Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD
Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD)...
Uloženo v:
| Vydáno v: | Energy (Oxford) Ročník 296; s. 131173 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2024
|
| Témata: | |
| ISSN: | 0360-5442 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy.
•An outlier-robust edRVRFL prediction model is developed.•Use VMD to decompose the data and enhance the model prediction capability.•Fuzzy entropy is used as a criterion for data aggregation.•AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. |
|---|---|
| AbstractList | Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. •An outlier-robust edRVRFL prediction model is developed.•Use VMD to decompose the data and enhance the model prediction capability.•Fuzzy entropy is used as a criterion for data aggregation.•AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. |
| ArticleNumber | 131173 |
| Author | Peng, Tian Li, Zhengbo Ge, Yida Liu, Qianlong Suo, Leiming Song, Shihao Zhang, Chu |
| Author_xml | – sequence: 1 givenname: Chu surname: Zhang fullname: Zhang, Chu email: zhangchuhust@foxmail.com organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 2 givenname: Zhengbo surname: Li fullname: Li, Zhengbo organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 3 givenname: Yida surname: Ge fullname: Ge, Yida organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 4 givenname: Qianlong surname: Liu fullname: Liu, Qianlong organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 5 givenname: Leiming surname: Suo fullname: Suo, Leiming organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 6 givenname: Shihao surname: Song fullname: Song, Shihao organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China – sequence: 7 givenname: Tian orcidid: 0000-0002-1811-2313 surname: Peng fullname: Peng, Tian email: husthydropt@126.com organization: Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China |
| BookMark | eNqFkU1P3DAQhn2gUvnoP-DgI5ds7djJxj0grSgUJBCXtlfLHxPWS2IH2wHRX8FPxtv01EN78sia5x3NM0fowAcPCJ1SsqKEtp93K_AQH15XNan5ijJK1-wAHRLWkqrhvP6IjlLaEUKaTohD9Hbpt8ob5x9w2oaYqwxxxC_OW5wmAIunCNaZ7ILHWqXyUQrlcZjz4CBWMeg5ZQw-wagHwBZgwlF5G0b8DCaHiPvZ_-bVgAfnH7GH_BLiYxmSt3hzv6nClN3ofpXsn3dfT9CHXg0JPv15j9GPq8vvF9fV7f23m4vNbWUYE7miGgy3a91aLjhXpiv7rYUwbcNq0qu268BAz0VrmlYzrq0iQtAeWMtr3VjNjtHZkjvF8DRDynJ0ycAwKA9hTpLRhq0p6TpSWr8srSaGlCL00ris9ivlqNwgKZF79XInF_Vyr14u6gvM_4Kn6EYVX_-HnS8YFAfPxbRMxoE35RixaJU2uH8HvAOeM6c4 |
| CitedBy_id | crossref_primary_10_1016_j_neucom_2025_129491 crossref_primary_10_1016_j_est_2025_115392 crossref_primary_10_3390_en18164215 crossref_primary_10_1016_j_energy_2024_133826 crossref_primary_10_1007_s00477_025_02945_x crossref_primary_10_3390_buildings15183367 crossref_primary_10_1016_j_compeleceng_2025_110219 crossref_primary_10_1016_j_compeleceng_2025_110517 crossref_primary_10_1016_j_enconman_2025_119819 crossref_primary_10_1016_j_renene_2025_123527 crossref_primary_10_1016_j_energy_2024_132161 crossref_primary_10_1016_j_swevo_2025_101977 crossref_primary_10_3390_pr13092888 crossref_primary_10_1016_j_eswa_2025_128523 crossref_primary_10_1016_j_measurement_2025_118194 crossref_primary_10_1016_j_apenergy_2024_124708 crossref_primary_10_3390_app14219650 crossref_primary_10_1016_j_measurement_2024_115328 crossref_primary_10_1016_j_renene_2024_121085 crossref_primary_10_1016_j_renene_2024_122191 crossref_primary_10_1016_j_compeleceng_2024_110040 crossref_primary_10_1016_j_enconman_2024_118821 crossref_primary_10_1088_1361_6501_adf65e crossref_primary_10_1007_s43236_025_01134_x crossref_primary_10_1016_j_energy_2024_132228 crossref_primary_10_1063_5_0245781 crossref_primary_10_3390_pr13061763 crossref_primary_10_1016_j_energy_2024_133978 crossref_primary_10_1016_j_compeleceng_2024_109820 crossref_primary_10_1016_j_oceaneng_2025_121422 crossref_primary_10_1016_j_ijhydene_2025_151304 crossref_primary_10_1016_j_energy_2024_132928 crossref_primary_10_1016_j_energy_2024_132152 crossref_primary_10_1016_j_enbuild_2024_115143 |
