Power grid electric quantity prediction method based on data mining and neural network

The invention relates to a power grid electric quantity prediction method based on data mining and a neural network, and the method comprises the following steps: firstly, data cleaning and recovery: employing a 3 sigma principle to remove abnormal points, and employing secondary Lagrange interpolat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: ZHENG PIAOPIAO, PAN DAN, YANG JINHUAI, CHEN RAN, LIN JIANCHEN, YANG QIFAN, CHEN JING
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 ZHENG PIAOPIAO
PAN DAN
YANG JINHUAI
CHEN RAN
LIN JIANCHEN
YANG QIFAN
CHEN JING
description The invention relates to a power grid electric quantity prediction method based on data mining and a neural network, and the method comprises the following steps: firstly, data cleaning and recovery: employing a 3 sigma principle to remove abnormal points, and employing secondary Lagrange interpolation to recover missing data, and avoiding the influence of the missing data on time continuity; then, constructing electric quantity characteristics from internal and external factors, considering historical electric quantity data for the internal factors, combining the historical electric quantity data with time coding, considering factors such as regional population and economy for the external factors, and introducing the factors into a prediction model; and finally, establishing a prediction model based on a Prophet model and an LSTM model, carrying out component decomposition on the power grid electric quantity data, respectively establishing LSTM networks for different components, carrying out fitting on the
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117422175A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117422175A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117422175A3</originalsourceid><addsrcrecordid>eNqNyrEKwjAQgOEsDqK-w_kADqlKZymKkziIazmTsz1Mk5hcKb69GXwAp48f_rm6X8NECbrEFsiRkcQG3iN6YflATGTZCAcPA0kfLDwwk4XSFgVhYM--A_QWPI0JXUGmkF5LNXuiy7T6uVDr0_HWnDcUQ0s5oqFyts1F63pXVbreH7b_PF8upTmk</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Power grid electric quantity prediction method based on data mining and neural network</title><source>esp@cenet</source><creator>ZHENG PIAOPIAO ; PAN DAN ; YANG JINHUAI ; CHEN RAN ; LIN JIANCHEN ; YANG QIFAN ; CHEN JING</creator><creatorcontrib>ZHENG PIAOPIAO ; PAN DAN ; YANG JINHUAI ; CHEN RAN ; LIN JIANCHEN ; YANG QIFAN ; CHEN JING</creatorcontrib><description>The invention relates to a power grid electric quantity prediction method based on data mining and a neural network, and the method comprises the following steps: firstly, data cleaning and recovery: employing a 3 sigma principle to remove abnormal points, and employing secondary Lagrange interpolation to recover missing data, and avoiding the influence of the missing data on time continuity; then, constructing electric quantity characteristics from internal and external factors, considering historical electric quantity data for the internal factors, combining the historical electric quantity data with time coding, considering factors such as regional population and economy for the external factors, and introducing the factors into a prediction model; and finally, establishing a prediction model based on a Prophet model and an LSTM model, carrying out component decomposition on the power grid electric quantity data, respectively establishing LSTM networks for different components, carrying out fitting on the</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>2024</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&amp;date=20240119&amp;DB=EPODOC&amp;CC=CN&amp;NR=117422175A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240119&amp;DB=EPODOC&amp;CC=CN&amp;NR=117422175A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHENG PIAOPIAO</creatorcontrib><creatorcontrib>PAN DAN</creatorcontrib><creatorcontrib>YANG JINHUAI</creatorcontrib><creatorcontrib>CHEN RAN</creatorcontrib><creatorcontrib>LIN JIANCHEN</creatorcontrib><creatorcontrib>YANG QIFAN</creatorcontrib><creatorcontrib>CHEN JING</creatorcontrib><title>Power grid electric quantity prediction method based on data mining and neural network</title><description>The invention relates to a power grid electric quantity prediction method based on data mining and a neural network, and the method comprises the following steps: firstly, data cleaning and recovery: employing a 3 sigma principle to remove abnormal points, and employing secondary Lagrange interpolation to recover missing data, and avoiding the influence of the missing data on time continuity; then, constructing electric quantity characteristics from internal and external factors, considering historical electric quantity data for the internal factors, combining the historical electric quantity data with time coding, considering factors such as regional population and economy for the external factors, and introducing the factors into a prediction model; and finally, establishing a prediction model based on a Prophet model and an LSTM model, carrying out component decomposition on the power grid electric quantity data, respectively establishing LSTM networks for different components, carrying out fitting on the</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>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQgOEsDqK-w_kADqlKZymKkziIazmTsz1Mk5hcKb69GXwAp48f_rm6X8NECbrEFsiRkcQG3iN6YflATGTZCAcPA0kfLDwwk4XSFgVhYM--A_QWPI0JXUGmkF5LNXuiy7T6uVDr0_HWnDcUQ0s5oqFyts1F63pXVbreH7b_PF8upTmk</recordid><startdate>20240119</startdate><enddate>20240119</enddate><creator>ZHENG PIAOPIAO</creator><creator>PAN DAN</creator><creator>YANG JINHUAI</creator><creator>CHEN RAN</creator><creator>LIN JIANCHEN</creator><creator>YANG QIFAN</creator><creator>CHEN JING</creator><scope>EVB</scope></search><sort><creationdate>20240119</creationdate><title>Power grid electric quantity prediction method based on data mining and neural network</title><author>ZHENG PIAOPIAO ; PAN DAN ; YANG JINHUAI ; CHEN RAN ; LIN JIANCHEN ; YANG QIFAN ; CHEN JING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117422175A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</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>ZHENG PIAOPIAO</creatorcontrib><creatorcontrib>PAN DAN</creatorcontrib><creatorcontrib>YANG JINHUAI</creatorcontrib><creatorcontrib>CHEN RAN</creatorcontrib><creatorcontrib>LIN JIANCHEN</creatorcontrib><creatorcontrib>YANG QIFAN</creatorcontrib><creatorcontrib>CHEN JING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHENG PIAOPIAO</au><au>PAN DAN</au><au>YANG JINHUAI</au><au>CHEN RAN</au><au>LIN JIANCHEN</au><au>YANG QIFAN</au><au>CHEN JING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Power grid electric quantity prediction method based on data mining and neural network</title><date>2024-01-19</date><risdate>2024</risdate><abstract>The invention relates to a power grid electric quantity prediction method based on data mining and a neural network, and the method comprises the following steps: firstly, data cleaning and recovery: employing a 3 sigma principle to remove abnormal points, and employing secondary Lagrange interpolation to recover missing data, and avoiding the influence of the missing data on time continuity; then, constructing electric quantity characteristics from internal and external factors, considering historical electric quantity data for the internal factors, combining the historical electric quantity data with time coding, considering factors such as regional population and economy for the external factors, and introducing the factors into a prediction model; and finally, establishing a prediction model based on a Prophet model and an LSTM model, carrying out component decomposition on the power grid electric quantity data, respectively establishing LSTM networks for different components, carrying out fitting on the</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN117422175A
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 grid electric quantity prediction method based on data mining and neural network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T17%3A18%3A45IST&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=ZHENG%20PIAOPIAO&rft.date=2024-01-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117422175A%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