Machine learning model training method and system for power load identification
The embodiment of the invention provides a machine learning model training method and system for power load identification. Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network mod...
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| creator | ZHANG LINSHAN LUO YONGMU XUANYUAN ZHE LI JIA ZOU JINGXI CAO MIN ZHOU NIANRONG WANG HAO LI BO ZHU QUANCONG |
| description | The embodiment of the invention provides a machine learning model training method and system for power load identification. Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. According to the method, the model can be trained according to the input sampling data, so that the electric equipment in use can be identified according to waveform sampling data of specific voltage, current and active power; therefore, manual parameter adjustment and feature extraction are not needed, and the feature parameters required for identifying the powerload can be autonomously |
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Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. According to the method, the model can be trained according to the input sampling data, so that the electric equipment in use can be identified according to waveform sampling data of specific voltage, current and active power; therefore, manual parameter adjustment and feature extraction are not needed, and the feature parameters required for identifying the powerload can be autonomously</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=20200605&DB=EPODOC&CC=CN&NR=111242391A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200605&DB=EPODOC&CC=CN&NR=111242391A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG LINSHAN</creatorcontrib><creatorcontrib>LUO YONGMU</creatorcontrib><creatorcontrib>XUANYUAN ZHE</creatorcontrib><creatorcontrib>LI JIA</creatorcontrib><creatorcontrib>ZOU JINGXI</creatorcontrib><creatorcontrib>CAO MIN</creatorcontrib><creatorcontrib>ZHOU NIANRONG</creatorcontrib><creatorcontrib>WANG HAO</creatorcontrib><creatorcontrib>LI BO</creatorcontrib><creatorcontrib>ZHU QUANCONG</creatorcontrib><title>Machine learning model training method and system for power load identification</title><description>The embodiment of the invention provides a machine learning model training method and system for power load identification. Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. 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Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. According to the method, the model can be trained according to the input sampling data, so that the electric equipment in use can be identified according to waveform sampling data of specific voltage, current and active power; therefore, manual parameter adjustment and feature extraction are not needed, and the feature parameters required for identifying the powerload can be autonomously</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 PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Machine learning model training method and system for power load identification |
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