Ultra-short-term forecasting of agricultural intelligent greenhouse power load based on ISDS and MDUS-LSTM
The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address...
Saved in:
Published in | Journal of physics. Conference series Vol. 2661; no. 1; pp. 12010 - 12020 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Bristol
IOP Publishing
01.12.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China. |
---|---|
AbstractList | The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China. |
Author | Xin, Xiaoming Wu, Hengtian Wu, Xiaoyu Sun, Bo Yu, Binbin Kong, Weizheng |
Author_xml | – sequence: 1 givenname: Binbin surname: Yu fullname: Yu, Binbin organization: Control Science and Engineering College, Shandong University . , . China – sequence: 2 givenname: Xiaoming surname: Xin fullname: Xin, Xiaoming organization: Control Science and Engineering College, Shandong University . , . China – sequence: 3 givenname: Weizheng surname: Kong fullname: Kong, Weizheng organization: State Grid Energy Research Institute Co., Ltd . , . China – sequence: 4 givenname: Hengtian surname: Wu fullname: Wu, Hengtian organization: State Grid Energy Research Institute Co., Ltd . , . China – sequence: 5 givenname: Xiaoyu surname: Wu fullname: Wu, Xiaoyu organization: State Grid Energy Research Institute Co., Ltd . , . China – sequence: 6 givenname: Bo surname: Sun fullname: Sun, Bo organization: Control Science and Engineering College, Shandong University . , . China |
BookMark | eNqFkN1KxDAQhYMoqKvPYMA7oe4k7abJpaz_rCjUvQ7ZdrJ2qUlNWsS3t2VFEQTnZgbmO3OYc0h2nXdIyAmDcwZSTlme8UTMlJhyIdiUTYFxYLBDDr43u9-zlPvkMMYNQDpUfkA2y6YLJokvPnRJh-GVWh-wNLGr3Zp6S8061GXfdH0wDa1dh01Tr9F1dB0Q3YvvI9LWv2OgjTcVXZmIFfWO3hWXBTWuog-XyyJZFM8PR2TPmibi8VefkOX11fP8Nlk83tzNLxZJyfMMEsF5piBdITJWCgNYAajUsvE7xUs1y6RdKVkJaTOjqhlKqZgR1loJKeN5OiGn27tt8G89xk5vfB_cYKm5AhAS8hkMVL6lyuBjDGh1G-pXEz40Az0Gq8fI9BifHq0109tgB-XZVln79uf0_dO8-A3qtrIDnP4B_2fxCb-ViMY |
Cites_doi | 10.1016/j.apenergy.2021.116452 |
ContentType | Journal Article |
Copyright | Published under licence by IOP Publishing Ltd Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: Published under licence by IOP Publishing Ltd – notice: Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | O3W TSCCA AAYXX CITATION 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO H8D HCIFZ L7M P5Z P62 PIMPY PQEST PQQKQ PQUKI PRINS |
DOI | 10.1088/1742-6596/2661/1/012010 |
DatabaseName | Institute of Physics - IOP eJournals - Open Access IOPscience (Open Access) CrossRef Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Korea Aerospace Database SciTech Premium Collection Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China |
DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection Technology Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database Aerospace Database ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest One Academic Advanced Technologies Database with Aerospace |
DatabaseTitleList | CrossRef Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: O3W name: IOP Publishing url: http://iopscience.iop.org/ sourceTypes: Enrichment Source Publisher – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 1742-6596 |
