{"id":"https://openalex.org/W4387730314","doi":"https://doi.org/10.1186/s13634-023-01068-1","title":"A deep learning-based load forecasting algorithm for energy consumption monitoring system using dimension expansion","display_name":"A deep learning-based load forecasting algorithm for energy consumption monitoring system using dimension expansion","publication_year":2023,"publication_date":"2023-10-18","ids":{"openalex":"https://openalex.org/W4387730314","doi":"https://doi.org/10.1186/s13634-023-01068-1"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-023-01068-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-023-01068-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01068-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01068-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011736994","display_name":"Wei-guo Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei-guo Zhang","raw_affiliation_strings":["Southeast University, Nanjing, 210096, China"],"raw_orcid":"https://orcid.org/0009-0007-3427-8923","affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, 210096, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100779836","display_name":"Qing Zhu","orcid":"https://orcid.org/0000-0003-2262-2743"},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Zhu","raw_affiliation_strings":["NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","Nari Technology Co., Ltd., Nanjing, 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]},{"raw_affiliation_string":"Nari Technology Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100820585","display_name":"Linlin Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin-Lin Gu","raw_affiliation_strings":["NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","Nari Technology Co., Ltd., Nanjing, 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]},{"raw_affiliation_string":"Nari Technology Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081099069","display_name":"Huijie Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui-Jie Lin","raw_affiliation_strings":["NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","Nari Technology Co., Ltd., Nanjing, 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NARI Nanjing Control System Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]},{"raw_affiliation_string":"Nari Technology Co., Ltd., Nanjing, 211106, China","institution_ids":["https://openalex.org/I4210118629"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011736994"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.9291,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74648023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"2023","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9919000267982483,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/computer-science","display_name":"Computer science","score":0.8037266731262207},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6501414775848389},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5862314105033875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5095136761665344},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5077946782112122},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4986701011657715},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49207228422164917},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4410947859287262},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.42835482954978943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4223008155822754},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4200458824634552},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4194210469722748},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4108525514602661},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4065094590187073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.369717001914978},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34379881620407104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08900126814842224},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.0813295841217041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8037266731262207},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6501414775848389},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5862314105033875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5095136761665344},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5077946782112122},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4986701011657715},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49207228422164917},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4410947859287262},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.42835482954978943},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4223008155822754},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4200458824634552},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4194210469722748},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4108525514602661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4065094590187073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.369717001914978},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34379881620407104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08900126814842224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0813295841217041},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-023-01068-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-023-01068-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01068-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b4b62cfd3ee14557acdca0c47fee2364","is_oa":true,"landing_page_url":"https://doaj.org/article/b4b62cfd3ee14557acdca0c47fee2364","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2023, Iss 1, Pp 1-13 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-023-01068-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-023-01068-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01068-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9100000262260437}],"awards":[{"id":"https://openalex.org/G6092970640","display_name":null,"funder_award_id":"5100-202118566A-0-5-SF","funder_id":"https://openalex.org/F4320335967","funder_display_name":"Science and Technology Project of State Grid"},{"id":"https://openalex.org/G8709186652","display_name":null,"funder_award_id":"5400-202019491A-0-0-00","funder_id":"https://openalex.org/F4320335967","funder_display_name":"Science and Technology Project of State Grid"}],"funders":[{"id":"https://openalex.org/F4320326707","display_name":"State Grid Corporation of China","ror":"https://ror.org/05twwhs70"},{"id":"https://openalex.org/F4320335967","display_name":"Science and Technology Project of State Grid","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387730314.pdf"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1987016416","https://openalex.org/W2011021089","https://openalex.org/W2026131661","https://openalex.org/W2033923462","https://openalex.org/W2149723631","https://openalex.org/W2153635508","https://openalex.org/W2539014457","https://openalex.org/W2754252319","https://openalex.org/W2762685704","https://openalex.org/W2790545079","https://openalex.org/W2847088336","https://openalex.org/W2963643265","https://openalex.org/W3080724736","https://openalex.org/W3098900881","https://openalex.org/W3134220793","https://openalex.org/W3197015000","https://openalex.org/W4224246033"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2510582230","https://openalex.org/W4375867731","https://openalex.org/W2978674666","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Abstract":[0],"As":[1],"a":[2,27],"basic":[3],"task":[4],"in":[5],"energy":[6],"consumption":[7],"monitoring":[8],"system,":[9],"load":[10,29,140],"forecasting":[11,30,141],"has":[12,138],"great":[13],"effects":[14],"on":[15],"system":[16],"operation":[17],"safety,":[18],"generation":[19],"costs":[20],"and":[21,36,55,85,109],"economic":[22],"benefits.":[23],"In":[24],"this":[25],"paper,":[26],"long-term":[28],"algorithm":[31,137],"using":[32],"data":[33],"dimension":[34],"expansion":[35],"deep":[37,96],"feature":[38,102],"extraction":[39],"is":[40,60,76,91],"proposed.":[41],"First,":[42],"the":[43,46,57,64,67,71,80,83,86,95,105,107,135],"outliers":[44],"of":[45,66,82,122],"meteorological":[47],"measurements":[48],"are":[49,113],"removed":[50],"by":[51],"median":[52],"filter":[53],"method,":[54],"then":[56],"time":[58],"information":[59],"encoded":[61],"to":[62,78,93],"form":[63],"fingerprint":[65],"training":[68],"data.":[69],"Next,":[70],"full":[72],"connected":[73],"network":[74,89,124],"(FCN)":[75],"used":[77,92],"expand":[79],"dimensions":[81],"fingerprint,":[84],"convolutional":[87],"neural":[88],"(CNN)":[90],"extract":[94],"features":[97],"which":[98],"can":[99,125],"obtain":[100],"better":[101,139],"representation.":[103],"Finally,":[104],"FCN,":[106],"CNN":[108],"regression":[110],"learning":[111],"model":[112],"combined":[114],"for":[115],"jointly":[116],"offline":[117],"training.":[118],"The":[119],"optimal":[120],"parameters":[121],"these":[123],"be":[126],"obtained":[127],"under":[128],"global":[129],"solution.":[130],"Experimental":[131],"results":[132],"show":[133],"that":[134],"proposed":[136],"performance":[142],"than":[143],"existing":[144],"methods.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}