Multi-objective algorithm for the design of prediction intervals for wind power forecasting model
A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-proc...
Uloženo v:
| Vydáno v: | Applied Mathematical Modelling Ročník 67; s. 101 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Elsevier BV
01.03.2019
|
| Témata: | |
| ISSN: | 1088-8691, 0307-904X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width. |
|---|---|
| AbstractList | A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width. |
| Author | Li, Ranran Li, Hongmin Jiang, Ping |
| Author_xml | – sequence: 1 givenname: Ping surname: Jiang fullname: Jiang, Ping – sequence: 2 givenname: Ranran surname: Li fullname: Li, Ranran – sequence: 3 givenname: Hongmin surname: Li fullname: Li, Hongmin |
| BookMark | eNotjl1LwzAYhYNMcE5_gHcBr1vfpEmaXMrwCybeKHg3uuTNltIltc22v2_9uDo88HDOuSSzmCIScsOgZMDUXVs2_b7kwPTEJTBzRuZQQV0YEJ8zMmegdaGVYRfkchxbAMa1gjlpXg9dDkXatGhzOCJtum0aQt7tqU8DzTukDsewjTR52g_owqSlSEPMOBybbvzVTiE62qcTDj-IthlziFu6Tw67K3LuJw-v_3NBPh4f3pfPxert6WV5vypsxWUuNkp6Z6fHXlYNWCmFRrCWWS1q4SvuhJYOPXDVCCNR-rquasHdxhlnhMJqQW7_evshfR1wzOs2HYY4Ta45q5UyhoOsvgHovlrp |
| CitedBy_id | crossref_primary_10_1007_s12652_020_01866_7 crossref_primary_10_1016_j_scs_2020_102036 crossref_primary_10_3390_en15249657 crossref_primary_10_1049_tje2_12186 crossref_primary_10_3233_JIFS_210004 crossref_primary_10_1109_ACCESS_2021_3127940 crossref_primary_10_1155_2020_9601763 crossref_primary_10_3390_sym15040781 crossref_primary_10_1016_j_jclepro_2019_119318 crossref_primary_10_1109_ACCESS_2022_3171610 crossref_primary_10_1016_j_renene_2019_04_154 crossref_primary_10_1016_j_ins_2020_10_034 crossref_primary_10_1016_j_swevo_2022_101070 crossref_primary_10_1016_j_apenergy_2025_126234 crossref_primary_10_1016_j_eswa_2020_113498 crossref_primary_10_1016_j_renene_2021_05_082 crossref_primary_10_1061__ASCE_WR_1943_5452_0001329 crossref_primary_10_1007_s00521_019_04629_4 crossref_primary_10_1016_j_energy_2021_122012 crossref_primary_10_1016_j_ress_2022_108820 crossref_primary_10_1016_j_apm_2019_10_069 crossref_primary_10_1016_j_apenergy_2019_113353 crossref_primary_10_1109_ACCESS_2020_2978169 crossref_primary_10_1016_j_epsr_2023_109159 crossref_primary_10_1155_2022_3764215 crossref_primary_10_1016_j_scs_2020_102052 crossref_primary_10_1016_j_renene_2022_07_009 crossref_primary_10_3390_s19092055 crossref_primary_10_1016_j_asoc_2022_108933 crossref_primary_10_1016_j_apenergy_2019_05_016 crossref_primary_10_3390_app11209383 crossref_primary_10_1016_j_seta_2020_100757 crossref_primary_10_3390_a18060354 crossref_primary_10_1016_j_rser_2021_111758 crossref_primary_10_1109_ACCESS_2019_2957174 crossref_primary_10_1002_we_2763 crossref_primary_10_1016_j_energy_2020_119361 crossref_primary_10_3390_su15010334 crossref_primary_10_1007_s42452_020_2830_0 crossref_primary_10_2166_ws_2021_146 crossref_primary_10_1016_j_asoc_2023_110464 crossref_primary_10_1016_j_egyr_2022_11_167 crossref_primary_10_1109_ACCESS_2019_2957062 crossref_primary_10_1016_j_asoc_2019_03_035 crossref_primary_10_1016_j_renene_2022_10_122 crossref_primary_10_1109_ACCESS_2020_2980562 crossref_primary_10_1016_j_oceaneng_2025_122518 crossref_primary_10_1016_j_enconman_2020_112474 crossref_primary_10_1109_ACCESS_2021_3129883 