ExternalDocumentID | 10_1088_1742_6596_2661_1_012010 JPCS_2661_1_012010 |
GroupedDBID | 1JI 29L 2WC 4.4 5B3 5GY 5PX 5VS 7.Q AAJIO AAJKP ABHWH ACAFW ACHIP AEFHF AEJGL AFKRA AFYNE AIYBF AKPSB ALMA_UNASSIGNED_HOLDINGS ARAPS ASPBG ATQHT AVWKF AZFZN BENPR BGLVJ CCPQU CEBXE CJUJL CRLBU CS3 DU5 E3Z EBS EDWGO EQZZN F5P FRP GROUPED_DOAJ GX1 HCIFZ HH5 IJHAN IOP IZVLO J9A KNG KQ8 LAP N5L N9A O3W OK1 P2P PIMPY PJBAE RIN RNS RO9 ROL SY9 T37 TR2 TSCCA UCJ W28 XSB ~02 AAYXX CITATION 8FD 8FE 8FG ABUWG AZQEC DWQXO H8D L7M P62 PQEST PQQKQ PQUKI PRINS |
ID | FETCH-LOGICAL-c2740-6224903bee11c6a0ed0093f1266192c9548fb98d68f4a9d5e8891a6fff8031273 |
IEDL.DBID | IOP |
ISSN | 1742-6588 |
IngestDate | Thu Oct 10 16:28:56 EDT 2024 Thu Nov 21 21:56:45 EST 2024 Tue Aug 20 22:17:08 EDT 2024 Sun Aug 18 15:00:26 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Language | English |
License | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c2740-6224903bee11c6a0ed0093f1266192c9548fb98d68f4a9d5e8891a6fff8031273 |
OpenAccessLink | https://proxy.k.utb.cz/login?url=https://iopscience.iop.org/article/10.1088/1742-6596/2661/1/012010 |
PQID | 2900680750 |
PQPubID | 4998668 |
PageCount | 11 |
ParticipantIDs | proquest_journals_2900680750 crossref_primary_10_1088_1742_6596_2661_1_012010 iop_journals_10_1088_1742_6596_2661_1_012010 |
PublicationCentury | 2000 |
PublicationDate | 20231201 |
PublicationDateYYYYMMDD | 2023-12-01 |
PublicationDate_xml | – month: 12 year: 2023 text: 20231201 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Bristol |
PublicationPlace_xml | – name: Bristol |
PublicationTitle | Journal of physics. Conference series |
PublicationTitleAlternate | J. Phys.: Conf. Ser |
PublicationYear | 2023 |
Publisher | IOP Publishing |
Publisher_xml | – name: IOP Publishing |
References | Xing (JPCS_2661_1_012010bib4) 2011; 11 Chengwen (JPCS_2661_1_012010bib9) 2020; 44 Fei (JPCS_2661_1_012010bib22) 2021 Zhixing (JPCS_2661_1_012010bib12) 2020; 29 Liang (JPCS_2661_1_012010bib17) 2021; 285 Jinji (JPCS_2661_1_012010bib5) 2018; 37 Zhuo (JPCS_2661_1_012010bib8) 2018; 47 Gang (JPCS_2661_1_012010bib11) 2014 Zewen (JPCS_2661_1_012010bib21) 2020; 32 Herui (JPCS_2661_1_012010bib7) 2016; 43 Bing (JPCS_2661_1_012010bib14) 2019; 43 Jianwen (JPCS_2661_1_012010bib1) 2008; 36 Ruixiao (JPCS_2661_1_012010bib18) 2020 Shanshan (JPCS_2661_1_012010bib3) 2022; 31 Chenxi (JPCS_2661_1_012010bib6) 2015 Zhenyu (JPCS_2661_1_012010bib16) 2021; 24 Yuansheng (JPCS_2661_1_012010bib10) 2020; 4 Chuanjun (JPCS_2661_1_012010bib13) 2021; 40 Yh (JPCS_2661_1_012010bib19) 2001 Chenxi (JPCS_2661_1_012010bib20) 2022; 34 Zhen (JPCS_2661_1_012010bib2) 2021; 39 Jixiang (JPCS_2661_1_012010bib15) 2019 |
