{"id":"https://openalex.org/W4411670514","doi":"https://doi.org/10.1145/3728985.3728987","title":"Optimization of LSTM Neural Networks Based on CNN for Power Load Forecasting Research","display_name":"Optimization of LSTM Neural Networks Based on CNN for Power Load Forecasting Research","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4411670514","doi":"https://doi.org/10.1145/3728985.3728987"},"language":"en","primary_location":{"id":"doi:10.1145/3728985.3728987","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3728985.3728987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 10th International Conference on Robotics and Artificial Intelligence","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/A5041780665","display_name":"Yiqun Deng","orcid":"https://orcid.org/0000-0002-6838-3779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiqun Gui","raw_affiliation_strings":["compute department, Amazingx academy, Foshan, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6838-3779","affiliations":[{"raw_affiliation_string":"compute department, Amazingx academy, Foshan, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006970268","display_name":"Zhiqian Li","orcid":"https://orcid.org/0000-0001-9936-6819"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiqian Li","raw_affiliation_strings":["compute department, Amazingx academy, Foshan, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9936-6819","affiliations":[{"raw_affiliation_string":"compute department, Amazingx academy, Foshan, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045191293","display_name":"Junyang Chen","orcid":"https://orcid.org/0000-0002-1139-8654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junyang Xiao","raw_affiliation_strings":["compute department, Amazingx academy, Foshan, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1139-8654","affiliations":[{"raw_affiliation_string":"compute department, Amazingx academy, Foshan, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100528054","display_name":"Yihong Xu","orcid":"https://orcid.org/0000-0001-6177-8316"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yihong Xu","raw_affiliation_strings":["compute department, Amazingx academy, Foshan, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6177-8316","affiliations":[{"raw_affiliation_string":"compute department, Amazingx academy, Foshan, Guangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031961745","display_name":"Kai Yang","orcid":"https://orcid.org/0000-0003-3044-2240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["Sanya Science and Education Innovation Park,, WUHAN UNIERSITY OF TECHNOLOG, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0000-0003-3044-2240","affiliations":[{"raw_affiliation_string":"Sanya Science and Education Innovation Park,, WUHAN UNIERSITY OF TECHNOLOG, Wuhan, Hubei, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":"72","last_page":"79"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9994000196456909,"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":0.9994000196456909,"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/T12451","display_name":"Smart Grid and Power Systems","score":0.9868000149726868,"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/T14392","display_name":"Geoscience and Mining Technology","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.7278797030448914},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6393867135047913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6022146344184875},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5186473727226257},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38669514656066895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7278797030448914},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6393867135047913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6022146344184875},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5186473727226257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38669514656066895},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3728985.3728987","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3728985.3728987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 10th International Conference on Robotics and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.75,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W3047313329","https://openalex.org/W3090661556","https://openalex.org/W3109256480","https://openalex.org/W4248841288","https://openalex.org/W4320015912"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"It's":[0],"crucial":[1],"for":[2,109,147,220,239],"estimating":[3],"future":[4],"electricity":[5],"demand,":[6],"aiding":[7],"in":[8,40,81,114,161,187],"power":[9,17,36,42,62,100,123,133,170,221],"system":[10,37,222],"operations":[11],"and":[12,23,44,55,65,102,130,180,189,200,226,236],"planning.":[13],"Accurate":[14],"predictions":[15],"facilitate":[16],"generation":[18],"planning,":[19,225],"energy":[20,227],"resource":[21],"allocation,":[22],"cost":[24],"reduction":[25],"during":[26],"peak":[27],"demand.This":[28],"paper":[29,210],"first":[30],"explains":[31],"the":[32,41,46,58,77,85,93,96,110,118,148,152,155,168,175,178,183,190,194,212,217,241,245],"key":[33],"role":[34],"of":[35,49,61,99,121,151,182,216,244],"load":[38,50,63,101,134,171,206],"forecasting":[39,51,64],"grid":[43,224],"introduces":[45],"status":[47],"quo":[48],"research":[52],"at":[53],"home":[54],"abroad.":[56],"Next,":[57],"basic":[59],"concepts":[60],"deep-learning-related":[66],"techniques":[67],"are":[68,140,185],"analyzed.":[69,137],"The":[70,138],"deep":[71],"learning":[72],"technique":[73],"focuses":[74],"on":[75],"analyzing":[76],"typical":[78],"model,":[79,157],"which":[80,158,230],"turn":[82],"reveals":[83],"that":[84],"classical":[86],"model":[87,112],"is":[88,136,159,163],"unable":[89],"to":[90,145,165,203],"better":[91,205],"mine":[92,193],"connection":[94],"between":[95],"historical":[97],"data":[98,234],"weather":[103],"data,":[104,135],"providing":[105],"a":[106,232],"theoretical":[107],"basis":[108,238],"subsequent":[111,149],"proposed":[113],"this":[115,209],"paper.":[116],"Secondly,":[117],"pre-processing":[119],"method":[120],"short-term":[122,169],"load's":[124],"influencing":[125],"factors,":[126,196],"such":[127,197],"as":[128,198],"temperature":[129],"date":[131,201],"with":[132,167],"loads":[139],"normalized":[141],"under":[142],"different":[143],"scenarios":[144],"prepare":[146],"training":[150],"model.":[153],"Then":[154],"CNN-LSTM":[156],"effective":[160],"prediction,":[162],"introduced":[164],"deal":[166],"prediction":[172,218],"problem.":[173],"At":[174],"same":[176],"time,":[177],"structure":[179],"principle":[181],"mechanism":[184],"analyzed":[186],"depth,":[188],"algorithm":[191],"can":[192],"main":[195],"meteorology":[199],"effectively,":[202],"uncover":[204],"patterns.":[207],"Further,":[208],"explores":[211],"practical":[213],"application":[214],"value":[215],"results":[219],"scheduling,":[223],"management":[228],"decision-making,":[229],"provides":[231],"strong":[233],"support":[235],"decision-making":[237],"realizing":[240],"efficient":[242],"operation":[243],"smart":[246],"grid.":[247]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}