crossref_primary_10_1016_j_eswa_2022_118622 crossref_primary_10_1016_j_jclepro_2019_119195 crossref_primary_10_1016_j_egyr_2023_05_063 crossref_primary_10_1002_2050_7038_13189 crossref_primary_10_1016_j_apenergy_2019_114257 crossref_primary_10_1109_ACCESS_2022_3142083 crossref_primary_10_1016_j_apenergy_2019_114137 crossref_primary_10_1007_s12555_020_0529_z crossref_primary_10_3390_app13031311 crossref_primary_10_1016_j_energy_2024_131057 crossref_primary_10_1016_j_engappai_2020_104133 crossref_primary_10_12677_aam_2024_1310429 crossref_primary_10_1016_j_enconman_2020_113324 crossref_primary_10_1016_j_apenergy_2019_114243 crossref_primary_10_1109_ACCESS_2021_3135527 crossref_primary_10_1155_2020_9564287 crossref_primary_10_1080_15325008_2023_2220688 crossref_primary_10_1002_eng2_12178 crossref_primary_10_1007_s00521_020_04996_3 crossref_primary_10_1002_jnm_3094 crossref_primary_10_1093_jcde_qwac021 crossref_primary_10_1007_s10462_019_09768_7 crossref_primary_10_1016_j_apenergy_2021_117446 crossref_primary_10_1109_ACCESS_2022_3189477 crossref_primary_10_1049_rpg2_12588 crossref_primary_10_1109_ACCESS_2019_2942040 crossref_primary_10_1016_j_apenergy_2019_03_097 crossref_primary_10_1007_s00500_021_06189_z crossref_primary_10_1016_j_compeleceng_2022_108000 crossref_primary_10_1109_ACCESS_2020_2977921 crossref_primary_10_3390_su152015050 crossref_primary_10_1007_s00521_024_09923_4 crossref_primary_10_1155_2021_6632390 crossref_primary_10_3390_en12214146 crossref_primary_10_3390_en16031530 crossref_primary_10_1049_gtd2_12291 crossref_primary_10_1016_j_asoc_2020_106900 crossref_primary_10_1371_journal_pone_0273257 crossref_primary_10_1016_j_egyr_2022_04_050 crossref_primary_10_1016_j_measurement_2020_108373 crossref_primary_10_1016_j_measurement_2021_109762 crossref_primary_10_1016_j_asoc_2020_106509 crossref_primary_10_1109_ACCESS_2020_3025967 crossref_primary_10_1007_s10586_024_04996_1 crossref_primary_10_1016_j_asoc_2019_105587 crossref_primary_10_1016_j_segan_2025_101664 crossref_primary_10_1080_15567036_2022_2126035 crossref_primary_10_3390_electronics14173373 crossref_primary_10_1016_j_asoc_2020_106476 |
| ContentType | Journal Article |
| Copyright | Copyright Elsevier BV Mar 2019 |
| Copyright_xml | – notice: Copyright Elsevier BV Mar 2019 |
| DBID | 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.apm.2018.10.019 |
| DatabaseName | Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology Mathematics |
| EISSN | 0307-904X |
| GroupedDBID | -W8 -~X .7I .GO .QK 0BK 0R~ 23M 2DF 4.4 53G 5GY 6J9 7SC 8FD 8VB AAGDL AAGZJ AAHIA AAHSB AAMFJ AAMIU AAPUL AATTQ AAZMC ABCCY ABDBF ABFIM ABIVO ABJNI ABLIJ ABPEM ABRYG ABTAI ABXUL ABXYU ABZLS ACGFS ACGOD ACHQT ACTIO ACTOA ACUHS ADAHI ADCVX ADKVQ AECIN AEFOU AEGXH AEISY AEKEX AEMOZ AEMXT AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AHDZW AHQJS AIJEM AIYEW AJWEG AKBVH AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AQTUD AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CQ1 CS3 DGFLZ DKSSO EAP EBR EBS EBU EDJ EJD EMK EPL EPS EST ESX E~B E~C F5P FEDTE G-F GTTXZ H13 HF~ HVGLF HZ~ J.O JQ2 K1G KYCEM L7M LJTGL L~C L~D M4Z NA5 O9- P2P PQQKQ QWB RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TH9 TNTFI TRJHH TUROJ TUS TWZ UPT UT5 UT9 VAE ZL0 ~01 ~S~ |
