Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data

The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this...

Full description

Saved in:

Bibliographic Details
Published in:	Electric power components and systems Vol. 48; no. 6-7; pp. 603 - 614
Main Authors:	Li, Yuancheng, Yang, Rongyan, Guo, Panpan
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 06.08.2020 Taylor & Francis Ltd
Subjects:	Algorithms Big Data Clustering Data processing Datasets Electrical loads Electricity consumption Electricity meters Forecasting K-means algorithm Machine learning massive user load data Mathematical models parallel OS-ELM algorithm short-term load forecast Smart grid Spark Training User requirements
ISSN:	1532-5008, 1532-5016
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this article. By analyzing the characteristics of the Spark framework and the MapReduce framework, the Spark big data processing framework is determined as the basic framework for processing massive user load data, and a parallel K-means load clustering model based on Spark is designed. The on-line sequential learning machine OS-ELM makes the hidden layer data of computing each incremental training dataset mutually independent, therefore, a Spark-based parallel OS-ELM (SBPOS-ELM) algorithm is put forward. The proposed model is applied under the smart electricity big data environment and the training samples are selected using the incremental training dataset to make a short-term prediction of the millions of users' smart meter electricity load, which verifies the feasibility and effectiveness of the proposed model. At last, comparing with other commonly used short-term load forecasting algorithms, the experimental results show that SBPOS-ELM algorithm has higher accuracy and operation efficiency.
AbstractList	The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this article. By analyzing the characteristics of the Spark framework and the MapReduce framework, the Spark big data processing framework is determined as the basic framework for processing massive user load data, and a parallel K-means load clustering model based on Spark is designed. The on-line sequential learning machine OS-ELM makes the hidden layer data of computing each incremental training dataset mutually independent, therefore, a Spark-based parallel OS-ELM (SBPOS-ELM) algorithm is put forward. The proposed model is applied under the smart electricity big data environment and the training samples are selected using the incremental training dataset to make a short-term prediction of the millions of users' smart meter electricity load, which verifies the feasibility and effectiveness of the proposed model. At last, comparing with other commonly used short-term load forecasting algorithms, the experimental results show that SBPOS-ELM algorithm has higher accuracy and operation efficiency.
Author	Yang, Rongyan Li, Yuancheng Guo, Panpan
