A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability
Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, howe...
Uloženo v:
| Vydáno v: | Expert systems with applications Ročník 261; s. 125567 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.02.2025
|
| Témata: | |
| ISSN: | 0957-4174 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.
•The problem of feature limitation is solved by data dimension extension.•The ability of the optimization algorithm is improved by using multiple improvement strategies.•Ensemble learning using improves the generalization ability of the prediction framework.•Multi-objective optimization algorithm improves the accuracy and stability of prediction. |
|---|---|
| AbstractList | Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.
•The problem of feature limitation is solved by data dimension extension.•The ability of the optimization algorithm is improved by using multiple improvement strategies.•Ensemble learning using improves the generalization ability of the prediction framework.•Multi-objective optimization algorithm improves the accuracy and stability of prediction. |
| ArticleNumber | 125567 |
| Author | Tian, Zhirui Yang, Qing |
| Author_xml | – sequence: 1 givenname: Qing orcidid: 0009-0007-5093-2999 surname: Yang fullname: Yang, Qing email: 19980025@dufe.edu.cn, yq201506@yeah.net organization: School of Statistics, Dongbei University of Finance and Economics, Dalian, China – sequence: 2 givenname: Zhirui orcidid: 0000-0001-7680-6770 surname: Tian fullname: Tian, Zhirui email: zhiruitian@link.cuhk.edu.cn organization: School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, China |
| BookMark | eNp9kMtqwzAQAHVIoUnaH-hJP2BXUiw_oJcQ-oJAL-1ZrKR1qmDLRVJS_Pe1SU899LSwyyzMrMjCDx4JueMs54yX98cc4zfkgoki50LKslqQJWtklRW8Kq7JKsYjY7xirFqStKWfow7O0m4AS9shoIGYnD_QOMaEPdUQ0dLBUwsJKJwOPfoEyU0b8Jaij9jrDmmHEPzMnbzFQDvXuzSBLUI6BaRwBteBdp1L4w25aqGLePs71-Tj6fF995Lt355fd9t9ZjaMpQwNFlbyCspKNKCNtFyULWclVgZsW5qCm7pkmjW61VLXcrqYWhorNo3Wotisibj8NWGIMWCrvoLrIYyKMzW3Ukc1t1JzK3VpNUH1H8i4i28Kk8L_6MMFxUnq7DCoaBx6g9ZNWZOyg_sP_wGeMIyR |
| CitedBy_id | crossref_primary_10_1016_j_engappai_2025_110980 crossref_primary_10_3390_en18164408 crossref_primary_10_1016_j_jenvman_2025_124540 crossref_primary_10_1016_j_enconman_2025_119484 crossref_primary_10_1016_j_energy_2025_136476 crossref_primary_10_1016_j_eswa_2024_126361 crossref_primary_10_1063_5_0281592 crossref_primary_10_1016_j_renene_2025_123277 |
| Cites_doi | 10.1016/j.cie.2022.108839 10.1016/j.egyr.2024.08.078 10.1016/j.eswa.2024.124875 10.1016/j.scitotenv.2024.174374 10.1016/j.cie.2024.110114 10.58496/BJML/2024/004 10.1016/j.epsr.2024.111051 10.1016/j.egyr.2024.07.030 10.1016/j.engappai.2024.108375 10.1016/j.eswa.2024.125055 10.1016/S0952-1976(98)00064-5 10.1016/j.eswa.2020.114094 10.1016/j.eswa.2024.124286 10.1016/j.epsr.2024.111119 10.1016/j.cma.2024.116840 10.1016/j.renene.2023.118932 10.1109/ACCESS.2024.3378515 10.58496/BJML/2023/001 10.1016/j.eswa.2023.123088 10.1016/j.ifacol.2020.12.760 10.1016/j.epsr.2024.110953 10.1016/j.eswa.2010.11.033 10.1016/j.apenergy.2022.120171 10.1016/j.engappai.2022.105530 10.1016/j.sigpro.2021.108026 10.1016/j.jclepro.2019.119252 10.1016/j.eswa.2023.120402 10.1016/j.eswa.2023.121647 10.1051/e3sconf/201911802050 10.1109/ICJECE.2021.3076124 10.1016/j.renene.2021.12.100 10.1016/j.eswa.2023.121527 10.1016/j.energy.2023.128225 10.1016/j.jup.2019.03.004 10.1016/j.eswa.2024.123751 10.1016/j.apenergy.2021.117178 10.1016/j.cie.2023.109677 10.1016/j.powtec.2024.119836 10.1016/j.cjche.2024.07.011 10.1016/j.enbuild.2023.113550 10.1016/j.engappai.2018.01.005 10.1016/j.knosys.2023.111034 10.1016/j.iswa.2024.200422 10.1016/j.apenergy.2024.124308 10.1016/j.ijlmm.2022.07.002 10.1016/j.eswa.2024.124751 10.1016/j.asoc.2020.106809 10.1016/j.eswa.2012.01.166 10.1016/j.energy.2018.06.012 10.1016/j.asoc.2022.108877 10.1016/j.enconman.2015.07.041 10.1016/j.eswa.2023.122686 10.1016/j.eswa.2023.120594 10.1016/j.eswa.2023.121512 10.1016/j.cie.2023.109837 10.1016/j.cie.2021.107598 10.1016/j.eswa.2021.115939 10.1016/j.rineng.2024.103033 10.1016/j.energy.2024.132976 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2024.125567 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_eswa_2024_125567 S0957417424024345 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXKI AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFJKZ AFKWA AFTJW AGHFR AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ LG9 LY1 LY7 R2- SBC SET WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-ece4d517a6729abc5d126f106e7cadf6c41c860b09bfb5b8506ec85cd239bb243 |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001352992900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Tue Nov 18 21:34:47 EST 2025 Sat Nov 29 03:07:44 EST 2025 Sat Jan 04 15:43:48 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Load forecasting Multi-objective algorithm Data preprocessing Machine learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-ece4d517a6729abc5d126f106e7cadf6c41c860b09bfb5b8506ec85cd239bb243 |