| Cites_doi | 10.1016/j.egyr.2023.05.034 10.1016/j.egyr.2022.08.271 10.1016/j.buildenv.2023.110446 10.1016/j.renene.2016.03.103 10.1016/j.asoc.2022.108814 10.1162/neco.1997.9.8.1735 10.1016/j.energy.2023.128947 10.1016/j.renene.2022.09.114 10.1016/j.renene.2022.12.123 10.1016/j.patcog.2021.107978 10.1016/j.cma.2020.113609 10.1016/j.renene.2020.09.109 10.1016/j.jclepro.2023.138386 10.1109/TSP.2013.2288675 10.1109/TNSRE.2007.897025 10.1016/j.energy.2021.119887 10.1016/j.energy.2023.127865 10.1016/j.renene.2018.02.092 10.1016/j.eswa.2023.119878 10.1016/j.energy.2022.123848 10.1016/j.ecoinf.2023.102270 10.1016/j.enconman.2018.10.089 10.1016/j.energy.2016.06.075 10.1016/j.energy.2022.126419 10.1016/j.enconman.2022.115815 10.1016/j.enconman.2021.114919 10.1016/j.energy.2022.124250 10.1016/j.energy.2023.127526 10.1016/j.egyr.2023.05.181 10.1016/j.renene.2016.08.009 10.1016/j.asoc.2018.01.030 10.1016/j.apenergy.2018.10.080 10.1016/j.neucom.2014.09.022 10.1016/j.enconman.2019.111889 10.1016/j.energy.2023.126738 10.1016/j.energy.2022.126283 10.1016/j.energy.2023.129588 10.1016/j.enconman.2021.115102 10.1016/j.enconman.2023.116760 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.energy.2024.131173 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Environmental Sciences |
| ExternalDocumentID | 10_1016_j_energy_2024_131173 S0360544224009460 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHBH AAHCO AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARJD AAXKI AAXUO ABJNI ABMAC ACDAQ ACGFS ACIWK ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHIDL AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SES SEW SPC SPCBC SSR SSZ T5K TN5 XPP ZMT ~02 ~G- 29G 6TJ 9DU AAQXK AATTM AAYWO AAYXX ABDPE ABFNM ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AHHHB AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SAC WUQ ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c339t-1bec4d7b6d4944ac8360799c65320fa688ecef496c56b34bda0991fe3642b5db3 |
| ISICitedReferencesCount | 36 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001227448200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0360-5442 |
| IngestDate | Sun Sep 28 11:02:50 EDT 2025 Sat Nov 29 06:35:54 EST 2025 Tue Nov 18 22:25:27 EST 2025 Sat Sep 14 18:11:59 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Anti-interference ability Arithmetic optimization algorithm Wind speed prediction Ensemble deep random vector functional link network |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c339t-1bec4d7b6d4944ac8360799c65320fa688ecef496c56b34bda0991fe3642b5db3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-1811-2313 |
| PQID | 3153710880 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_3153710880 crossref_citationtrail_10_1016_j_energy_2024_131173 crossref_primary_10_1016_j_energy_2024_131173 elsevier_sciencedirect_doi_10_1016_j_energy_2024_131173 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-06-01 2024-06-00 20240601 |
