{"id":"https://openalex.org/W4206429903","doi":"https://doi.org/10.1109/bigdata52589.2021.9671748","title":"Transformer Oil Temperature Prediction Based on Long and Short-term Memory Networks","display_name":"Transformer Oil Temperature Prediction Based on Long and Short-term Memory Networks","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206429903","doi":"https://doi.org/10.1109/bigdata52589.2021.9671748"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671748","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031650069","display_name":"Jianxin Sui","orcid":null},"institutions":[{"id":"https://openalex.org/I4387155567","display_name":"State Grid Hunan Electric Power Company Limited","ror":"https://ror.org/00e17m144","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155567"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianxin Sui","raw_affiliation_strings":["Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China","institution_ids":["https://openalex.org/I4387155567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064655963","display_name":"Xiao Ling","orcid":"https://orcid.org/0000-0001-7216-6825"},"institutions":[{"id":"https://openalex.org/I4387155567","display_name":"State Grid Hunan Electric Power Company Limited","ror":"https://ror.org/00e17m144","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155567"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Ling","raw_affiliation_strings":["Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China","institution_ids":["https://openalex.org/I4387155567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111108279","display_name":"Xing Xiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4387155567","display_name":"State Grid Hunan Electric Power Company Limited","ror":"https://ror.org/00e17m144","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155567"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xiang","raw_affiliation_strings":["Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Information and Communication Branch of State Grid Hunan Electric Power Company Limited, Changsha, China","institution_ids":["https://openalex.org/I4387155567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102942392","display_name":"Genwei Zhang","orcid":"https://orcid.org/0000-0002-6177-4435"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genwei Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047273740","display_name":"Xiangchi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangchi Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031650069"],"corresponding_institution_ids":["https://openalex.org/I4387155567"],"apc_list":null,"apc_paid":null,"fwci":3.0292,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93258836,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6029","last_page":"6031"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.741601824760437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.682578444480896},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.6439058780670166},{"id":"https://openalex.org/keywords/transformer-oil","display_name":"Transformer oil","score":0.602161169052124},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5580487847328186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5464344620704651},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5003001689910889},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.494354248046875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47902432084083557},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.43489930033683777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3650810122489929},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.26437246799468994},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1625952124595642},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11657372117042542},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09816604852676392},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.06807878613471985}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.741601824760437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.682578444480896},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.6439058780670166},{"id":"https://openalex.org/C181335627","wikidata":"https://www.wikidata.org/wiki/Q901418","display_name":"Transformer oil","level":4,"score":0.602161169052124},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5580487847328186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5464344620704651},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5003001689910889},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.494354248046875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47902432084083557},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.43489930033683777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3650810122489929},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.26437246799468994},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1625952124595642},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11657372117042542},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09816604852676392},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.06807878613471985},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671748","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2495528827","https://openalex.org/W2512460811","https://openalex.org/W2768571736","https://openalex.org/W2809399184","https://openalex.org/W2884099630","https://openalex.org/W2955260208","https://openalex.org/W2958449190","https://openalex.org/W3011555880","https://openalex.org/W3018399397","https://openalex.org/W3043507573","https://openalex.org/W3095959679","https://openalex.org/W3110097300","https://openalex.org/W3189332573","https://openalex.org/W3190754914"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2523437662","https://openalex.org/W2024709534","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456"],"abstract_inverted_index":{"In":[0,22],"terms":[1],"of":[2,30,41],"transformer":[3,32],"oil":[4,15,33,53],"temperature":[5,16,34],"prediction,":[6],"it":[7],"has":[8,87],"been":[9],"a":[10],"challenge":[11],"to":[12,35,99],"effectively":[13],"predict":[14],"by":[17],"using":[18],"time":[19],"series":[20],"data.":[21],"this":[23],"paper,":[24],"we":[25,44,69,81],"focus":[26],"on":[27],"the":[28,38,46,52,57,60,66,73,77,91,95,100,104],"problem":[29],"predicting":[31],"accurately":[36],"get":[37,109],"running":[39],"state":[40],"transformer.":[42],"Firstly,":[43],"analyze":[45],"correlation":[47,62],"between":[48],"several":[49],"data":[50,58],"and":[51,55,76,112,116],"temperature,":[54],"then":[56],"with":[59],"higher":[61],"were":[63],"selected":[64],"for":[65],"experiment.":[67,97],"Secondly,":[68],"make":[70,113],"predictions":[71],"via":[72,94],"multi-layer":[74,92],"perceptron":[75,93],"LSTM":[78,85,105],"networks.":[79],"Finally,":[80],"can":[82,107],"learn":[83],"that":[84],"networks":[86,106],"better":[88,110],"performance":[89],"than":[90],"comparison":[96],"Compared":[98],"existing":[101],"prediction":[102],"strategies,":[103],"also":[108],"results":[111],"more":[114],"effective":[115],"efficient":[117],"prediction.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}