Author_xml	– sequence: 1 givenname: Yuancheng surname: Li fullname: Li, Yuancheng organization: School of Control and Computer Engineering, North China Electric Power University – sequence: 2 givenname: Rongyan surname: Yang fullname: Yang, Rongyan organization: School of Control and Computer Engineering, North China Electric Power University – sequence: 3 givenname: Panpan surname: Guo fullname: Guo, Panpan organization: School of Control and Computer Engineering, North China Electric Power University
BookMark	eNqFkE1P3DAQhq2KSgXKT6hkqeeAP-LYUS9d8dFWWkSlhbM1SezF1BuHsWnFvyfL0h440NOMZt53Pp4Dsjem0RHyibNjzgw74UoKxZg5FkzMJd1KI8U7sr-tV4rxZu9fzswHcpDzHWNctK3YJ-vVBPir6iC7gf4EhBhdpFer6nx5SRdxnTCU2w1dTFMMPZSQRuoT0tVtwlIVhxu6TDDQi4Suh1zCuH7uX0LO4bejN9khPYMCH8l7DzG7o5d4SG4uzq9Pv1fLq28_ThfLqpfSlEq12nSyHTpfK8WMbDj0xrdOK-GHplMDzFd37dAo6aT2xnS6032reK2V8VzLQ_J5N3fCdP_gcrF36QHHeaUVdS2N0KxuZtWXnarHlDM6b_tQnr8rCCFazuyWrP1L1m7J2heys1u9ck8YNoCP__V93fnCODPawJ-EcbAFHmNCjzD2IVv59ogn8sCQUQ
CitedBy_id	crossref_primary_10_3390_en18010176 crossref_primary_10_1016_j_apenergy_2021_117798 crossref_primary_10_1155_2022_2171553 crossref_primary_10_1007_s11227_022_04659_1 crossref_primary_10_1080_15325008_2023_2185832 crossref_primary_10_1002_cpe_6967 crossref_primary_10_1155_2021_9669868 crossref_primary_10_1016_j_jestch_2021_02_016 crossref_primary_10_3389_fenrg_2024_1453039
Cites_doi	10.1109/NAPS.2014.6965453 10.1109/TSG.2013.2277171 10.1007/s11708-016-0393-y 10.1109/TSG.2016.2547964 10.1109/ACCESS.2017.2759509 10.1109/TSG.2017.2697440 10.1109/TIA.2016.2558563 10.1109/TSG.2014.2364233 10.1007/s00202-017-0587-2 10.1017/S1466252319000136
ContentType	Journal Article
Copyright	2020 Taylor & Francis Group, LLC 2020 2020 Taylor & Francis Group, LLC
Copyright_xml	– notice: 2020 Taylor & Francis Group, LLC 2020 – notice: 2020 Taylor & Francis Group, LLC
DBID	AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M
DOI	10.1080/15325008.2020.1793832
DatabaseName	CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Civil Engineering Abstracts Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Civil Engineering Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1532-5016
EndPage	614
ExternalDocumentID	10_1080_15325008_2020_1793832 1793832
Genre	Research Article
GroupedDBID	.7F .QJ 0BK 0R~ 29G 2DF 30N 4.4 5GY 5VS 8VB AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABFIM ABHAV ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGEJ ACGFS ACGOD ACIWK ACTIO ADCVX ADGTB ADXPE AEISY AENEX AEOZL AEPSL AEYOC AFKVX AFRVT AGDLA AGMYJ AHDZW AIJEM AIYEW AJWEG AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AQTUD AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO DU5 EBS E~A E~B FYQZC GTTXZ H13 HF~ HZ~ H~P IPNFZ J.P KYCEM LJTGL M4Z MS~ NA5 NX~ O9- PQQKQ QWB RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TDBHL TEN TFL TFT TFW TNC TTHFI TUROJ TWF UT5 UU3 ZGOLN ZL0 ~S~ AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M
ID	FETCH-LOGICAL-c338t-5978b39dbf45508361ac8f9e752fd6b5da992b9d653e37f88b7b7c9514758f173
IEDL.DBID	TFW
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000556479900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1532-5008
IngestDate	Wed Aug 13 04:27:22 EDT 2025 Sat Nov 29 05:38:03 EST 2025 Tue Nov 18 21:09:52 EST 2025 Mon Oct 20 23:48:20 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6-7
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c338t-5978b39dbf45508361ac8f9e752fd6b5da992b9d653e37f88b7b7c9514758f173
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	2443827046
PQPubID	52905
PageCount	12
ParticipantIDs	crossref_citationtrail_10_1080_15325008_2020_1793832 proquest_journals_2443827046 crossref_primary_10_1080_15325008_2020_1793832 informaworld_taylorfrancis_310_1080_15325008_2020_1793832
PublicationCentury	2000
PublicationDate	2020-08-06
PublicationDateYYYYMMDD	2020-08-06
PublicationDate_xml	– month: 08 year: 2020 text: 2020-08-06 day: 06
PublicationDecade	2020
PublicationPlace	Philadelphia
PublicationPlace_xml	– name: Philadelphia
PublicationTitle	Electric power components and systems
PublicationYear	2020
Publisher	Taylor & Francis Taylor & Francis Ltd
Publisher_xml	– name: Taylor & Francis – name: Taylor & Francis Ltd
References	Ben Z. (CIT0005) 2015; 35 CIT0010 Xiangrong Z. U. (CIT0013) 2016; 36 CIT0021 CIT0020 CIT0012 Gang X. (CIT0003) 2017; 41 Dewen W. (CIT0011) 2015; 35 Huixia Y. (CIT0014) 2017; 45 Baoyi W. (CIT0004) 2016; 36 CIT0002 CIT0016 CIT0015 CIT0007 Ekonomou L. (CIT0009) 2016; 1 CIT0018 Yundong G. (CIT0001) 2015; 30 CIT0006 CIT0017 CIT0008 CIT0019
References_xml	– ident: CIT0020 – volume: 36 start-page: 134 issue: 10 year: 2016 ident: CIT0013 publication-title: Electr. Power Autom. Equip. – ident: CIT0010 – volume: 35 start-page: 152 issue: 11 year: 2015 ident: CIT0005 publication-title: Electr. Power Autom. Equip. – volume: 35 start-page: 527 issue: 03 year: 2015 ident: CIT0011 publication-title: Proc. CSEE – ident: CIT0006 doi: 10.1109/NAPS.2014.6965453 – ident: CIT0015 doi: 10.1109/TSG.2013.2277171 – ident: CIT0007 doi: 10.1007/s11708-016-0393-y – volume: 1 start-page: 64 year: 2016 ident: CIT0009 publication-title: Int. J. Power Syst. – ident: CIT0021 doi: 10.1109/TSG.2016.2547964 – ident: CIT0017 doi: 10.1109/ACCESS.2017.2759509 – ident: CIT0018 doi: 10.1109/TSG.2017.2697440 – volume: 30 start-page: 110 issue: 23 year: 2015 ident: CIT0001 publication-title: Trans. China Electrotech. Soc. – ident: CIT0002 doi: 10.1109/TIA.2016.2558563 – ident: CIT0019 – ident: CIT0012 doi: 10.1109/TSG.2014.2364233 – volume: 36 start-page: 117 issue: 01 year: 2016 ident: CIT0004 publication-title: Electr. Power Autom. Equip. – ident: CIT0008 doi: 10.1007/s00202-017-0587-2 – volume: 45 start-page: 62 issue: 15 year: 2017 ident: CIT0014 publication-title: Power Syst. Prot. Control – ident: CIT0016 doi: 10.1017/S1466252319000136 – volume: 41 start-page: 1964 issue: 06 year: 2017 ident: CIT0003 publication-title: Power Syst. Technol.
SSID	ssj0012992
Score	2.2672882
Snippet	The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting...
SourceID	proquest crossref informaworld
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	603
SubjectTerms	Algorithms Big Data Clustering Data processing Datasets Electrical loads Electricity consumption Electricity meters Forecasting K-means algorithm Machine learning massive user load data Mathematical models parallel OS-ELM algorithm short-term load forecast Smart grid Spark Training User requirements
Title	Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data
URI	https://www.tandfonline.com/doi/abs/10.1080/15325008.2020.1793832 https://www.proquest.com/docview/2443827046
Volume	48
WOSCitedRecordID	wos000556479900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAWR databaseName: Taylor & Francis Journals Complete customDbUrl: eissn: 1532-5016 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012992 issn: 1532-5008 databaseCode: TFW dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NT8IwFG-M8aAHv40omh68Vrd1W7sjUYgHQBIgclu6rgUiAtmmf7-v-0CIMRx0t215zfL663u_t76-h9CdtG2hFXWI5NonLpea8MhhRAWepYAQSCrdvNkE63b5aBT0ymzCtEyrNDG0LgpF5LbaLG4RpVVG3AMsUnDclknMcuARIAxgCVYYmL3B-KD1utpHAGPrFBVTIeQCkeoMz2-jbHinjdqlP2x17oBaR__w6cfosGSfuFHA5QTtqPkpOlirSXiGxv2lSN6I8W4x7onE9FqZ4Zc-abY7uDEbL5JpNnnHje-Nbwyfj_sT4PHE2HncXogYm5afUqQmqTp_3wGSDoYVDwHy-Elk4hwNW83B4zMpuzEQCWFsRiDy4BEN4kibg9Cc-raA-Q0U8xwd-5EXC9B1FMS-RxVlmvOIRUwCgXMhJNE2oxdod76Yq0uEAwAOFVbMpAsXCFHlK8WBjIIfBQJYQ241C6EsS5Wbjhmz0C4rmlZ6DI0ew1KPNXS_ElsWtTq2CQTrUxxm-U8SXXQ0CekW2XqFh7Bc9mkIXIlyh1muf_WHoa_RvrnN0wz9OtrNkg91g_bkZzZNk9sc4F-KY_Oe
linkProvider	Taylor & Francis
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8MwDI5gIAEH3ojHgBy4BtambdLjBEwgtoG0TXCL0jTZJsaGusLvx-kDhhDaAXps5CiyHftz4tgInSnHkUZTlyhuAuJxZQiPXEZ06Nc0AAJFlZc1m2DtNn96Cmffwti0ShtDm7xQRGar7ea2h9FlStwF7FLw3DWbmeXCL1Ax0MtFtOSDr7Vpfd3G4-dNAphbN6-ZCkEX0JSveH6b5pt_-la99Ie1zlxQY-M_Fr-J1gsAiuu5xmyhBT3eRmszZQl3UL_zKpNnYh1cjB9kYtutjPB9h1w3W7g-6k-SYTp4wfWvu28M68edAUB5Yk09bk5kjG3XTyWnNq86G28BTgfbinug9fhKpnIX9RrX3csbUjRkIAoi2ZRA8MEjGsaRsW-hOQ0cCSIONfNdEweRH0tgdhTGgU81ZYbziEVMAYbzICoxDqN7qDKejPU-wiHoDpW1mCkPPiCiOtCaAx4FVwoY8AB5pRiEKqqV26YZI-EURU1LPgrLR1Hw8QCdf5K95uU65hGEszIWaXZOYvKmJoLOoa2WCiGKnT8VAJcod1nNCw7_MPUpWrnptpqiedu-O0KrdijLOgyqqJImb_oYLav3dDhNTjJt_wDyAPe_
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3JTsMwELWgIAQHdsSOD1wNTZzGzrGirUC0pVJB9GY5jt0iSlslge9nnKWAEOIAOSYayxo_zxKP3yB0rhxHGk1dorjxiceVITx0GdFBraohIFBUeVmzCdbt8sEg6BXVhElRVmlzaJMTRWS22m7uWWTKirhL2KTguKu2MMuFV4AwgOUiWsrIsQDS963H-UECWFs3p0yFnAtkyks8Pw3zxT19IS_9ZqwzD9Ta-Ie5b6L1IvzE9RwvW2hBT7bR2idSwh007M9k_Eyse4twT8a22coY3_VJs93B9fFwGj-loxdc_zj5xjB93B9BIE-socftqYyw7fmpZGKrqrPvHYjSwbLiB8A8bshU7qKHVvP-6poU7RiIgjw2JZB68JAGUWjsTWhOfUfCAgea1VwT-WEtkqDrMIj8GtWUGc5DFjIFEZwHOYlxGN1Dlcl0ovcRDgA5VFYjpjx4QIhqX2sO0Sg4UogAD5BXroJQBVe5bZkxFk5BaVrqUVg9ikKPB-hiLjbLyTp-Ewg-L7FIs78kJm9pIugvssclHkSx7xMBwRLlLqt6_uEfhj5DK71GS7RvurdHaNV-yUoO_WNUSeNXfYKW1Vv6lMSnGdbfAdT59mM
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=Spark-based+Parallel+OS-ELM+Algorithm+Application+for+Short-term+Load+Forecasting+for+Massive+User+Data&rft.jtitle=Electric+power+components+and+systems&rft.au=Li%2C+Yuancheng&rft.au=Yang%2C+Rongyan&rft.au=Guo%2C+Panpan&rft.date=2020-08-06&rft.issn=1532-5008&rft.eissn=1532-5016&rft.volume=48&rft.issue=6-7&rft.spage=603&rft.epage=614&rft_id=info:doi/10.1080%2F15325008.2020.1793832&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_15325008_2020_1793832
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1532-5008&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1532-5008&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1532-5008&client=summon