References_xml | – volume: 11 start-page: 4 year: 2011 ident: JPCS_2661_1_012010bib4 article-title: Nonlinear Flow Load Prediction Based on Adaptive Auto-regression Model [J] publication-title: Science Technology and Engineering contributor: fullname: Xing – volume: 24 start-page: 8 year: 2021 ident: JPCS_2661_1_012010bib16 article-title: Short-term electric power Load Prediction based on LSTM neural network [J] publication-title: Electric Power Big Data contributor: fullname: Zhenyu – volume: 4 year: 2020 ident: JPCS_2661_1_012010bib10 article-title: SVM short-time Power Load Prediction Based on Time Series [J] publication-title: Modern Information Technology contributor: fullname: Yuansheng – start-page: 5 year: 2014 ident: JPCS_2661_1_012010bib11 article-title: Bp-ann method for short-term load forecasting of power grid and its application [J] publication-title: Power Construction contributor: fullname: Gang – volume: 34 start-page: 8 year: 2022 ident: JPCS_2661_1_012010bib20 article-title: Short-term load forecasting based on similar-day and multi-integrated combinations [J] publication-title: Proceedings of the Chinese Society of Universities for Electric Power System and Automation contributor: fullname: Chenxi – year: 2021 ident: JPCS_2661_1_012010bib22 article-title: Remaining life prediction of lithium batteries based on sequential Bayesian updating [J] publication-title: Computer Integrated Manufacturing Systems contributor: fullname: Fei – volume: 43 start-page: 8 year: 2016 ident: JPCS_2661_1_012010bib7 article-title: SARIMA Mid-term Power load Prediction based on HP Filter [J] publication-title: Journal of North China Electric Power University: Natural Science Edition contributor: fullname: Herui – volume: 37 start-page: 7 year: 2018 ident: JPCS_2661_1_012010bib5 article-title: Daily Load Prediction of Power Grid Based on ARMA and ANN Model Combination Crossover Method [J] publication-title: Zhejiang Electric Power contributor: fullname: Jinji – start-page: 7 year: 2019 ident: JPCS_2661_1_012010bib15 article-title: Short-term Load Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model [J] publication-title: Automation of Electric Power Systems contributor: fullname: Jixiang – start-page: 1805 year: 2001 ident: JPCS_2661_1_012010bib19 article-title: Short-term electric load forecasting using ANN based trends combination model publication-title: (2001) PROCEEDINGS OF THE 2001 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING, VOLS I AND II 2001 contributor: fullname: Yh – volume: 32 start-page: 8 year: 2020 ident: JPCS_2661_1_012010bib21 article-title: Multifactor short-term load forecasting model based on PCA-DBILSTM [J] publication-title: Proceedings of the Chinese Society of Universities for Electric Power System and Automation contributor: fullname: Zewen – volume: 40 year: 2021 ident: JPCS_2661_1_012010bib13 article-title: Short-term Power Load Prediction based on LSTM Recurrent Neural Network [J] publication-title: Electric Power Engineering Technology contributor: fullname: Chuanjun – volume: 39 start-page: 5 year: 2021 ident: JPCS_2661_1_012010bib2 article-title: Short-term power load forecasting based on VMD-LSTM-MLR [J] publication-title: Water And Power Energy Science contributor: fullname: Zhen – volume: 31 start-page: 6 year: 2022 ident: JPCS_2661_1_012010bib3 article-title: Prediction of Ultra-short-term Power Load based on CNN-BilstM-ATTENTION [J] publication-title: Journal of Yunnan Minzu University: Natural Science contributor: fullname: Shanshan – year: 2015 ident: JPCS_2661_1_012010bib6 article-title: Short-term Power Load Forecasting Based on ARIMA Model [J] publication-title: Jilin Electric Power contributor: fullname: Chenxi – volume: 36 start-page: 3 year: 2008 ident: JPCS_2661_1_012010bib1 article-title: Research on load forecasting method of rural power system [J] publication-title: Journal of Anhui Agricultural Sciences contributor: fullname: Jianwen – start-page: 181 year: 2020 ident: JPCS_2661_1_012010bib18 article-title: Short-term load prediction by multilayer long and short-term memory neural network considering temperature fuzzification [J] publication-title: Power Automation Equipment contributor: fullname: Ruixiao – volume: 44 start-page: 8 year: 2020 ident: JPCS_2661_1_012010bib9 article-title: Load Prediction Method based on CNN-GRU Hybrid Neural Network [J] publication-title: Power Grid Technology contributor: fullname: Chengwen – volume: 285 start-page: 116452 year: 2021 ident: JPCS_2661_1_012010bib17 article-title: A review of machine learning in building load prediction [J] publication-title: Applied Energy doi: 10.1016/j.apenergy.2021.116452 contributor: fullname: Liang – volume: 29 start-page: 6 year: 2020 ident: JPCS_2661_1_012010bib12 article-title: Short-term Power Load Prediction based on K-MEANS and CNN [J] publication-title: Application of Computer Systems contributor: fullname: Zhixing – volume: 47 start-page: 3 year: 2018 ident: JPCS_2661_1_012010bib8 article-title: Short-term Power Load Forecasting Method based on Deep Learning LSTM Network [J] publication-title: Electronic Technique (Shanghai) contributor: fullname: Zhuo – volume: 43 year: 2019 ident: JPCS_2661_1_012010bib14 article-title: CNN-GRU short-term power load forecasting method based on attention mechanism [J] publication-title: Power Grid Technology contributor: fullname: Bing |
SSID | ssj0033337 |
Score | 2.3971395 |
Snippet | The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of... |
SourceID | proquest crossref iop |
SourceType | Aggregation Database Enrichment Source Publisher |
StartPage | 12010 |
SubjectTerms | Electrical loads Feature extraction Forecasting Greenhouses Mathematical models Meteorological data Model updating Parameters Physics Similarity |
SummonAdditionalLinks | – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3dS8MwEA86EXwRP3E6JaCPhrXZliVPIur8wInQDfYW2iaZymjrOv9_7_rhFEH72B6EXpL73V1-uSPkrOdhX6uuYJF0nHWFi5gyijMFphDwVUWRw9TA8EncjbsPk96kSrjlFa2ytomFoTZpjDnyNldFmwgAuIvsnWHXKDxdrVporJI1n_cFUvrk4La2xB14-uWFSM4AaWXN74Kgr3qnRBsRqu238RIpXqP9hk6rr2n2y0QXuDPYIpuVw0gvyxneJis22SHrBXEzznfJ23i2mIcsfwE3mqGZpeCF2jjMkc5MU0fD6fyrvAZ9_SrAuaBTZNy8QOBvaYat0ugsDQ1FVDM0Teh9cB3QMDF0eD0O2GMwGu6R8eBmdHXHqv4JLIZYE6JCgGfldSJrfT8WoWcN5i-cj3-seIyl3lykpBHSdUNlelZK5YfCOSdhq4Nfs08aSZrYA0KlH3MDcMqFARdA9bCcMQRymI9wcYd7TeLVetNZWSZDF8fbUmpUtUZVaxxY-7pUdZOcg351tWXy_8VPf4g_PF8FPyV0ZlyTtOrpWooul87h35-PyAZ2ky_ZKi3SWMw_7DH4HIvopFhYn0VhyyM priority: 102 providerName: ProQuest |
Title | Ultra-short-term forecasting of agricultural intelligent greenhouse power load based on ISDS and MDUS-LSTM |
URI | https://iopscience.iop.org/article/10.1088/1742-6596/2661/1/012010 https://www.proquest.com/docview/2900680750 |
Volume | 2661 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB7RVkhceCMCJVoJjmziR7zdPULb0FakjXAjelvZ-2ihlR3FzoVfz4wfhYAQQvhg-bDP8Xrm2_U3MwBvkoDyWk0Ez6WP-ET4nCurIq5QFaJ9VXnu6WhgdiqOFpOTi-TiZ1-Yctmp_hE-toGCWxF2hDg5xqYjLhIlxmRcxuGY_D_Jy2onllIQre_4bN5r4xivvdYpkipJ2XO8_tzQhoXawlH8pqYb2zN9AKYfdUs5uR6t63xkvv0S0PH_pvUQ7nfQlL1razyCO654DHcbiqipnsDXxU29ynh1hYCdk0JniHedySoiTrPSs-xydRvIg325DfVZs0vi9lyV68qxJSVlYzdlZhnZT8vKgh2nBynLCstmB4uUf0zPZ09hMT083z_iXaYGbnBXi_tPBAIqiHPnwtCILHCWTkp8SDNRkaGgcj5X0grpJ5myiZNShZnw3ktUKoignsF2URbuOTAZmsii4Y6ERbChEgqcjFtGOvnwJo6CAQT929HLNiCHbn6kS6lJhJpEqKljHepWhAN4i0LX3cdZ_b34643iJ_P9dLOEXlo_gN1-UfwoGqkmlwmisBf_1udLuEd57FuezC5s16u1e4Vop86HsCWnH4aw8_7wdP5pSJYnGTZLHO9n8efvX0HvwQ |
link.rule.ids | 314,780,784,12765,21388,27924,27925,33373,33744,38865,38890,43600,43805,53841,53867 |
linkProvider | IOP Publishing |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3dS8MwEA9OEX0RP3F-BvTRsLbravIkos5tbiJ0A99C2yTbZLR1nf-_d_2YiqB9bI-WXpL73V0u9yPksmUhr5XrsZAbh7meCZlQwmECTCHgqwhDg6mBwbPXGbm919ZrmXDLyrLKyibmhlolEebIG47IaSIA4G7Sd4asUbi7WlJo1Mia24RX40nx9mNliZtwXRcHIh0GSMur-i4I-sp7wmsgQjXsBh4ixWO039CpNk3SXyY6x532NtkqHUZ6W4zwDlnR8S5Zzws3o2yPvI1mi3nAsgm40QzNLAUvVEdBhuXMNDE0GM-X7TXodNmAc0HHWHEzgcBf0xSp0ugsCRRFVFM0iWnXv_dpECs6uB_5rO8PB_tk1H4Y3nVYyZ_AIog1ISoEeBZWM9TatiMvsLTC_IWx8Y-FE2GrNxMKrjxu3EColuZc2IFnjOGw1MGvOSCrcRLrQ0K5HTkK4NTxFLgAooXtjCGQw3yEiZqOVSdWpTeZFm0yZL69zblEVUtUtcQPS1sWqq6TK9CvLJdM9r_4xQ_x3sud_1NCpsrUyUk1XF-iX1Pn6O_H52SjMxz0Zb_7_HRMNpFZvqhcOSGri_mHPgX_YxGe5ZPsE9pVzgU |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JT-swEB6x6CEuiFWU1RLviMnSxthHRKnYykMKFdysJLZZhJKqKf-fmSQtqp4QIqccJrYzsef7xhnPAPyNfKpr1RE8lS7kHeFSrowKuUJTiPiq0tTR1kD_TlwOOtdP0dMc9KZnYYphY_pP8LZOFFyrsAmIkx42HXIRKeERuHiBR-c_A98bGjcPi7iABTlh_9qPE4vcxuu0PhhJD0o5ifP6vrEZlJrHkfxnqiv86a3CSkMc2Vk9zDWYs_k6_KkCOLNyA94G7-NRwssXpNOczC1DNmqzpKSwZlY4ljyPpmk22Os0EeeYPVPkzUvxUVo2pJJp7L1IDCN0M6zI2VXcjVmSG9bvDmJ-Gz_0N2HQu3g4v-RNHQWeoc-J3iHCtPLbqbVBkInEt4b2MVxAb6zCjFK-uVRJI6TrJMpEVkoVJMI5J3HJI7_ZgoW8yO02MBlkoUFYDYVBKqAiSmuMDh3tS7isHfot8Cd608M6XYaufnNLqUnVmlStqWMd6FrVLThG_epm6ZQ_ix_NiF_fn8ezEhonQgv2Jp_rSzRUVaUR5Eg7v-vzEJbuuz19e3V3swvLVHC-DmjZg4Xx6MPuIy0ZpwfVnPsEKMLPWQ |
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=Ultra-short-term+forecasting+of+agricultural+intelligent+greenhouse+power+load+based+on+ISDS+and+MDUS-LSTM&rft.jtitle=Journal+of+physics.+Conference+series&rft.au=Yu%2C+Binbin&rft.au=Xin%2C+Xiaoming&rft.au=Kong%2C+Weizheng&rft.au=Wu%2C+Hengtian&rft.date=2023-12-01&rft.pub=IOP+Publishing&rft.issn=1742-6588&rft.eissn=1742-6596&rft.volume=2661&rft.issue=1&rft_id=info:doi/10.1088%2F1742-6596%2F2661%2F1%2F012010&rft.externalDocID=JPCS_2661_1_012010 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1742-6588&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1742-6588&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1742-6588&client=summon |