| ID | FETCH-LOGICAL-c325t-b65fdc307f53a0c5548e0cc1c8474f32d485def026a495e5f773742dbd9d946e3 |
| ISICitedReferencesCount | 107 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000456492500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1088-8691 |
| IngestDate | Sun Nov 09 07:25:16 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c325t-b65fdc307f53a0c5548e0cc1c8474f32d485def026a495e5f773742dbd9d946e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.apm.2018.10.019 |
| PQID | 2176699205 |
| PQPubID | 2045280 |
| ParticipantIDs | proquest_journals_2176699205 |
| PublicationCentury | 2000 |
| PublicationDate | 20190301 |
| PublicationDateYYYYMMDD | 2019-03-01 |
| PublicationDate_xml | – month: 03 year: 2019 text: 20190301 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Applied Mathematical Modelling |
| PublicationYear | 2019 |
| Publisher | Elsevier BV |
| Publisher_xml | – name: Elsevier BV |
| SSID | ssj0012860 ssj0005904 |
| Score | 2.554938 |
| Snippet | A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 101 |
| SubjectTerms | Algorithms Datasets Electric power generation Feature extraction Forecasting Fuzzy set theory Fuzzy sets Intervals Machine learning Multiple objective analysis Pareto optimization Variation Wind power Wind power generation Wind speed |
| Title | Multi-objective algorithm for the design of prediction intervals for wind power forecasting model |
| URI | https://www.proquest.com/docview/2176699205 |
| Volume | 67 |
| WOSCitedRecordID | wos000456492500008&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: PRVAWR databaseName: Taylor and Francis Online Journals customDbUrl: eissn: 0307-904X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012860 issn: 1088-8691 databaseCode: TFW dateStart: 19970301 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NbxMxELVC4VAOCAqoQEE-IC7I0X7G9hGhRhxCqNAWcou8u3ZJle6GTVrKv2fG9n7QSggOXFaJs1lt8t6OZ-w3M4S8hmcZaCBKprQULMlTzaThExaC65yjfx4IZZtN8PlcLBbyZDS6bHNhrta8qsT1tdz8V6hhDMDG1Nl_gLu7KAzAawAdjgA7HP8KeJtSy-r83Jmyt2p9Vjer3beLTlFYWtWG1To3uE_j9Y4ofsRiynjajxXWD8AOavhWF2pr5dG2b87Qn22d2I9d9VfM9cWz1u2ciPKclV-VPhkMzqyO4LOqmoEsyPXQrquzC18R3C9IYA5UPFyQ6DNlvgzsKhgzJiauMddY-4StgDMZOIFma4xdbw5vTUN32VtW3i04nI_VBmsJhGKM-jxveX-rqD3_tJyezmbL7HiRvdl8Z9hsDDflfeeVO-RuxFOJSsBs-rVXBkkMUf1OVCRcpnn7A9qdcasRvHEDt-Zz66RkD8kDH13Qd44Vj8hIVwfkfg_O9oDsd1Pez8dE3SAL7chCAXYKX6OOLLQ2tCcL7chiT0OyUEsWOiALtWR5Qk6nx9n7D8w33WBFHKU7lk9SUxYAjUljFRTgbQodFEVYgBuTmDgqE5GW2kDoriC21qnhPOZJVOalLGUy0fFTslfVlT4kNDK5AX9TyTIW4CsZFXOeQ7gdJkZAXBs_I0ft37X0D9B2GWHFUimjIH3-549fkP2efUdkb9dc6pfkXnG1W22bVxbRX233bSg |
| linkProvider | Taylor & Francis |
| 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=Multi-objective+algorithm+for+the+design+of+prediction+intervals+for+wind+power+forecasting+model&rft.jtitle=Applied+Mathematical+Modelling&rft.au=Jiang%2C+Ping&rft.au=Li%2C+Ranran&rft.au=Li%2C+Hongmin&rft.date=2019-03-01&rft.pub=Elsevier+BV&rft.issn=1088-8691&rft.eissn=0307-904X&rft.volume=67&rft.spage=101&rft_id=info:doi/10.1016%2Fj.apm.2018.10.019&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1088-8691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1088-8691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1088-8691&client=summon |