Power load prediction method based on dynamic decomposition-reconstruction integrated processing
The invention discloses a power load prediction method based on dynamic decomposition-reconstruction integrated processing, and the method comprises the steps: firstly obtaining power load data, carrying out the preprocessing, carrying out the decomposition of the data through mlptdense decompositio...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | ZHANG CHU CHEN JIE PENG TIAN WANG YIWEI WANG ZHENG GE YIDA ZHANG XUEDONG CHEN JIALEI ZHAO HUANYU |
| description | The invention discloses a power load prediction method based on dynamic decomposition-reconstruction integrated processing, and the method comprises the steps: firstly obtaining power load data, carrying out the preprocessing, carrying out the decomposition of the data through mlptdense decomposition, building a GCN-Reformer power load prediction model according to the decomposed components, carrying out the optimization of the Reformer hyper-parameters through an improved OOA algorithm, and carrying out the prediction of the power load. Selecting low-precision components needing secondary decomposition according to performance evaluation of decomposed components on a verification set, aggregating all the low-precision components by adopting permutation entropy to obtain a high-complexity component and a low-complexity component, performing secondary decomposition by adopting a WPD method, and then predicting all WPD decomposed D components by using a GCN-Reformer model, whether decomposition needs to be carr |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116822742A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116822742A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116822742A3</originalsourceid><addsrcrecordid>eNqNjLEKwjAURbM4iPoP8QM6NIq6SlGcxMG9xuS2Btr3Ql5E_Htb9AOcLgfOuVN1u_ALSXdsvY4JPrgcmHSP_GCv71bg9cD-TbYPTns47iNLGK0iDUSS0_MbBcpok80Yr9hBJFA7V5PGdoLFb2dqeTxcq1OByDUkWgdCrqtzWW52xmzXZr_6x_kAVR4-mQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Power load prediction method based on dynamic decomposition-reconstruction integrated processing</title><source>esp@cenet</source><creator>ZHANG CHU ; CHEN JIE ; PENG TIAN ; WANG YIWEI ; WANG ZHENG ; GE YIDA ; ZHANG XUEDONG ; CHEN JIALEI ; ZHAO HUANYU</creator><creatorcontrib>ZHANG CHU ; CHEN JIE ; PENG TIAN ; WANG YIWEI ; WANG ZHENG ; GE YIDA ; ZHANG XUEDONG ; CHEN JIALEI ; ZHAO HUANYU</creatorcontrib><description>The invention discloses a power load prediction method based on dynamic decomposition-reconstruction integrated processing, and the method comprises the steps: firstly obtaining power load data, carrying out the preprocessing, carrying out the decomposition of the data through mlptdense decomposition, building a GCN-Reformer power load prediction model according to the decomposed components, carrying out the optimization of the Reformer hyper-parameters through an improved OOA algorithm, and carrying out the prediction of the power load. Selecting low-precision components needing secondary decomposition according to performance evaluation of decomposed components on a verification set, aggregating all the low-precision components by adopting permutation entropy to obtain a high-complexity component and a low-complexity component, performing secondary decomposition by adopting a WPD method, and then predicting all WPD decomposed D components by using a GCN-Reformer model, whether decomposition needs to be carr</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230929&DB=EPODOC&CC=CN&NR=116822742A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230929&DB=EPODOC&CC=CN&NR=116822742A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG CHU</creatorcontrib><creatorcontrib>CHEN JIE</creatorcontrib><creatorcontrib>PENG TIAN</creatorcontrib><creatorcontrib>WANG YIWEI</creatorcontrib><creatorcontrib>WANG ZHENG</creatorcontrib><creatorcontrib>GE YIDA</creatorcontrib><creatorcontrib>ZHANG XUEDONG</creatorcontrib><creatorcontrib>CHEN JIALEI</creatorcontrib><creatorcontrib>ZHAO HUANYU</creatorcontrib><title>Power load prediction method based on dynamic decomposition-reconstruction integrated processing</title><description>The invention discloses a power