| ORCID | 0009-0007-5093-2999 0000-0001-7680-6770 |
| ParticipantIDs | crossref_primary_10_1016_j_eswa_2024_125567 crossref_citationtrail_10_1016_j_eswa_2024_125567 elsevier_sciencedirect_doi_10_1016_j_eswa_2024_125567 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-01 2025-02-00 |
| PublicationDateYYYYMMDD | 2025-02-01 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Sharma, Sharma, Singh, Arora, Gill, Singh (b39) 2022; 5 Sreekumar, Khan, Rana, Sajjadi, Kothari (b42) 2022; 328 Sarangi, Panda, Das, Abraham (b37) 2018; 70 Yao, Yuan, Tsai, Zhang, Lu, Ding (b57) 2023; 230 Xu, Song, Du, Zhang (b54) 2024 Zhang, Jánošík (b60) 2024; 241 Wan, Ye, Peng (b48) 2023; 117 Guo, Fang, Zhao, Wang (b14) 2021; 161 Sarker, Shanmugam, Azam, Thennadil (b38) 2024; 23 He, Zhao, Gao, Zhang, Zhang, Li (b17) 2025; 238 Salgotra, Mirjalili (b36) 2024; 258 Wang, Li, Shi, Jiang, Song, Li (b49) 2024; 12 Lindberg, Bakker, Sartori (b26) 2019; 58 Ke, Ge, Yin, Zhang, Zheng, Zhang, Li, Wang, Wang (b21) 2024; 237 Dibaj, Ettefagh, Hassannejad, Ehghaghi (b7) 2021; 167 Khalid, Wu, Wahid, Alam, Ullah (b22) 2021; PP Lahouar, Slama (b24) 2015; 103 Haque, Rahman (b16) 2022; 122 Nadda, Singh, Roy, Yadav (b31) 2024 Zhang, Lin, Liu (b61) 2022; 185 Tian, Wang (b47) 2023; 215 Sun, Zheng, Zhao, Zhou, Li, Li, Xiong, Liu, Li (b43) 2024; 250 Fan, Wang, Wang, Zhang, Sun (b10) 2024; 308 Yunhua, X., Haojun, Z., & Nan, D. (2019). Short-term Load Forecasting Model Considering Meteorological Factors. In Wang, Lu, Chang (b50) 2024; 946 Cao, Wang, Xia (b3) 2024; 132 Fan, Han, Wang, Jia, Peng, Huang, Hong (b9) 2023; 280 Nafea, Alameri, Majeed, Khalaf, AL-Ani (b32) 2024; 2024 Gu, Wang, Liu (b13) 2024; 191 Fadoul, Hassan, Çağlar (b8) 2024; 24 Dab, Henao, Nagarsheth, Dubé, Sansregret, Agbossou (b6) 2023; 299 (pp. 572–577). Luo, Wang, Gao, Wang, Pang (b28) 2024; 12 Lee, Ko (b25) 2011; 38 Wang, Wang, Wang (b52) 2013; 40 Xu, Zheng, Zhu, chun Wong, Wang, Lin (b56) 2024; 254 Piya, Triki, Al Maimani, Mokhtarzadeh (b35) 2023; 175 Alsajri (b2) 2023; 2023 Luiz Junho Pereira, Antônio Oliver, Brendon Francisco, Simões Cunha Jr, Ferreira Gomes (b27) 2022; 187 Wang, Sun, Fathi, Eslami (b51) 2024; 10 Karim, Khalid, Aleryani, Tairan, Ali, Ali (b20) 2024; 12 Sheher, Shah, Mansoor, Habib, Ali (b40) 2023; 35 Mathew, Chikte, Sadanandan, Abdelaziz, Ijaz, Ghaoud (b29) 2024; 237 Tian, Gai (b45) 2023; 281 Song, Cai, Ma, Li (b41) 2024; 237 Moon, Hossain, Chon (b30) 2021; 183 Zhang, Wei, Li, Tan, Zhou (b62) 2018; 158 Golmohammadi, Abedsoltan, Goli, Ali (b12) 2024; 187 Xie, Li, Li, Huang, He (b53) 2023; 228 Tan, De, Li, Lin, Yang, Huang, Tan (b44) 2020; 248 Jamei, Ali, Karbasi, Karimi, Jahannemaei, Farooque, Yaseen (b19) 2024; 237 Feng, Wang, Wu, Liu, Liu, Xie (b11) 2024; 237 Neupane, Aryal, Rajabifard (b33) 2024; 255 Nie, Jiang, Zhang (b34) 2020; 97 Hafeez, Khan, Jan, Shah, Khan, Derhab (b15) 2021; 299 Alhmoud, Nawafleh (b1) 2021; 44 Chen, Wang, Tuo (b4) 2020; 53 Tian, Gai (b46) 2024; 245 Jameel, Abouhawwash (b18) 2024; 422 Cheng, Liu (b5) 2024; 376 Yuan, Li, Zhang, Zheng, Mao, Pei (b58) 2023; 185 Xu, Yao, Zheng, Chen (b55) 2024; 255 Kodogiannis, Anagnostakis (b23) 1999; 12 Golmohammadi (10.1016/j.eswa.2024.125567_b12) 2024; 187 Piya (10.1016/j.eswa.2024.125567_b35) 2023; 175 Zhang (10.1016/j.eswa.2024.125567_b60) 2024; 241 Cao (10.1016/j.eswa.2024.125567_b3) 2024; 132 Dibaj (10.1016/j.eswa.2024.125567_b7) 2021; 167 Fan (10.1016/j.eswa.2024.125567_b10) 2024; 308 Dab (10.1016/j.eswa.2024.125567_b6) 2023; 299 Luo (10.1016/j.eswa.2024.125567_b28) 2024; 12 Cheng (10.1016/j.eswa.2024.125567_b5) 2024; 376 Neupane (10.1016/j.eswa.2024.125567_b33) 2024; 255 Kodogiannis (10.1016/j.eswa.2024.125567_b23) 1999; 12 Wang (10.1016/j.eswa.2024.125567_b49) 2024; 12 Lindberg (10.1016/j.eswa.2024.125567_b26) 2019; 58 Moon (10.1016/j.eswa.2024.125567_b30) 2021; 183 Xu (10.1016/j.eswa.2024.125567_b54) 2024 Fadoul (10.1016/j.eswa.2024.125567_b8) 2024; 24 He (10.1016/j.eswa.2024.125567_b17) 2025; 238 Wang (10.1016/j.eswa.2024.125567_b52) 2013; 40 Alsajri (10.1016/j.eswa.2024.125567_b2) 2023; 2023 Xu (10.1016/j.eswa.2024.125567_b55) 2024; 255 Tan (10.1016/j.eswa.2024.125567_b44) 2020; 248 Sun (10.1016/j.eswa.2024.125567_b43) 2024; 250 Haque (10.1016/j.eswa.2024.125567_b16) 2022; 122 Ke (10.1016/j.eswa.2024.125567_b21) 2024; 237 Nadda (10.1016/j.eswa.2024.125567_b31) 2024 Nie (10.1016/j.eswa.2024.125567_b34) 2020; 97 Lahouar (10.1016/j.eswa.2024.125567_b24) 2015; 103 Luiz Junho Pereira (10.1016/j.eswa.2024.125567_b27) 2022; 187 Wang (10.1016/j.eswa.2024.125567_b51) 2024; 10 Sarangi (10.1016/j.eswa.2024.125567_b37) 