Building load prediction method and system considering load curve characteristics
The invention provides a building load prediction method and system considering load curve characteristics. The method comprises the steps: obtaining building load data at a set time interval, and taking a plurality of data points in a certain time period as a group of samples; filtering the buildin...
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| creator | LI XINGYU PEI LIWEI GONG CHIYU QI XINYUN LYU BIN ZHANG XU WANG XIAOJIE PAN GUANGXU WANG RUIQI LI YINGJIE GUO JIAN HU JUN JIN XINKAI ZHANG CHENGXIANG SHI HONG LIAN YAN YANG QING |
| description | The invention provides a building load prediction method and system considering load curve characteristics. The method comprises the steps: obtaining building load data at a set time interval, and taking a plurality of data points in a certain time period as a group of samples; filtering the building load data to obtain a smooth curve of the load data; discrete data inflection points in each group of samples are obtained to form an inflection point sequence, the slope of a fitting curve of each group of samples is calculated, and the slope corresponding to each historical data point forms a slope sequence; combining the historical data sequence, the inflection point sequence and the slope sequence into input of a neural network model, and training the neural network model; testing the trained neural network model, and predicting the building load data by using the neural network model meeting the test requirements to obtain a prediction result; the method not only considers the data characteristics of the loa |
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The method comprises the steps: obtaining building load data at a set time interval, and taking a plurality of data points in a certain time period as a group of samples; filtering the building load data to obtain a smooth curve of the load data; discrete data inflection points in each group of samples are obtained to form an inflection point sequence, the slope of a fitting curve of each group of samples is calculated, and the slope corresponding to each historical data point forms a slope sequence; combining the historical data sequence, the inflection point sequence and the slope sequence into input of a neural network model, and training the neural network model; testing the trained neural network model, and predicting the building load data by using the neural network model meeting the test requirements to obtain a prediction result; the method not only considers the data characteristics of the loa</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>2021</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=20210507&DB=EPODOC&CC=CN&NR=112766535A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210507&DB=EPODOC&CC=CN&NR=112766535A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI XINGYU</creatorcontrib><creatorcontrib>PEI LIWEI</creatorcontrib><creatorcontrib>GONG CHIYU</creatorcontrib><creatorcontrib>QI XINYUN</creatorcontrib><creatorcontrib>LYU BIN</creatorcontrib><creatorcontrib>ZHANG XU</creatorcontrib><creatorcontrib>WANG XIAOJIE</creatorcontrib><creatorcontrib>PAN GUANGXU</creatorcontrib><creatorcontrib>WANG RUIQI</creatorcontrib><creatorcontrib>LI YINGJIE</creatorcontrib><creatorcontrib>GUO JIAN</creatorcontrib><creatorcontrib>HU JUN</creatorcontrib><creatorcontrib>JIN XINKAI</creatorcontrib><creatorcontrib>ZHANG CHENGXIANG</creatorcontrib><creatorcontrib>SHI HONG</creatorcontrib><creatorcontrib>LIAN YAN</creatorcontrib><creatorcontrib>YANG QING</creatorcontrib><title>Building load prediction method and system considering load curve characteristics</title><description>The invention provides a building load prediction method and system considering load curve characteristics. The method comprises the steps: obtaining building load data at a set time interval, and taking a plurality of data points in a certain time period as a group of samples; filtering the building load data to obtain a smooth curve of the load data; discrete data inflection points in each group of samples are obtained to form an inflection point sequence, the slope of a fitting curve of each group of samples is calculated, and the slope corresponding to each historical data point forms a slope sequence; combining the historical data sequence, the inflection point sequence and the slope sequence into input of a neural network model, and training the neural network model; testing the trained neural network model, and predicting the building load data by using the neural network model meeting the test requirements to obtain a prediction result; the method not only considers the data characteristics of the loa</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>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwkAMgOFbHER9h_gADrW0zloUJ0FwLyEXbeB6d1xSwbfXQZydfvj55u56mCR4iQ8ICT3kwl7IJEUY2YbkAaMHfanxCJSiiufy0zSVJwMNWJDs89WEdOlmdwzKq28Xbn063rrzhnPqWTMSR7a-u1TVdte2Td3s63_MGxdrOE4</recordid><startdate>20210507</startdate><enddate>20210507</enddate><creator>LI XINGYU</creator><creator>PEI LIWEI</creator><creator>GONG CHIYU</creator><creator>QI XINYUN</creator><creator>LYU BIN</creator><creator>ZHANG XU</creator><creator>WANG XIAOJIE</creator><creator>PAN GUANGXU</creator><creator>WANG RUIQI</creator><creator>LI YINGJIE</creator><creator>GUO JIAN</creator><creator>HU JUN</creator><creator>JIN XINKAI</creator><creator>ZHANG CHENGXIANG</creator><creator>SHI HONG</creator><creator>LIAN YAN</creator><creator>YANG QING</creator><scope>EVB</scope></search><sort><creationdate>20210507</creationdate><title>Building load prediction method and system considering load curve characteristics</title><author>LI XINGYU ; 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The method comprises the steps: obtaining building load data at a set time interval, and taking a plurality of data points in a certain time period as a group of samples; filtering the building load data to obtain a smooth curve of the load data; discrete data inflection points in each group of samples are obtained to form an inflection point sequence, the slope of a fitting curve of each group of samples is calculated, and the slope corresponding to each historical data point forms a slope sequence; combining the historical data sequence, the inflection point sequence and the slope sequence into input of a neural network model, and training the neural network model; testing the trained neural network model, and predicting the building load data by using the neural network model meeting the test requirements to obtain a prediction result; the method not only considers the data characteristics of the loa</abstract><oa>free_for_read</oa></addata></record> |
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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 ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Building load prediction method and system considering load curve characteristics |
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