Method for predicting load of electric vehicle under condition of data shortage
The invention discloses an electric vehicle load prediction method under data shortage, and the method comprises the steps: enabling a conventional GAN to learn a potential relation between observation values containing irregular time lag through employing a GRUI cell structure for the irregular tim...
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| creator | XU XIAODONG GUO MING QIU WEIJIE XIAO XIAOBING ZHANG RUIFENG LIN SHUNSHENG YANG QIANG TAN BIN MA XIN SHEN WEI LU SENWEI XING ZHAOSEN DING YUJIE WU JUNFENG LONG XIAOBIN SHI QIHONG ZHAO YUANLIANG |
| description | The invention discloses an electric vehicle load prediction method under data shortage, and the method comprises the steps: enabling a conventional GAN to learn a potential relation between observation values containing irregular time lag through employing a GRUI cell structure for the irregular time lag change between front and back effective observation values in EV load data caused by a vacancy value; data restoration is carried out through an interpolation method adapting to EV load data, and a data set is obtained; the performance of the LSTM network is improved by adopting a Mogrifier gating mechanism, and an EV short-term load prediction result is obtained on the processed data set; the technical problem of low EV load prediction precision caused by data missing and data abnormity in the prior art is solved.
本发明公开了一种数据缺乏下电动汽车负荷预测方法，所述方法为：针对由于空缺值所导致EV负荷数据中前后有效观测值之间的不规则的时滞变化，采用GRUI细胞结构让传统GAN学习到包含不规则时滞的观测值之间的潜在联系，从而适应EV负荷数据的插补方法来进行数据修复，得到数据集；采用Mogrifier门控机制提升LSTM网络性能在经处理过的数据集上获取EV短期负荷预测结果；解决了现有技术数据缺失和数据异常 |
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本发明公开了一种数据缺乏下电动汽车负荷预测方法，所述方法为：针对由于空缺值所导致EV负荷数据中前后有效观测值之间的不规则的时滞变化，采用GRUI细胞结构让传统GAN学习到包含不规则时滞的观测值之间的潜在联系，从而适应EV负荷数据的插补方法来进行数据修复，得到数据集；采用Mogrifier门控机制提升LSTM网络性能在经处理过的数据集上获取EV短期负荷预测结果；解决了现有技术数据缺失和数据异常</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 ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2022</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=20220415&DB=EPODOC&CC=CN&NR=114358362A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220415&DB=EPODOC&CC=CN&NR=114358362A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XU XIAODONG</creatorcontrib><creatorcontrib>GUO MING</creatorcontrib><creatorcontrib>QIU WEIJIE</creatorcontrib><creatorcontrib>XIAO XIAOBING</creatorcontrib><creatorcontrib>ZHANG RUIFENG</creatorcontrib><creatorcontrib>LIN SHUNSHENG</creatorcontrib><creatorcontrib>YANG QIANG</creatorcontrib><creatorcontrib>TAN BIN</creatorcontrib><creatorcontrib>MA XIN</creatorcontrib><creatorcontrib>SHEN WEI</creatorcontrib><creatorcontrib>LU SENWEI</creatorcontrib><creatorcontrib>XING ZHAOSEN</creatorcontrib><creatorcontrib>DING YUJIE</creatorcontrib><creatorcontrib>WU JUNFENG</creatorcontrib><creatorcontrib>LONG XIAOBIN</creatorcontrib><creatorcontrib>SHI QIHONG</creatorcontrib><creatorcontrib>ZHAO YUANLIANG</creatorcontrib><title>Method for predicting load of electric vehicle under condition of data shortage</title><description>The invention discloses an electric vehicle load prediction method under data shortage, and the method comprises the steps: enabling a conventional GAN to learn a potential relation between observation values containing irregular time lag through employing a GRUI cell structure for the irregular time lag change between front and back effective observation values in EV load data caused by a vacancy value; data restoration is carried out through an interpolation method adapting to EV load data, and a data set is obtained; the performance of the LSTM network is improved by adopting a Mogrifier gating mechanism, and an EV short-term load prediction result is obtained on the processed data set; the technical problem of low EV load prediction precision caused by data missing and data abnormity in the prior art is solved.