| PublicationDateYYYYMMDD | 2024-06-01 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Energy (Oxford) |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Xiong, Meng, Xiong, Ma, Lou, Wang (bib2) 2022; 8 Liu, Mi, Li (bib5) 2018; 123 Li, Song, Wang, Wang, Jia (bib23) 2022; 251 Zheng, Lu, Zhou (bib11) 2023; 239 Joseph, Deo, Prasad, Salcedo-Sanz, Raj, Soar (bib14) 2023; 204 Wang, Zhu, Cheng, Zhou, Zhang, Xu (bib9) 2023; 420 Lin, Li, Shi, Sheng, Sun, Wang (bib29) 2023; 77 Chen, Wang, Xie, Yu (bib38) 2007; 15 Peng, Zhang, Zhou, Nazir (bib12) 2021; 221 Hu, Wang, Tao (bib22) 2021; 164 Yang, Guo, Huang (bib3) 2023; 282 Yaghoubirad, Azizi, Farajollahi, Ahmadi (bib10) 2023; 281 Yang, Zhou, Wu, Ding, Wang (bib30) 2022; 122 Zhang, Peng, Pan, Liu (bib31) 2019; 180 Zhang, Wang (bib15) 2023; 278 Qian, Pei, Zareipour, Chen (bib24) 2019; 235 Gao, Guo, Mei, Sha, Guo, Sun (bib28) 2023; 9 Dirk (bib34) 1999 Shi, Katuwal, Suganthan, Tanveer (bib35) 2021; 117 Hochreiter, Schmidhuber (bib40) 1997; 9 Wang, Zhang, Wu, Wang (bib4) 2016; 94 Hua, Zhang, Peng, Ji, Shahzad Nazir (bib6) 2022; 252 Jiang, Che, Wang (bib17) 2021; 250 Wang, Wu (bib25) 2016; 112 Soukissian, Karathanasi (bib33) 2016; 99 Suo, Peng, Song, Zhang, Wang, Fu (bib20) 2023; 276 Chung, Gulcehre, Cho, Bengio (bib41) 2014 Chakravarty, Demirhan, Baser (bib32) 2022; 266 Gao, Wang, Chen, Zhang, Zhou, Wu (bib8) 2023; 9 Yu, Niu, Gao, Wang, Sun, Li (bib27) 2023; 269 Abualigah, Diabat, Mirjalili, Abd Elaziz, Gandomi (bib39) 2021; 376 Zhong, Xu, Xian, He, Zhai, Zhou (bib26) 2024; 286 Zhang, Luo (bib36) 2015; 151 Li, Lin, Tseng, Tan, Lim (bib1) 2018; 65 Wang, Zhu, Zhang, Cheng, Zhou (bib16) 2023; 265 Xiong, Peng, Tao, Zhang, Song, Nazir (bib19) 2023; 266 Zhang, Ma, Hua, Sun, Nazir, Peng (bib21) 2022; 254 Duan, Chang, Chen, Wang, Zuo, Bai (bib13) 2022; 200 Wahbah, Feng, El-Fouly, Zahawi (bib7) 2019; 199 Shang, Chen, Chen, Guo, Yang (bib18) 2023; 223 Dragomiretskiy, Zosso (bib37) 2014; 62 Shi (10.1016/j.energy.2024.131173_bib35) 2021; 117 Wang (10.1016/j.energy.2024.131173_bib16) 2023; 265 Shang (10.1016/j.energy.2024.131173_bib18) 2023; 223 Zhang (10.1016/j.energy.2024.131173_bib15) 2023; 278 Gao (10.1016/j.energy.2024.131173_bib28) 2023; 9 Suo (10.1016/j.energy.2024.131173_bib20) 2023; 276 Hu (10.1016/j.energy.2024.131173_bib22) 2021; 164 Abualigah (10.1016/j.energy.2024.131173_bib39) 2021; 376 Peng (10.1016/j.energy.2024.131173_bib12) 2021; 221 Soukissian (10.1016/j.energy.2024.131173_bib33) 2016; 99 Yaghoubirad (10.1016/j.energy.2024.131173_bib10) 2023; 281 Lin (10.1016/j.energy.2024.131173_bib29) 2023; 77 Duan (10.1016/j.energy.2024.131173_bib13) 2022; 200 Yu (10.1016/j.energy.2024.131173_bib27) 2023; 269 Dirk (10.1016/j.energy.2024.131173_bib34) 1999 Yang (10.1016/j.energy.2024.131173_bib3) 2023; 282 Zheng (10.1016/j.energy.2024.131173_bib11) 2023; 239 Zhang (10.1016/j.energy.2024.131173_bib31) 2019; 180 Zhang (10.1016/j.energy.2024.131173_bib21) 2022; 254 Zhang (10.1016/j.energy.2024.131173_bib36) 2015; 151 Zhong (10.1016/j.energy.2024.131173_bib26) 2024; 286 Hochreiter (10.1016/j.energy.2024.131173_bib40) 1997; 9 Wahbah (10.1016/j.energy.2024.131173_bib7) 2019; 199 Yang (10.1016/j.energy.2024.131173_bib30) 2022; 122 Li (10.1016/j.energy.2024.131173_bib1) 2018; 65 Li (10.1016/j.energy.2024.131173_bib23) 2022; 251 Wang (10.1016/j.energy.2024.131173_bib4) 2016; 94 Wang (10.1016/j.energy.2024.131173_bib9) 2023; 420 Xiong (10.1016/j.energy.2024.131173_bib19) 2023; 266 Chen (10.1016/j.energy.2024.131173_bib38) 2007; 15 Hua (10.1016/j.energy.2024.131173_bib6) 2022; 252 Chung (10.1016/j.energy.2024.131173_bib41) 2014 Wang (10.1016/j.energy.2024.131173_bib25) 2016; 112 Liu (10.1016/j.energy.2024.131173_bib5) 2018; 123 Qian (10.1016/j.energy.2024.131173_bib24) 2019; 235 Chakravarty (10.1016/j.energy.2024.131173_bib32) 2022; 266 Gao (10.1016/j.energy.2024.131173_bib8) 2023; 9 Joseph (10.1016/j.energy.2024.131173_bib14) 2023; 204 Jiang (10.1016/j.energy.2024.131173_bib17) 2021; 250 Dragomiretskiy (10.1016/j.energy.2024.131173_bib37) 2014; 62 Xiong (10.1016/j.energy.2024.131173_bib2) 2022; 8 |
