955 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2252-4940&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2252-4940&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2252-4940&client=summon |