本发明公开了一种数据缺乏下电动汽车负荷预测方法，所述方法为：针对由于空缺值所导致EV负荷数据中前后有效观测值之间的不规则的时滞变化，采用GRUI细胞结构让传统GAN学习到包含不规则时滞的观测值之间的潜在联系，从而适应EV负荷数据的插补方法来进行数据修复，得到数据集；采用Mogrifier门控机制提升LSTM网络性能在经处理过的数据集上获取EV短期负荷预测结果；解决了现有技术数据缺失和数据异常</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>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>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyjEKAjEQBdA0FqLeYTyAxRoVW1kUG7WxX4bk7yYQMiEZPb8IHsDqNW9uHjdoEE-jVCoVPjqNeaIk7ElGQoLTGh29EaJLoFf2qOQk-6hR8vd4VqYWpCpPWJrZyKlh9XNh1pfzs79uUGRAK-yQoUN_77qd3R_tYXuy_5wPOg42ug</recordid><startdate>20220415</startdate><enddate>20220415</enddate><creator>XU XIAODONG</creator><creator>GUO MING</creator><creator>QIU WEIJIE</creator><creator>XIAO XIAOBING</creator><creator>ZHANG RUIFENG</creator><creator>LIN SHUNSHENG</creator><creator>YANG QIANG</creator><creator>TAN BIN</creator><creator>MA XIN</creator><creator>SHEN WEI</creator><creator>LU SENWEI</creator><creator>XING ZHAOSEN</creator><creator>DING YUJIE</creator><creator>WU JUNFENG</creator><creator>LONG XIAOBIN</creator><creator>SHI QIHONG</creator><creator>ZHAO YUANLIANG</creator><scope>EVB</scope></search><sort><creationdate>20220415</creationdate><title>Method for predicting load of electric vehicle under condition of data shortage</title><author>XU XIAODONG ; GUO MING ; QIU WEIJIE ; XIAO XIAOBING ; ZHANG RUIFENG ; LIN SHUNSHENG ; YANG QIANG ; TAN BIN ; MA XIN ; SHEN WEI ; LU SENWEI ; XING ZHAOSEN ; DING YUJIE ; WU JUNFENG ; LONG XIAOBIN ; SHI QIHONG ; ZHAO YUANLIANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114358362A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</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>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>XU XIAODONG</creatorcontrib><creatorcontrib>GUO MING</creatorcontrib><creatorcontrib>QIU WEIJIE</creatorcontrib><creatorcontrib>XIAO XIAOBING</creatorcontrib><creatorcontrib>ZHANG RUIFENG</creatorcontrib><creatorcontrib>LIN SHUNSHENG</creatorcontrib><creatorcontrib>YANG QIANG</creatorcontrib><creatorcontrib>TAN BIN</creatorcontrib><creatorcontrib>MA XIN</creatorcontrib><creatorcontrib>SHEN WEI</creatorcontrib><creatorcontrib>LU SENWEI</creatorcontrib><creatorcontrib>XING ZHAOSEN</creatorcontrib><creatorcontrib>DING YUJIE</creatorcontrib><creatorcontrib>WU JUNFENG</creatorcontrib><creatorcontrib>LONG XIAOBIN</creatorcontrib><creatorcontrib>SHI QIHONG</creatorcontrib><creatorcontrib>ZHAO YUANLIANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XU XIAODONG</au><au>GUO MING</au><au>QIU WEIJIE</au><au>XIAO XIAOBING</au><au>ZHANG RUIFENG</au><au>LIN SHUNSHENG</au><au>YANG QIANG</au><au>TAN BIN</au><au>MA XIN</au><au>SHEN WEI</au><au>LU SENWEI</au><au>XING ZHAOSEN</au><au>DING YUJIE</au><au>WU JUNFENG</au><au>LONG XIAOBIN</au><au>SHI QIHONG</au><au>ZHAO YUANLIANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Method for predicting load of electric vehicle under condition of data shortage</title><date>2022-04-15</date><risdate>2022</risdate><abstract>The invention discloses an electric vehicle load prediction method under data shortage, and the method comprises the steps: enabling a conventional GAN to learn a potential relation between observation values containing irregular time lag through employing a GRUI cell structure for the irregular time lag change between front and back effective observation values in EV load data caused by a vacancy value; data restoration is carried out through an interpolation method adapting to EV load data, and a data set is obtained; the performance of the LSTM network is improved by adopting a Mogrifier gating mechanism, and an EV short-term load prediction result is obtained on the processed data set; the technical problem of low EV load prediction precision caused by data missing and data abnormity in the prior art is solved.
本发明公开了一种数据缺乏下电动汽车负荷预测方法，所述方法为：针对由于空缺值所导致EV负荷数据中前后有效观测值之间的不规则的时滞变化，采用GRUI细胞结构让传统GAN学习到包含不规则时滞的观测值之间的潜在联系，从而适应EV负荷数据的插补方法来进行数据修复，得到数据集；采用Mogrifier门控机制提升LSTM网络性能在经处理过的数据集上获取EV短期负荷预测结果；解决了现有技术数据缺失和数据异常</abstract><oa>free_for_read</oa></addata></record> |
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| title | Method for predicting load of electric vehicle under condition of data shortage |
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