{"id":"https://openalex.org/W4412130187","doi":"https://doi.org/10.1109/ias62731.2025.11061536","title":"Short-Term Load Forecasting Method for Distribution Networks Based on Sample Entropy Secondary Decomposition and Deep Learning","display_name":"Short-Term Load Forecasting Method for Distribution Networks Based on Sample Entropy Secondary Decomposition and Deep Learning","publication_year":2025,"publication_date":"2025-06-15","ids":{"openalex":"https://openalex.org/W4412130187","doi":"https://doi.org/10.1109/ias62731.2025.11061536"},"language":"en","primary_location":{"id":"doi:10.1109/ias62731.2025.11061536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias62731.2025.11061536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Industry Applications Society Annual Meeting (IAS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100332330","display_name":"Yifeng Wang","orcid":"https://orcid.org/0000-0003-0948-1296"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifeng Wang","raw_affiliation_strings":["North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004231292","display_name":"Xinyuan Hu","orcid":"https://orcid.org/0000-0002-5617-4734"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyuan Hu","raw_affiliation_strings":["North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026722416","display_name":"Yuting Yan","orcid":"https://orcid.org/0000-0001-7081-2037"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuting Yan","raw_affiliation_strings":["North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052882097","display_name":"Ting Wu","orcid":"https://orcid.org/0000-0002-2574-9451"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Wu","raw_affiliation_strings":["Harbin Institute of Technology,School of Robotics and Advanced Manufacture,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,School of Robotics and Advanced Manufacture,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072408826","display_name":"Shiwei Xia","orcid":"https://orcid.org/0000-0003-1272-210X"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiwei Xia","raw_affiliation_strings":["North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Electric Power University,School of Electrical and Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12451","display_name":"Smart Grid and Power Systems","score":0.9341999888420105,"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/T12451","display_name":"Smart Grid and Power Systems","score":0.9341999888420105,"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/term","display_name":"Term (time)","score":0.6773456335067749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6168445348739624},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.57212895154953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5513038635253906},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5167081356048584},{"id":"https://openalex.org/keywords/sample-entropy","display_name":"Sample entropy","score":0.48292896151542664},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46844273805618286},{"id":"https://openalex.org/keywords/technology-forecasting","display_name":"Technology forecasting","score":0.42268896102905273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37828123569488525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.23788252472877502},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06280669569969177}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6773456335067749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6168445348739624},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.57212895154953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5513038635253906},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5167081356048584},{"id":"https://openalex.org/C66696666","wikidata":"https://www.wikidata.org/wiki/Q17105612","display_name":"Sample entropy","level":3,"score":0.48292896151542664},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46844273805618286},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.42268896102905273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37828123569488525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.23788252472877502},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06280669569969177},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ias62731.2025.11061536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias62731.2025.11061536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Industry Applications Society Annual Meeting (IAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W3004437308","https://openalex.org/W3033406500","https://openalex.org/W3087093340","https://openalex.org/W3090516321","https://openalex.org/W3095582408","https://openalex.org/W3162100006","https://openalex.org/W3198084268","https://openalex.org/W4212987714","https://openalex.org/W4225243427","https://openalex.org/W4285297243","https://openalex.org/W4312371287","https://openalex.org/W4321483329","https://openalex.org/W4362654051","https://openalex.org/W4367333170","https://openalex.org/W4386322034","https://openalex.org/W4386534963","https://openalex.org/W4406860892","https://openalex.org/W4407510962","https://openalex.org/W4407694514","https://openalex.org/W4409199204"],"related_works":["https://openalex.org/W2050076996","https://openalex.org/W2006708776","https://openalex.org/W3088843374","https://openalex.org/W2076258781","https://openalex.org/W229760109","https://openalex.org/W2093589470","https://openalex.org/W2889925888","https://openalex.org/W2415751681","https://openalex.org/W2106977028","https://openalex.org/W2022287597"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,15,54,72,93,102,148],"address":[3],"the":[4,9,47,75,95,98,103,107,139,143,150],"difficulty":[5],"in":[6],"accurately":[7],"predicting":[8],"load":[10,17,30,61,76,110,136],"of":[11,22,97,142],"distribution":[12,34],"networks":[13,35],"due":[14],"strong":[16],"fluctuations":[18],"and":[19,43,82,109],"high":[20],"randomness":[21],"user":[23],"behavior,":[24],"this":[25],"paper":[26],"proposes":[27],"a":[28,116],"short-term":[29,119],"forecasting":[31],"method":[32],"for":[33,135],"based":[36],"on":[37],"sample":[38],"entropy":[39],"(SE)":[40],"secondary":[41],"decomposition":[42,68],"deep":[44],"learning.":[45],"First,":[46],"random":[48],"forest":[49],"(RF)":[50],"algorithm":[51],"is":[52,70],"used":[53],"extract":[55],"features":[56],"from":[57],"external":[58],"factors":[59,112],"affecting":[60],"data.":[62],"Then,":[63],"time-varying":[64],"filtering":[65],"empirical":[66],"mode":[67],"(TVFEMD)":[69],"applied":[71],"preliminarily":[73],"decompose":[74],"sequence":[77],"into":[78,115],"subsequences.":[79],"Combining":[80],"SE":[81],"singular":[83],"spectrum":[84],"analysis":[85],"(SSA),":[86],"highly":[87],"complex":[88],"subsequences":[89,108,145],"are":[90,113,125,146],"further":[91],"decomposed":[92],"improve":[94],"quality":[96],"sequences":[99],"as":[100],"inputs":[101],"prediction":[104,152],"model.":[105],"Next,":[106],"influencing":[111],"input":[114],"bidirectional":[117],"long":[118],"memory":[120],"(BiLSTM)":[121],"model,":[122],"whose":[123],"hyperparameters":[124],"optimized":[126],"by":[127],"an":[128],"improved":[129],"dung":[130],"beetle":[131],"optimization":[132],"(IDBO)":[133],"algorithm,":[134],"forecasting.":[137],"Finally,":[138],"predicted":[140],"results":[141],"output":[144],"superimposed":[147],"obtain":[149],"final":[151],"result.":[153]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}