| References_xml | – volume: 266 year: 2023 ident: bib19 article-title: A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction publication-title: Energy – volume: 112 start-page: 208 year: 2016 end-page: 220 ident: bib25 article-title: On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation publication-title: Energy – volume: 239 year: 2023 ident: bib11 article-title: Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model publication-title: Build Environ – volume: 65 start-page: 333 year: 2018 end-page: 348 ident: bib1 article-title: A maximum power point tracking method for PV system with improved gravitational search algorithm publication-title: Appl Soft Comput – volume: 9 start-page: 6114 year: 2023 end-page: 6134 ident: bib8 article-title: Multi-step wind speed prediction based on LSSVM combined with ESMD and fractional-order beetle swarm optimization publication-title: Energy Rep – volume: 251 year: 2022 ident: bib23 article-title: A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD publication-title: Energy – volume: 278 year: 2023 ident: bib15 article-title: Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting publication-title: Energy – volume: 235 start-page: 939 year: 2019 end-page: 953 ident: bib24 article-title: A review and discussion of decomposition-based hybrid models for wind energy forecasting applications publication-title: Appl Energy – volume: 62 start-page: 531 year: 2014 end-page: 544 ident: bib37 article-title: Variational mode decomposition publication-title: IEEE Trans Signal Process – volume: 250 year: 2021 ident: bib17 article-title: Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation publication-title: Energy Convers Manag – volume: 180 start-page: 338 year: 2019 end-page: 357 ident: bib31 article-title: A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine publication-title: Energy Convers Manag – volume: 151 start-page: 1519 year: 2015 end-page: 1527 ident: bib36 article-title: Outlier-robust extreme learning machine for regression problems publication-title: Neurocomputing – volume: 266 year: 2022 ident: bib32 article-title: Robust wind speed estimation with modified fuzzy regression functions with a noise cluster publication-title: Energy Convers Manag – volume: 117 year: 2021 ident: bib35 article-title: Random vector functional link neural network based ensemble deep learning publication-title: Pattern Recogn – volume: 281 year: 2023 ident: bib10 article-title: Deep learning-based multistep ahead wind speed and power generation forecasting using direct method publication-title: Energy Convers Manag – start-page: 87 year: 1999 end-page: 97 ident: bib34 article-title: Random vector functional link (RVFL) networks publication-title: Neural networks for conditional probability estimation: forecasting beyond point predictions – volume: 99 start-page: 1287 year: 2016 end-page: 1298 ident: bib33 article-title: On the use of robust regression methods in wind speed assessment publication-title: Renew Energy – volume: 276 year: 2023 ident: bib20 article-title: Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm publication-title: Energy – volume: 282 year: 2023 ident: bib3 article-title: Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process publication-title: Energy – volume: 265 year: 2023 ident: bib16 article-title: A novel prediction model for wind power based on improved long short-term memory neural network publication-title: Energy – volume: 223 year: 2023 ident: bib18 article-title: Decomposition-based wind speed forecasting model using causal convolutional network and attention mechanism publication-title: Expert Syst Appl – volume: 200 start-page: 788 year: 2022 end-page: 808 ident: bib13 article-title: A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error publication-title: Renew Energy – volume: 252 year: 2022 ident: bib6 article-title: Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction publication-title: Energy Convers Manag – volume: 221 year: 2021 ident: bib12 article-title: An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting publication-title: Energy – volume: 376 year: 2021 ident: bib39 article-title: The arithmetic optimization algorithm publication-title: Comput Methods Appl Mech Eng – volume: 420 year: 2023 ident: bib9 article-title: A novel wind power prediction model improved with feature enhancement and autoregressive error compensation publication-title: J Clean Prod – volume: 94 start-page: 629 year: 2016 end-page: 636 ident: bib4 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy – volume: 8 start-page: 11181 year: 2022 end-page: 11191 ident: bib2 article-title: Multi-branch wind power prediction based on optimized variational mode decomposition publication-title: Energy Rep – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib40 article-title: Long short-term memory publication-title: Neural Comput – volume: 9 start-page: 335 year: 2023 end-page: 344 ident: bib28 article-title: Short-term wind power forecasting based on SSA-VMD-LSTM publication-title: Energy Rep – volume: 199 year: 2019 ident: bib7 article-title: Wind speed probability density estimation using root-transformed local linear regression publication-title: Energy Convers Manag – volume: 254 year: 2022 ident: bib21 article-title: An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction publication-title: Energy – volume: 286 year: 2024 ident: bib26 article-title: DTTM: a deep temporal transfer model for ultra-short-term online wind power forecasting publication-title: Energy – volume: 164 start-page: 729 year: 2021 end-page: 751 ident: bib22 article-title: Wind speed forecasting based on variational mode decomposition and improved echo state network publication-title: Renew Energy – volume: 122 year: 2022 ident: bib30 article-title: Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting publication-title: Appl Soft Comput – volume: 15 start-page: 266 year: 2007 end-page: 272 ident: bib38 article-title: Characterization of surface EMG signal based on fuzzy entropy publication-title: IEEE Trans Neural Syst Rehabil Eng – volume: 123 start-page: 694 year: 2018 end-page: 705 ident: bib5 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew Energy – volume: 204 start-page: 39 year: 2023 end-page: 58 ident: bib14 article-title: Near real-time wind speed forecast model with bidirectional LSTM networks publication-title: Renew Energy – volume: 77 year: 2023 ident: bib29 article-title: Forecasting of wind speed under wind-fire coupling scenarios by combining HS-VMD and AM-LSTM publication-title: Ecol Inf – volume: 269 year: 2023 ident: bib27 article-title: A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism publication-title: Energy – year: 2014 ident: bib41 article-title: Empirical evaluation of gated recurrent neural networks on sequence modeling – volume: 9 start-page: 6114 year: 2023 ident: 10.1016/j.energy.2024.131173_bib8 article-title: Multi-step wind speed prediction based on LSSVM combined with ESMD and fractional-order beetle swarm optimization publication-title: Energy Rep doi: 10.1016/j.egyr.2023.05.034 – volume: 8 start-page: 11181 year: 2022 ident: 10.1016/j.energy.2024.131173_bib2 article-title: Multi-branch wind power prediction based on optimized variational mode decomposition publication-title: Energy Rep doi: 10.1016/j.egyr.2022.08.271 – volume: 239 year: 2023 ident: 10.1016/j.energy.2024.131173_bib11 article-title: Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model publication-title: Build Environ doi: 10.1016/j.buildenv.2023.110446 – volume: 94 start-page: 629 year: 2016 ident: 10.1016/j.energy.2024.131173_bib4 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy doi: 10.1016/j.renene.2016.03.103 – volume: 122 year: 2022 ident: 10.1016/j.energy.2024.131173_bib30 article-title: Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2022.108814 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.energy.2024.131173_bib40 article-title: Long short-term memory publication-title: Neural Comput doi: 10.1162/neco.1997.9.8.1735 – volume: 282 year: 2023 ident: 10.1016/j.energy.2024.131173_bib3 article-title: Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process publication-title: Energy doi: 10.1016/j.energy.2023.128947 – volume: 200 start-page: 788 year: 2022 ident: 10.1016/j.energy.2024.131173_bib13 article-title: A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error publication-title: Renew Energy doi: 10.1016/j.renene.2022.09.114 – volume: 204 start-page: 39 year: 2023 ident: 10.1016/j.energy.2024.131173_bib14 article-title: Near real-time wind speed forecast model with bidirectional LSTM networks publication-title: Renew Energy doi: 10.1016/j.renene.2022.12.123 – volume: 117 year: 2021 ident: 10.1016/j.energy.2024.131173_bib35 article-title: Random vector functional link neural network based ensemble deep learning publication-title: Pattern Recogn doi: 10.1016/j.patcog.2021.107978 – volume: 376 year: 2021 ident: 10.1016/j.energy.2024.131173_bib39 article-title: The arithmetic optimization algorithm publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113609 – volume: 164 start-page: 729 year: 2021 ident: 10.1016/j.energy.2024.131173_bib22 article-title: Wind speed forecasting based on variational mode decomposition and improved echo state network publication-title: Renew Energy doi: 10.1016/j.renene.2020.09.109 – volume: 420 year: 2023 ident: 10.1016/j.energy.2024.131173_bib9 article-title: A novel wind power prediction model improved with feature enhancement and autoregressive error compensation publication-title: J Clean Prod doi: 10.1016/j.jclepro.2023.138386 – volume: 62 start-page: 531 issue: 3 year: 2014 ident: 10.1016/j.energy.2024.131173_bib37 article-title: Variational mode decomposition publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2013.2288675 – volume: 15 start-page: 266 issue: 2 year: 2007 ident: 10.1016/j.energy.2024.131173_bib38 article-title: Characterization of surface EMG signal based on fuzzy entropy publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2007.897025 – volume: 221 year: 2021 ident: 10.1016/j.energy.2024.131173_bib12 article-title: An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting publication-title: Energy doi: 10.1016/j.energy.2021.119887 – volume: 278 year: 2023 ident: 10.1016/j.energy.2024.131173_bib15 article-title: Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting publication-title: Energy doi: 10.1016/j.energy.2023.127865 – volume: 123 start-page: 694 year: 2018 ident: 10.1016/j.energy.2024.131173_bib5 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew Energy doi: 10.1016/j.renene.2018.02.092 – volume: 223 year: 2023 ident: 10.1016/j.energy.2024.131173_bib18 article-title: Decomposition-based wind speed forecasting model using causal convolutional network and attention mechanism publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.119878 – volume: 251 year: 2022 ident: 10.1016/j.energy.2024.131173_bib23 article-title: A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD publication-title: Energy doi: 10.1016/j.energy.2022.123848 – volume: 77 year: 2023 ident: 10.1016/j.energy.2024.131173_bib29 article-title: Forecasting of wind speed under wind-fire coupling scenarios by combining HS-VMD and AM-LSTM publication-title: Ecol Inf doi: 10.1016/j.ecoinf.2023.102270 – volume: 180 start-page: 338 year: 2019 ident: 10.1016/j.energy.2024.131173_bib31 article-title: A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2018.10.089 – volume: 112 start-page: 208 year: 2016 ident: 10.1016/j.energy.2024.131173_bib25 article-title: On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation publication-title: Energy doi: 10.1016/j.energy.2016.06.075 – year: 2014 ident: 10.1016/j.energy.2024.131173_bib41 – volume: 266 year: 2023 ident: 10.1016/j.energy.2024.131173_bib19 article-title: A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction publication-title: Energy doi: 10.1016/j.energy.2022.126419 – volume: 266 year: 2022 ident: 10.1016/j.energy.2024.131173_bib32 article-title: Robust wind speed estimation with modified fuzzy regression functions with a noise cluster publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2022.115815 – volume: 250 year: 2021 ident: 10.1016/j.energy.2024.131173_bib17 article-title: Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2021.114919 – start-page: 87 year: 1999 ident: 10.1016/j.energy.2024.131173_bib34 article-title: Random vector functional link (RVFL) networks – volume: 254 year: 2022 ident: 10.1016/j.energy.2024.131173_bib21 article-title: An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction publication-title: Energy doi: 10.1016/j.energy.2022.124250 – volume: 276 year: 2023 ident: 10.1016/j.energy.2024.131173_bib20 article-title: Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm publication-title: Energy doi: 10.1016/j.energy.2023.127526 – volume: 9 start-page: 335 year: 2023 ident: 10.1016/j.energy.2024.131173_bib28 article-title: Short-term wind power forecasting based on SSA-VMD-LSTM publication-title: Energy Rep doi: 10.1016/j.egyr.2023.05.181 – volume: 99 start-page: 1287 year: 2016 ident: 10.1016/j.energy.2024.131173_bib33 article-title: On the use of robust regression methods in wind speed assessment publication-title: Renew Energy doi: 10.1016/j.renene.2016.08.009 – volume: 65 start-page: 333 year: 2018 ident: 10.1016/j.energy.2024.131173_bib1 article-title: A maximum power point tracking method for PV system with improved gravitational search algorithm publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.01.030 – volume: 235 start-page: 939 year: 2019 ident: 10.1016/j.energy.2024.131173_bib24 article-title: A review and discussion of decomposition-based hybrid models for wind energy forecasting applications publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.10.080 – volume: 151 start-page: 1519 year: 2015 ident: 10.1016/j.energy.2024.131173_bib36 article-title: Outlier-robust extreme learning machine for regression problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.09.022 – volume: 199 year: 2019 ident: 10.1016/j.energy.2024.131173_bib7 article-title: Wind speed probability density estimation using root-transformed local linear regression publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2019.111889 – volume: 269 year: 2023 ident: 10.1016/j.energy.2024.131173_bib27 article-title: A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism publication-title: Energy doi: 10.1016/j.energy.2023.126738 – volume: 265 year: 2023 ident: 10.1016/j.energy.2024.131173_bib16 article-title: A novel prediction model for wind power based on improved long short-term memory neural network publication-title: Energy doi: 10.1016/j.energy.2022.126283 – volume: 286 year: 2024 ident: 10.1016/j.energy.2024.131173_bib26 article-title: DTTM: a deep temporal transfer model for ultra-short-term online wind power forecasting publication-title: Energy doi: 10.1016/j.energy.2023.129588 – volume: 252 year: 2022 ident: 10.1016/j.energy.2024.131173_bib6 article-title: Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2021.115102 – volume: 281 year: 2023 ident: 10.1016/j.energy.2024.131173_bib10 article-title: Deep learning-based multistep ahead wind speed and power generation forecasting using direct method publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2023.116760 |
| SSID | ssj0005899 |
| Score | 2.5742998 |
| Snippet | Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 131173 |
| SubjectTerms | algorithms Anti-interference ability Arithmetic optimization algorithm arithmetics energy Ensemble deep random vector functional link network prediction wind power wind speed Wind speed prediction |
| Title | Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD |
| URI | https://dx.doi.org/10.1016/j.energy.2024.131173 https://www.proquest.com/docview/3153710880 |
| Volume | 296 |
| WOSCitedRecordID | wos001227448200001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0360-5442 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0005899 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLZKhwQvEwwmxgAZibcoVZs4Fz9WowgQDJAGKrxEsePQTG1StU2p-BX8Bf4p59i5kE0w9sBLFFmO6_Z8Pef4y7kQ8gzEHDrSk9g1wLOZ4J4tQt-3PTYKVOonXqqGutlEcHoaTqf8fa_3s86F2c6DPA93O778r6KGMRA2ps5eQ9zNojAA9yB0uILY4fpPgp_kM6yhgTTBDHxrG3Wv9S1DfnwJpgqrAiSZaRCOJiyxdDyyhZFBYCLtVSHK9caC061amKwqtbTAoCXFwtpqit9CW1hRiPj-18pNKLnhdMfvxnYBemiRfYe1P7193uH-TaYhljjdmaj6hodomOuTWdlECelQgy8zlX8VRRMppCnYz1lLJbzJShz6AEifF5UlrogMh7UBV00C1xAwwjrK2eG_q1esDWQ6n1zS_IaEOB8o_UUG-AGDdnq30PYFA9iEJdYRb-eRWSXCVSKzyg2y5wQeD_tkb_xqMn3dxhGFuklps_s6P1MHEV7ezZ_8nwuegHZvzu6Q_epcQscGT3dJT-UH5Fadtr4-IIeTNiUSJlY2YX2P_GgAR1vAUQQc1YCjLeCoBhyFmzinXcDRGnAUAUcN4KgBHG0BRxFwtAIcRcDRDuAoAO4--fhicnby0q76fNjSdfnGHoEeYUkg_IRxxmKJeUUB59LHpiVp7Iehkipl3JeeL1wmkhiONaNUuXB2Fl4i3EPSz4tcPSDUF8yPmSO5J102TIJQCE8OY8kEOLapCo-IW__6kayK4GMvlnn0N9kfEbt5ammKwFwxP6gFG1WOrHFQI0DrFU8-rXEQgZ7Hl3dxropyHbngmsBpAMztw2vu5pjcbv9uj0h_syrVY3JTbjfZevWkgvMvbmLRJQ |
| linkProvider | Elsevier |
| 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=Enhancing+short-term+wind+speed+prediction+based+on+an+outlier-robust+ensemble+deep+random+vector+functional+link+network+with+AOA-optimized+VMD&rft.jtitle=Energy+%28Oxford%29&rft.au=Zhang%2C+Chu&rft.au=Li%2C+Zhengbo&rft.au=Ge%2C+Yida&rft.au=Liu%2C+Qianlong&rft.date=2024-06-01&rft.issn=0360-5442&rft.volume=296&rft.spage=131173&rft_id=info:doi/10.1016%2Fj.energy.2024.131173&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_energy_2024_131173 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0360-5442&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0360-5442&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0360-5442&client=summon |