2018; 70 Xie (10.1016/j.eswa.2024.125567_b53) 2023; 228 Yuan (10.1016/j.eswa.2024.125567_b58) 2023; 185 Guo (10.1016/j.eswa.2024.125567_b14) 2021; 161 Salgotra (10.1016/j.eswa.2024.125567_b36) 2024; 258 Chen (10.1016/j.eswa.2024.125567_b4) 2020; 53 Yao (10.1016/j.eswa.2024.125567_b57) 2023; 230 Alhmoud (10.1016/j.eswa.2024.125567_b1) 2021; 44 Tian (10.1016/j.eswa.2024.125567_b45) 2023; 281 10.1016/j.eswa.2024.125567_b59 Wan (10.1016/j.eswa.2024.125567_b48) 2023; 117 Xu (10.1016/j.eswa.2024.125567_b56) 2024; 254 Lee (10.1016/j.eswa.2024.125567_b25) 2011; 38 Sarker (10.1016/j.eswa.2024.125567_b38) 2024; 23 Tian (10.1016/j.eswa.2024.125567_b47) 2023; 215 Hafeez (10.1016/j.eswa.2024.125567_b15) 2021; 299 Sheher (10.1016/j.eswa.2024.125567_b40) 2023; 35 Tian (10.1016/j.eswa.2024.125567_b46) 2024; 245 Fan (10.1016/j.eswa.2024.125567_b9) 2023; 280 Sreekumar (10.1016/j.eswa.2024.125567_b42) 2022; 328 Gu (10.1016/j.eswa.2024.125567_b13) 2024; 191 Sharma (10.1016/j.eswa.2024.125567_b39) 2022; 5 Feng (10.1016/j.eswa.2024.125567_b11) 2024; 237 Jamei (10.1016/j.eswa.2024.125567_b19) 2024; 237 Mathew (10.1016/j.eswa.2024.125567_b29) 2024; 237 Wang (10.1016/j.eswa.2024.125567_b50) 2024; 946 Karim (10.1016/j.eswa.2024.125567_b20) 2024; 12 Song (10.1016/j.eswa.2024.125567_b41) 2024; 237 Zhang (10.1016/j.eswa.2024.125567_b61) 2022; 185 Jameel (10.1016/j.eswa.2024.125567_b18) 2024; 422 Nafea (10.1016/j.eswa.2024.125567_b32) 2024; 2024 Khalid (10.1016/j.eswa.2024.125567_b22) 2021; PP Zhang (10.1016/j.eswa.2024.125567_b62) 2018; 158 |
| References_xml | – volume: 237 year: 2024 ident: b11 article-title: Saturated load forecasting based on improved logistic regression and affinity propagation publication-title: Electric Power Systems Research – volume: 185 year: 2023 ident: b58 article-title: Research on real-time prediction of completion time based on AE-CNN-LSTM publication-title: Computers & Industrial Engineering – volume: 241 year: 2024 ident: b60 article-title: Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches publication-title: Expert Systems with Applications – volume: 215 year: 2023 ident: b47 article-title: A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer publication-title: Renewable Energy – volume: 161 year: 2021 ident: b14 article-title: The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality publication-title: Computers & Industrial Engineering – reference: (pp. 572–577). – volume: PP start-page: 1 year: 2021 ident: b22 article-title: An effective scholarly search by combining inverted indices and structured search with citation networks analysis publication-title: IEEE Access – volume: 40 start-page: 418 year: 2013 end-page: 428 ident: b52 article-title: Cost estimation of plastic injection molding parts through integration of PSO and BP neural network publication-title: Expert Systems with Applications – volume: 44 start-page: 356 year: 2021 end-page: 363 ident: b1 article-title: Short-term load forecasting for Jordan power system based on NARX-ELMAN neural network and ARMA model publication-title: IEEE Canadian Journal of Electrical and Computer Engineering – volume: 191 year: 2024 ident: b13 article-title: A combined system based on data preprocessing and optimization algorithm for electricity load forecasting publication-title: Computers & Industrial Engineering – volume: 245 year: 2024 ident: b46 article-title: Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization publication-title: Expert Systems with Applications – volume: 280 year: 2023 ident: b9 article-title: A new intelligent hybrid forecasting method for power load considering uncertainty publication-title: Knowledge-Based Systems – volume: 255 year: 2024 ident: b55 article-title: A hybrid Monte Carlo quantile EMD-LSTM method for satellite in-orbit temperature prediction and data uncertainty quantification publication-title: Expert Systems with Applications – volume: 422 year: 2024 ident: b18 article-title: Multi-objective Mantis Search Algorithm (MOMSA): A novel approach for engineering design problems and validation publication-title: Computer Methods in Applied Mechanics and Engineering – volume: 187 year: 2022 ident: b27 article-title: Multi-objective lichtenberg algorithm: A hybrid physics-based meta-heuristic for solving engineering problems publication-title: Expert Systems with Applications – volume: 23 year: 2024 ident: b38 article-title: Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability publication-title: Intelligent Systems with Applications – volume: 2023 start-page: 1 year: 2023 end-page: 6 ident: b2 article-title: A review on machine learning strategies for real-world engineering applications publication-title: Babylonian Journal of Machine Learning – volume: 38 start-page: 5902 year: 2011 end-page: 5911 ident: b25 