load prediction method based on dynamic decomposition-reconstruction integrated processing, and the method comprises the steps: firstly obtaining power load data, carrying out the preprocessing, carrying out the decomposition of the data through mlptdense decomposition, building a GCN-Reformer power load prediction model according to the decomposed components, carrying out the optimization of the Reformer hyper-parameters through an improved OOA algorithm, and carrying out the prediction of the power load. Selecting low-precision components needing secondary decomposition according to performance evaluation of decomposed components on a verification set, aggregating all the low-precision components by adopting permutation entropy to obtain a high-complexity component and a low-complexity component, performing secondary decomposition by adopting a WPD method, and then predicting all WPD decomposed D components by using a GCN-Reformer model, whether decomposition needs to be carr</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjLEKwjAURbM4iPoP8QM6NIq6SlGcxMG9xuS2Btr3Ql5E_Htb9AOcLgfOuVN1u_ALSXdsvY4JPrgcmHSP_GCv71bg9cD-TbYPTns47iNLGK0iDUSS0_MbBcpok80Yr9hBJFA7V5PGdoLFb2dqeTxcq1OByDUkWgdCrqtzWW52xmzXZr_6x_kAVR4-mQ</recordid><startdate>20230929</startdate><enddate>20230929</enddate><creator>ZHANG CHU</creator><creator>CHEN JIE</creator><creator>PENG TIAN</creator><creator>WANG YIWEI</creator><creator>WANG ZHENG</creator><creator>GE YIDA</creator><creator>ZHANG XUEDONG</creator><creator>CHEN JIALEI</creator><creator>ZHAO HUANYU</creator><scope>EVB</scope></search><sort><creationdate>20230929</creationdate><title>Power load prediction method based on dynamic decomposition-reconstruction integrated processing</title><author>ZHANG CHU ; CHEN JIE ; PENG TIAN ; WANG YIWEI ; WANG ZHENG ; GE YIDA ; ZHANG XUEDONG ; CHEN JIALEI ; ZHAO HUANYU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116822742A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHANG CHU</creatorcontrib><creatorcontrib>CHEN JIE</creatorcontrib><creatorcontrib>PENG TIAN</creatorcontrib><creatorcontrib>WANG YIWEI</creatorcontrib><creatorcontrib>WANG ZHENG</creatorcontrib><creatorcontrib>GE YIDA</creatorcontrib><creatorcontrib>ZHANG XUEDONG</creatorcontrib><creatorcontrib>CHEN JIALEI</creatorcontrib><creatorcontrib>ZHAO HUANYU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHANG CHU</au><au>CHEN JIE</au><au>PENG TIAN</au><au>WANG YIWEI</au><au>WANG ZHENG</au><au>GE YIDA</au><au>ZHANG XUEDONG</au><au>CHEN JIALEI</au><au>ZHAO HUANYU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Power load prediction method based on dynamic decomposition-reconstruction integrated processing</title><date>2023-09-29</date><risdate>2023</risdate><abstract>The invention discloses a power load prediction method based on dynamic decomposition-reconstruction integrated processing, and the method comprises the steps: firstly obtaining power load data, carrying out the preprocessing, carrying out the decomposition of the data through mlptdense decomposition, building a GCN-Reformer power load prediction model according to the decomposed components, carrying out the optimization of the Reformer hyper-parameters through an improved OOA algorithm, and carrying out the prediction of the power load. Selecting low-precision components needing secondary decomposition according to performance evaluation of decomposed components on a verification set, aggregating all the low-precision components by adopting permutation entropy to obtain a high-complexity component and a low-complexity component, performing secondary decomposition by adopting a WPD method, and then predicting all WPD decomposed D components by using a GCN-Reformer model, whether decomposition needs to be carr</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116822742A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Power load prediction method based on dynamic decomposition-reconstruction integrated processing |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T19%3A32%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHANG%20CHU&rft.date=2023-09-29&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116822742A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |