Power load prediction method considering temperature fuzzification
The invention discloses a power load prediction method considering temperature fuzzification, and the method comprises the steps: firstly collecting historical load data, historical temperature dataand related date data of a power grid, processing the historical load data, historical temperature dat...
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| creator | XIAO XIANYONG ZHANG SHU WANG QING ZHENG RUIXIAO CAI SHAORONG |
| description | The invention discloses a power load prediction method considering temperature fuzzification, and the method comprises the steps: firstly collecting historical load data, historical temperature dataand related date data of a power grid, processing the historical load data, historical temperature data and related date data into a 15-dimensional feature vector, and dividing the 15-dimensional feature vector into a training data set and a test data set in proportion; establishing a three-layer long-short-term memory neural network, and performing iterative training on the three-layer long-short-term memory neural network through the training data set to obtain a power load prediction model; and finally, inputting prediction day data in the test data set into the power load prediction modelto obtain a power load prediction value. According to the invention, the short-term load can be accurately predicted by considering the load timing characteristics; and meanwhile, a membership function is used for fuzzifying th |
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According to the invention, the short-term load can be accurately predicted by considering the load timing characteristics; and meanwhile, a membership function is used for fuzzifying th</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>2020</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=20200515&DB=EPODOC&CC=CN&NR=111160659A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200515&DB=EPODOC&CC=CN&NR=111160659A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XIAO XIANYONG</creatorcontrib><creatorcontrib>ZHANG SHU</creatorcontrib><creatorcontrib>WANG QING</creatorcontrib><creatorcontrib>ZHENG RUIXIAO</creatorcontrib><creatorcontrib>CAI SHAORONG</creatorcontrib><title>Power load prediction method considering temperature fuzzification</title><description>The invention discloses a power load prediction method considering temperature fuzzification, and the method comprises the steps: firstly collecting historical load data, historical temperature dataand related date data of a power grid, processing the historical load data, historical temperature data and related date data into a 15-dimensional feature vector, and dividing the 15-dimensional feature vector into a training data set and a test data set in proportion; establishing a three-layer long-short-term memory neural network, and performing iterative training on the three-layer long-short-term memory neural network through the training data set to obtain a power load prediction model; and finally, inputting prediction day data in the test data set into the power load prediction modelto obtain a power load prediction value. According to the invention, the short-term load can be accurately predicted by considering the load timing characteristics; and meanwhile, a membership function is used for fuzzifying th</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>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHAKyC9PLVLIyU9MUSgoSk3JTC7JzM9TyE0tychPUUjOzyvOTEktysxLVyhJzS1ILUosKS1KVUgrrarKTMtMTgQp5mFgTUvMKU7lhdLcDIpuriHOHrqpBfnxqcUFicmpeakl8c5-hkBgZmBmauloTIwaAKuJMww</recordid><startdate>20200515</startdate><enddate>20200515</enddate><creator>XIAO XIANYONG</creator><creator>ZHANG SHU</creator><creator>WANG QING</creator><creator>ZHENG RUIXIAO</creator><creator>CAI SHAORONG</creator><scope>EVB</scope></search><sort><creationdate>20200515</creationdate><title>Power load prediction method considering temperature fuzzification</title><author>XIAO XIANYONG ; ZHANG SHU ; WANG QING ; ZHENG RUIXIAO ; CAI SHAORONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN111160659A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</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>XIAO XIANYONG</creatorcontrib><creatorcontrib>ZHANG SHU</creatorcontrib><creatorcontrib>WANG QING</creatorcontrib><creatorcontrib>ZHENG RUIXIAO</creatorcontrib><creatorcontrib>CAI SHAORONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XIAO XIANYONG</au><au>ZHANG SHU</au><au>WANG QING</au><au>ZHENG RUIXIAO</au><au>CAI SHAORONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Power load prediction method considering temperature fuzzification</title><date>2020-05-15</date><risdate>2020</risdate><abstract>The invention discloses a power load prediction method considering temperature fuzzification, and the method comprises the steps: firstly collecting historical load data, historical temperature dataand related date data of a power grid, processing the historical load data, historical temperature data and related date data into a 15-dimensional feature vector, and dividing the 15-dimensional feature vector into a training data set and a test data set in proportion; establishing a three-layer long-short-term memory neural network, and performing iterative training on the three-layer long-short-term memory neural network through the training data set to obtain a power load prediction model; and finally, inputting prediction day data in the test data set into the power load prediction modelto obtain a power load prediction value. 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| 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 PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Power load prediction method considering temperature fuzzification |
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