article-title: Short-term load forecasting using lifting scheme and ARIMA models publication-title: Expert Systems with Applications – volume: 24 year: 2024 ident: b8 article-title: Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in djibouti publication-title: Results in Engineering – volume: 12 start-page: 159 year: 1999 end-page: 173 ident: b23 article-title: A study of advanced learning algorithms for short-term load forecasting publication-title: Engineering Applications of Artificial Intelligence – volume: 53 start-page: 12086 year: 2020 end-page: 12090 ident: b4 article-title: Short-term power load forecasting of GWO-KELM based on Kalman filter publication-title: IFAC-PapersOnLine – year: 2024 ident: b31 article-title: A comparative assessment of CFD based LSTM and GRU for hydrodynamic predictions of gas-solid fluidized bed publication-title: Powder Technology – volume: 35 year: 2023 ident: b40 article-title: Summarization of scholarly articles using BERT and BiGRU: Deep learning-based extractive approach publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 122 year: 2022 ident: b16 article-title: Short-term electrical load forecasting through heuristic configuration of regularized deep neural network publication-title: Applied Soft Computing – volume: 237 year: 2024 ident: b21 article-title: A general maximal margin hyper-sphere SVM for multi-class classification publication-title: Expert Systems with Applications – volume: 183 year: 2021 ident: b30 article-title: AR and ARMA model order selection for time-series modeling with ImageNet classification publication-title: Signal Processing – volume: 103 start-page: 1040 year: 2015 end-page: 1051 ident: b24 article-title: Day-ahead load forecast using random forest and expert input selection publication-title: Energy Conversion and Management – volume: 58 start-page: 63 year: 2019 end-page: 88 ident: b26 article-title: Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts publication-title: Utilities Policy – volume: 258 year: 2024 ident: b36 article-title: Multi-algorithm based evolutionary strategy with adaptive mutation mechanism for constraint engineering design problems publication-title: Expert Systems with Applications – volume: 187 year: 2024 ident: b12 article-title: Multi-objective dragonfly algorithm for optimizing a sustainable supply chain under resource sharing conditions publication-title: Computers & Industrial Engineering – volume: 238 year: 2025 ident: b17 article-title: Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters publication-title: Electric Power Systems Research – volume: 376 year: 2024 ident: b5 article-title: Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks publication-title: Applied Energy – volume: 255 year: 2024 ident: b33 article-title: CNNs for remote extraction of urban features: A survey-driven benchmarking publication-title: Expert Systems with Applications – volume: 5 start-page: 564 year: 2022 end-page: 575 ident: b39 article-title: Micro-drill on Al/SiC composite by EDD process: An RSM-MOGOA based hybrid approach publication-title: International Journal of Lightweight Materials and Manufacture – volume: 117 year: 2023 ident: b48 article-title: Multi-period dynamic multi-objective emergency material distribution model under uncertain demand publication-title: Engineering Applications of Artificial Intelligence – volume: 237 year: 2024 ident: b41 article-title: Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model publication-title: Expert Systems with Applications – volume: 299 year: 2023 ident: b6 article-title: Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand publication-title: Energy and Buildings – volume: 281 year: 2023 ident: b45 article-title: A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism publication-title: Energy – year: 2024 ident: b54 article-title: Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China publication-title: Chinese Journal of Chemical Engineering – volume: 237 year: 2024 ident: b29 article-title: Medium-term feeder load forecasting and boosting peak accuracy prediction using the PWP-XGBoost model publication-title: Electric Power Systems Research – volume: 250 year: 2024 ident: b43 article-title: Modifying the one-hot encoding technique can enhance the adversarial robustness of the visual model for symbol recognition publication-title: Expert Systems with Applications – volume: 230 year: 2023 ident: b57 article-title: ESO: An enhanced snake optimizer for real-world engineering problems publication-title: Expert Systems with Applications – volume: 12 start-page: 2452 year: 2024 end-page: 2461 ident: b49 article-title: Load forecasting method based on CNN and extended LSTM publication-title: Energy Reports – volume: 299 year: 2021 ident: b15 article-title: A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid publication-title: Applied Energy – volume: 12 start-page: 42769 year: 2024 end-page: 42790 ident: b20 article-title: HADE: Exploiting human action recognition through fine-tuned deep learning methods publication-title: IEEE ACCESS – volume: 70 start-page: 67 year: 2018 end-page: 80 ident: b37 article-title: Design of optimal high pass and band stop FIR filters using adaptive cuckoo search algorithm publication-title: Engineering Applications of Artificial Intelligence – volume: 167 year: 2021 ident: b7 article-title: A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults publication-title: Expert Systems with Applications – volume: 237 year: 2024 ident: b19 article-title: Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and explainable GPR publication-title: Expert Systems with Applications – volume: 254 year: 2024 ident: b56 article-title: A complementary fused method using GRU and XGBoost models for long-term solar energy hourly forecasting publication-title: Expert Systems with Applications – reference: Yunhua, X., Haojun, Z., & Nan, D. (2019). Short-term Load Forecasting Model Considering Meteorological Factors. In – volume: 158 start-page: 774 year: 2018 end-page: 781 ident: b62 article-title: Short term electricity load forecasting using a hybrid model publication-title: Energy – volume: 308 year: 2024 ident: b10 article-title: A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM publication-title: Energy – volume: 228 year: 2023 ident: b53 article-title: A decomposition-based multi-objective jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting publication-title: Expert Systems with Applications – volume: 12 start-page: 2676 year: 2024 end-page: 2689 ident: b28 article-title: Stacking integration algorithm based on CNN-BiLSTM-attention with XGBoost for short-term electricity load forecasting publication-title: Energy Reports – volume: 97 year: 2020 ident: b34 article-title: A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting publication-title: Applied Soft Computing – volume: 328 year: 2022 ident: b42 article-title: Aggregated net-load forecasting using Markov-Chain Monte-Carlo regression and C-vine copula publication-title: Applied Energy – volume: 10 year: 2024 ident: b51 article-title: Improving the method of short-term forecasting of electric load in distribution networks using wavelet transform combined with ridgelet neural network optimized by Self-adapted Kho-Kho Optimization Algorithm publication-title: Heliyon – volume: 248 year: 2020 ident: b44 article-title: Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine publication-title: Journal of Cleaner Production – volume: 185 start-page: 611 year: 2022 end-page: 628 ident: b61 article-title: Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM) publication-title: Renewable Energy – volume: 946 year: 2024 ident: b50 article-title: Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition publication-title: Science of the Total Environment – volume: 2024 start-page: 48 year: 2024 end-page: 55 ident: b32 article-title: A short review on supervised machine learning and deep learning techniques in computer vision publication-title: Babylonian Journal of Machine Learning – volume: 132 year: 2024 ident: b3 article-title: Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks publication-title: Engineering Applications of Artificial Intelligence – volume: 175 year: 2023 ident: b35 article-title: Optimization model for designing personalized tourism packages publication-title: Computers & Industrial Engineering – volume: 175 year: 2023 ident: 10.1016/j.eswa.2024.125567_b35 article-title: Optimization model for designing personalized tourism packages publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2022.108839 – volume: 35 issue: 9 year: 2023 ident: 10.1016/j.eswa.2024.125567_b40 article-title: Summarization of scholarly articles using BERT and BiGRU: Deep learning-based extractive approach publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 12 start-page: 2676 year: 2024 ident: 10.1016/j.eswa.2024.125567_b28 article-title: Stacking integration algorithm based on CNN-BiLSTM-attention with XGBoost for short-term electricity load forecasting publication-title: Energy Reports doi: 10.1016/j.egyr.2024.08.078 – volume: 255 year: 2024 ident: 10.1016/j.eswa.2024.125567_b55 article-title: A hybrid Monte Carlo quantile EMD-LSTM method for satellite in-orbit temperature prediction and data uncertainty quantification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.124875 – volume: 946 year: 2024 ident: 10.1016/j.eswa.2024.125567_b50 article-title: Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition publication-title: Science of the Total Environment doi: 10.1016/j.scitotenv.2024.174374 – volume: 191 year: 2024 ident: 10.1016/j.eswa.2024.125567_b13 article-title: A combined system based on data preprocessing and optimization algorithm for electricity load forecasting publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2024.110114 – volume: 2024 start-page: 48 year: 2024 ident: 10.1016/j.eswa.2024.125567_b32 article-title: A short review on supervised machine learning and deep learning techniques in computer vision publication-title: Babylonian Journal of Machine Learning doi: 10.58496/BJML/2024/004 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125567_b29 article-title: Medium-term feeder load forecasting and boosting peak accuracy prediction using the PWP-XGBoost model publication-title: Electric Power Systems Research doi: 10.1016/j.epsr.2024.111051 – volume: 12 start-page: 2452 year: 2024 ident: 10.1016/j.eswa.2024.125567_b49 article-title: Load forecasting method based on CNN and extended LSTM publication-title: Energy Reports doi: 10.1016/j.egyr.2024.07.030 – volume: 132 year: 2024 ident: 10.1016/j.eswa.2024.125567_b3 article-title: Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2024.108375 – volume: PP start-page: 1 issue: 99 year: 2021 ident: 10.1016/j.eswa.2024.125567_b22 article-title: An effective scholarly search by combining inverted indices and structured search with citation networks analysis publication-title: IEEE Access – volume: 258 year: 2024 ident: 10.1016/j.eswa.2024.125567_b36 article-title: Multi-algorithm based evolutionary strategy with adaptive mutation mechanism for constraint engineering design problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.125055 – volume: 12 start-page: 159 issue: 2 year: 1999 ident: 10.1016/j.eswa.2024.125567_b23 article-title: A study of advanced learning algorithms for short-term load forecasting publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/S0952-1976(98)00064-5 – volume: 10 year: 2024 ident: 10.1016/j.eswa.2024.125567_b51 article-title: Improving the method of short-term forecasting of electric load in distribution networks using wavelet transform combined with ridgelet neural network optimized by Self-adapted Kho-Kho Optimization Algorithm publication-title: Heliyon – volume: 167 year: 2021 ident: 10.1016/j.eswa.2024.125567_b7 article-title: A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.114094 – volume: 254 year: 2024 ident: 10.1016/j.eswa.2024.125567_b56 article-title: A complementary fused method using GRU and XGBoost models for long-term solar energy hourly forecasting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.124286 – volume: 238 year: 2025 ident: 10.1016/j.eswa.2024.125567_b17 article-title: Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters publication-title: Electric Power Systems Research doi: 10.1016/j.epsr.2024.111119 – volume: 422 year: 2024 ident: 10.1016/j.eswa.2024.125567_b18 article-title: Multi-objective Mantis Search Algorithm (MOMSA): A novel approach for engineering design problems and validation publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/j.cma.2024.116840 – volume: 215 year: 2023 ident: 10.1016/j.eswa.2024.125567_b47 article-title: A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer publication-title: Renewable Energy doi: 10.1016/j.renene.2023.118932 – volume: 12 start-page: 42769 year: 2024 ident: 10.1016/j.eswa.2024.125567_b20 article-title: HADE: Exploiting human action recognition through fine-tuned deep learning methods publication-title: IEEE ACCESS doi: 10.1109/ACCESS.2024.3378515 – volume: 2023 start-page: 1 year: 2023 ident: 10.1016/j.eswa.2024.125567_b2 article-title: A review on machine learning strategies for real-world engineering applications publication-title: Babylonian Journal of Machine Learning doi: 10.58496/BJML/2023/001 – volume: 245 year: 2024 ident: 10.1016/j.eswa.2024.125567_b46 article-title: Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.123088 – volume: 53 start-page: 12086 issue: 2 year: 2020 ident: 10.1016/j.eswa.2024.125567_b4 article-title: Short-term power load forecasting of GWO-KELM based on Kalman filter publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2020.12.760 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125567_b11 article-title: Saturated load forecasting based on improved logistic regression and affinity propagation publication-title: Electric Power Systems Research doi: 10.1016/j.epsr.2024.110953 – volume: 38 start-page: 5902 issue: 5 year: 2011 ident: 10.1016/j.eswa.2024.125567_b25 article-title: Short-term load forecasting using lifting scheme and ARIMA models publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.11.033 – volume: 328 year: 2022 ident: 10.1016/j.eswa.2024.125567_b42 article-title: Aggregated net-load forecasting using Markov-Chain Monte-Carlo regression and C-vine copula publication-title: Applied Energy doi: 10.1016/j.apenergy.2022.120171 – volume: 117 year: 2023 ident: 10.1016/j.eswa.2024.125567_b48 article-title: Multi-period dynamic multi-objective emergency material distribution model under uncertain demand publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2022.105530 – volume: 183 year: 2021 ident: 10.1016/j.eswa.2024.125567_b30 article-title: AR and ARMA model order selection for time-series modeling with ImageNet classification publication-title: Signal Processing doi: 10.1016/j.sigpro.2021.108026 – volume: 248 year: 2020 ident: 10.1016/j.eswa.2024.125567_b44 article-title: Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine publication-title: Journal of Cleaner Production doi: 10.1016/j.jclepro.2019.119252 – volume: 228 year: 2023 ident: 10.1016/j.eswa.2024.125567_b53 article-title: A decomposition-based multi-objective jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.120402 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125567_b21 article-title: A general maximal margin hyper-sphere SVM for multi-class classification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.121647 – ident: 10.1016/j.eswa.2024.125567_b59 doi: 10.1051/e3sconf/201911802050 – volume: 44 start-page: 356 issue: 3 year: 2021 ident: 10.1016/j.eswa.2024.125567_b1 article-title: Short-term load forecasting for Jordan power system based on NARX-ELMAN neural network and ARMA model publication-title: IEEE Canadian Journal of Electrical and Computer Engineering doi: 10.1109/ICJECE.2021.3076124 – volume: 185 start-page: 611 year: 2022 ident: 10.1016/j.eswa.2024.125567_b61 article-title: Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM) publication-title: Renewable Energy doi: 10.1016/j.renene.2021.12.100 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125567_b41 article-title: Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.121527 – volume: 281 year: 2023 ident: 10.1016/j.eswa.2024.125567_b45 article-title: A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism publication-title: Energy doi: 10.1016/j.energy.2023.128225 – volume: 58 start-page: 63 year: 2019 ident: 10.1016/j.eswa.2024.125567_b26 article-title: Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts publication-title: Utilities Policy doi: 10.1016/j.jup.2019.03.004 – volume: 250 year: 2024 ident: 10.1016/j.eswa.2024.125567_b43 article-title: Modifying the one-hot encoding technique can enhance the adversarial robustness of the visual model for symbol recognition publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.123751 – volume: 299 year: 2021 ident: 10.1016/j.eswa.2024.125567_b15 article-title: A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid publication-title: Applied Energy doi: 10.1016/j.apenergy.2021.117178 – volume: 185 year: 2023 ident: 10.1016/j.eswa.2024.125567_b58 article-title: Research on real-time prediction of completion time based on AE-CNN-LSTM publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2023.109677 – year: 2024 ident: 10.1016/j.eswa.2024.125567_b31 article-title: A comparative assessment of CFD based LSTM and GRU for hydrodynamic predictions of gas-solid fluidized bed publication-title: Powder Technology doi: 10.1016/j.powtec.2024.119836 – year: 2024 ident: 10.1016/j.eswa.2024.125567_b54 article-title: Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China publication-title: Chinese Journal of Chemical Engineering doi: 10.1016/j.cjche.2024.07.011 – volume: 299 year: 2023 ident: 10.1016/j.eswa.2024.125567_b6 article-title: Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand publication-title: Energy and Buildings doi: 10.1016/j.enbuild.2023.113550 – volume: 70 start-page: 67 year: 2018 ident: 10.1016/j.eswa.2024.125567_b37 article-title: Design of optimal high pass and band stop FIR filters using adaptive cuckoo search algorithm publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2018.01.005 – volume: 280 year: 2023 ident: 10.1016/j.eswa.2024.125567_b9 article-title: A new intelligent hybrid forecasting method for power load considering uncertainty publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2023.111034 – volume: 23 year: 2024 ident: 10.1016/j.eswa.2024.125567_b38 article-title: Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability publication-title: Intelligent Systems with Applications doi: 10.1016/j.iswa.2024.200422 – volume: 376 year: 2024 ident: 10.1016/j.eswa.2024.125567_b5 article-title: Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks publication-title: Applied Energy doi: 10.1016/j.apenergy.2024.124308 – volume: 5 start-page: 564 issue: 4 year: 2022 ident: 10.1016/j.eswa.2024.125567_b39 article-title: Micro-drill on Al/SiC composite by EDD process: An RSM-MOGOA based hybrid approach publication-title: International Journal of Lightweight Materials