{"id":"https://openalex.org/W4320057771","doi":"https://doi.org/10.1109/ccis57298.2022.10016322","title":"Short-Term Power Load Forecasting Based on IWOA-Attention-BiLSTM","display_name":"Short-Term Power Load Forecasting Based on IWOA-Attention-BiLSTM","publication_year":2022,"publication_date":"2022-11-26","ids":{"openalex":"https://openalex.org/W4320057771","doi":"https://doi.org/10.1109/ccis57298.2022.10016322"},"language":"en","primary_location":{"id":"doi:10.1109/ccis57298.2022.10016322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis57298.2022.10016322","pdf_url":null,"source":{"id":"https://openalex.org/S4363608348","display_name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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/A5101475189","display_name":"Zhanpeng Liu","orcid":"https://orcid.org/0009-0005-1908-320X"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhanpeng Liu","raw_affiliation_strings":["Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088910250","display_name":"Xiuquan Wang","orcid":"https://orcid.org/0000-0002-3718-3416"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuquan Wang","raw_affiliation_strings":["Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051802065","display_name":"Jiwei Xing","orcid":"https://orcid.org/0009-0007-4073-5481"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwei Xing","raw_affiliation_strings":["Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101897479","display_name":"Mifeng Ren","orcid":"https://orcid.org/0000-0001-5062-0521"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mifeng Ren","raw_affiliation_strings":["Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088143484","display_name":"Xinying Xu","orcid":"https://orcid.org/0000-0001-5968-5989"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinying Xu","raw_affiliation_strings":["Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Electrical and Power Engineering,Taiyuan,China,030024","institution_ids":["https://openalex.org/I9086337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101475189"],"corresponding_institution_ids":["https://openalex.org/I9086337"],"apc_list":null,"apc_paid":null,"fwci":0.3222,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51504831,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"444","last_page":"450"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9921000003814697,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7434425950050354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7084826827049255},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.5633000731468201},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5419666767120361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49819207191467285},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.4848669469356537},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42399734258651733},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.388449490070343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32756662368774414},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10365059971809387},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.09535050392150879}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7434425950050354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084826827049255},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.5633000731468201},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5419666767120361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49819207191467285},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.4848669469356537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42399734258651733},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.388449490070343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32756662368774414},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10365059971809387},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.09535050392150879},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccis57298.2022.10016322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis57298.2022.10016322","pdf_url":null,"source":{"id":"https://openalex.org/S4363608348","display_name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322666","display_name":"Natural Science Foundation of Shanxi Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1980477020","https://openalex.org/W1991151501","https://openalex.org/W1993247634","https://openalex.org/W2290883490","https://openalex.org/W2340247464","https://openalex.org/W2562607067","https://openalex.org/W2783716222","https://openalex.org/W2922329508","https://openalex.org/W2945007383","https://openalex.org/W2948490758","https://openalex.org/W2972307511","https://openalex.org/W2992859584","https://openalex.org/W3093607496","https://openalex.org/W3097967101","https://openalex.org/W3103277488","https://openalex.org/W3106195229","https://openalex.org/W3116475140","https://openalex.org/W3126625402","https://openalex.org/W3128608971","https://openalex.org/W3133841585","https://openalex.org/W3135409625","https://openalex.org/W3154546263","https://openalex.org/W3166279068","https://openalex.org/W3172835321"],"related_works":["https://openalex.org/W4295309597","https://openalex.org/W4210794429","https://openalex.org/W4280535922","https://openalex.org/W4223456145","https://openalex.org/W4309113015","https://openalex.org/W3199608561","https://openalex.org/W4283697347","https://openalex.org/W4304128395","https://openalex.org/W4307195028","https://openalex.org/W4295681619"],"abstract_inverted_index":{"Accurate":[0],"power":[1,11,25,70,159],"load":[2,71],"forecasting":[3,28],"can":[4,151],"significantly":[5],"improve":[6,14,153],"the":[7,15,20,38,56,65,76,79,84,89,97,117,138,142,154],"economic":[8],"benefits":[9],"of":[10,24,58,69,78,91,157],"systems.":[12],"To":[13],"prediction":[16,119,143,155],"accuracy,":[17,120],"aiming":[18],"at":[19],"complexity":[21],"and":[22,61,87,102,113,121,126,133],"volatility":[23],"load,":[26],"a":[27],"model":[29,53],"based":[30],"on":[31],"improved":[32],"whale":[33],"optimization":[34],"algorithm":[35],"(IWOA)":[36],"optimized":[37],"bidirectional":[39,66],"long":[40],"short-term":[41,158],"memory":[42],"(BiLSTM)":[43],"combined":[44],"with":[45,110],"attention":[46,85],"mechanism":[47],"(IWOA-Attention-":[48],"BiLSTM)":[49],"is":[50,146],"proposed.":[51],"The":[52,105],"comprehensively":[54],"considers":[55],"influence":[57],"meteorological":[59],"factors":[60],"date":[62],"types,":[63],"learns":[64],"series":[67],"features":[68],"data":[72],"by":[73,83,93],"BiLSTM,":[74],"calculates":[75],"weights":[77],"hidden":[80],"layer":[81],"state":[82],"mechanism,":[86],"finds":[88],"hyperparameters":[90],"Attention-BiLSTM":[92],"IWOA,":[94],"such":[95],"as":[96],"learning":[98],"rate,":[99],"iteration":[100],"times":[101],"batch":[103],"size.":[104],"results":[106],"show":[107],"that":[108,148],"compared":[109],"BP,":[111],"LSTM":[112],"Seq2Seq,":[114],"IWOA-Attention-BiLSTM":[115],"has":[116],"highest":[118],"its":[122],"MAPE,":[123],"RMSE,":[124],"MAE":[125],"R2":[127],"are":[128,137],"1.44":[129],"%,":[130],"128.83MW,":[131],"97.83MW":[132],"0.9931":[134],"respectively,":[135],"which":[136],"best":[139],"among":[140],"all":[141],"models.":[144],"It":[145],"proved":[147],"IWOA-Attention-":[149],"BiLSTM":[150],"effectively":[152],"accuracy":[156],"load.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}