and Manufacture doi: 10.1016/j.ijlmm.2022.07.002 – volume: 255 year: 2024 ident: 10.1016/j.eswa.2024.125567_b33 article-title: CNNs for remote extraction of urban features: A survey-driven benchmarking publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.124751 – volume: 97 year: 2020 ident: 10.1016/j.eswa.2024.125567_b34 article-title: A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106809 – volume: 40 start-page: 418 issue: 2 year: 2013 ident: 10.1016/j.eswa.2024.125567_b52 article-title: Cost estimation of plastic injection molding parts through integration of PSO and BP neural network publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.01.166 – volume: 158 start-page: 774 year: 2018 ident: 10.1016/j.eswa.2024.125567_b62 article-title: Short term electricity load forecasting using a hybrid model publication-title: Energy doi: 10.1016/j.energy.2018.06.012 – volume: 122 year: 2022 ident: 10.1016/j.eswa.2024.125567_b16 article-title: Short-term electrical load forecasting through heuristic configuration of regularized deep neural network publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2022.108877 – volume: 103 start-page: 1040 year: 2015 ident: 10.1016/j.eswa.2024.125567_b24 article-title: Day-ahead load forecast using random forest and expert input selection publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2015.07.041 – volume: 241 year: 2024 ident: 10.1016/j.eswa.2024.125567_b60 article-title: Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.122686 – volume: 230 year: 2023 ident: 10.1016/j.eswa.2024.125567_b57 article-title: ESO: An enhanced snake optimizer for real-world engineering problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.120594 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125567_b19 article-title: Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and explainable GPR publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.121512 – volume: 187 year: 2024 ident: 10.1016/j.eswa.2024.125567_b12 article-title: Multi-objective dragonfly algorithm for optimizing a sustainable supply chain under resource sharing conditions publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2023.109837 – volume: 161 year: 2021 ident: 10.1016/j.eswa.2024.125567_b14 article-title: The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107598 – volume: 187 year: 2022 ident: 10.1016/j.eswa.2024.125567_b27 article-title: Multi-objective lichtenberg algorithm: A hybrid physics-based meta-heuristic for solving engineering problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115939 – volume: 24 year: 2024 ident: 10.1016/j.eswa.2024.125567_b8 article-title: Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in djibouti publication-title: Results in Engineering doi: 10.1016/j.rineng.2024.103033 – volume: 308 year: 2024 ident: 10.1016/j.eswa.2024.125567_b10 article-title: A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM publication-title: Energy doi: 10.1016/j.energy.2024.132976 |
| SSID | ssj0017007 |
| Score | 2.513865 |
| Snippet | Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 125567 |
| SubjectTerms | Data preprocessing Deep learning Load forecasting Machine learning Multi-objective algorithm |
| Title | A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability |
| URI | https://dx.doi.org/10.1016/j.eswa.2024.125567 |
| Volume | 261 |
| WOSCitedRecordID | wos001352992900001&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: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0017007 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb5wwELa2SQ-99F016UNz6A0RgddgOK6qVG0PUSul0qoX5BfNRoSNdtlt-u8zxjYhURo1h14QQjAg5vN4PDPfmJAPWhQSHTcd5zUOciZpGZeFMjYkoFStUpFr3W82wY-Oivm8_DaZ1IELs2142xYXF-X5f1U1XkNlW-rsPdQ9CMULeI5KxyOqHY__pPhZdPLH0rCiZim0rSI0Sqz74mbXtTmyE5e2SQJbHRqJza8zzz9ydcm4rjVnlk_VhKiJ5ZmtosZRoaLaCJd12IpF47p8X0sN982TO_-yQJ4bpckHO-Mj1d_D5NkXA7t47M-TxWqzGEckaBaKmEehRR6z1O2-E6wsdT3XvZ1MbeczfqsJd9GE0wOz_m37QlF2cHXz9X7ZN-axobowFK6dVlZGZWVUTsYDskt5VqL12519OZx_HfJNPHHE-vDlnl7lKgFvfsntLszILTl-Sh779QTMHA6ekYlpn5MnYa8O8Kb7Belm4GABFhYwggU4TUEPC1i2YGEBY1gAwgICLCDAAnpYgIcFeFjAGBYvyY9Ph8cfP8d-x41YTZOki40yTGcpFzmuuYRUmU6pHcS54UroOlcsVUWeyKSUtcyk7XZoVJEpTaellJRNX5Gddtma1wRwZZAmPOclymIsoSVK4zJnWcGZQMdsj6ThJ1bKt6O3u6I01d_Vt0ei4Zlz14zlzruzoJvKu5POTawQanc8t3-vt7whj67GwFuy06025h15qLbdYr1673F2CaQfm6Y |
| 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=A+hybrid+load+forecasting+system+based+on+data+augmentation+and+ensemble+learning+under+limited+feature+availability&rft.jtitle=Expert+systems+with+applications&rft.au=Yang%2C+Qing&rft.au=Tian%2C+Zhirui&rft.date=2025-02-01&rft.issn=0957-4174&rft.volume=261&rft.spage=125567&rft_id=info:doi/10.1016%2Fj.eswa.2024.125567&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2024_125567 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |