{"id":"https://openalex.org/W7130831135","doi":"https://doi.org/10.31449/inf.v50i7.7552","title":"Enhancing Short-Term Load Forecasting in the Electric Power Industry: A Hybrid Approach Integrating Machine Learning and Advanced Optimization Techniques","display_name":"Enhancing Short-Term Load Forecasting in the Electric Power Industry: A Hybrid Approach Integrating Machine Learning and Advanced Optimization Techniques","publication_year":2026,"publication_date":"2026-02-21","ids":{"openalex":"https://openalex.org/W7130831135","doi":"https://doi.org/10.31449/inf.v50i7.7552"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i7.7552","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i7.7552","pdf_url":null,"source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.31449/inf.v50i7.7552","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaoyan Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166499","display_name":"Henan Polytechnic University","ror":"https://ror.org/05vr1c885","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyan Tang","raw_affiliation_strings":["Henan Polytechnic Institute, Nanyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Polytechnic Institute, Nanyang, China","institution_ids":["https://openalex.org/I4210166499"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068320658","display_name":"Pengfei Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166499","display_name":"Henan Polytechnic University","ror":"https://ror.org/05vr1c885","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Yue","raw_affiliation_strings":["Henan Polytechnic Institute, Nanyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Polytechnic Institute, Nanyang, China","institution_ids":["https://openalex.org/I4210166499"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210166499"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1589183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"7","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":0.963100016117096,"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":0.963100016117096,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.003599999938160181,"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/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.739300012588501},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6333000063896179},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5478000044822693},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.41519999504089355},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.4059999883174896},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3953999876976013},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.39100000262260437},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.3885999917984009},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.373199999332428},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.367900013923645}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.739300012588501},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6333000063896179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6309000253677368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5740000009536743},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5231999754905701},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.4059999883174896},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.39100000262260437},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.3885999917984009},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.367900013923645},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C40293303","wikidata":"https://www.wikidata.org/wiki/Q27137","display_name":"Electric power","level":3,"score":0.35100001096725464},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.32120001316070557},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3181999921798706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30640000104904175},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.2840999960899353},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C122357587","wikidata":"https://www.wikidata.org/wiki/Q6934508","display_name":"Multi-swarm optimization","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2727999985218048},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C62469222","wikidata":"https://www.wikidata.org/wiki/Q17092103","display_name":"Hybrid algorithm (constraint satisfaction)","level":5,"score":0.258899986743927},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i7.7552","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i7.7552","pdf_url":null,"source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.31449/inf.v50i7.7552","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i7.7552","pdf_url":null,"source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5970238447189331,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,169],"work":[1],"introduces":[2],"a":[3,54,118,126],"hybrid":[4,100,135,147],"methodology":[5],"that":[6,56,98,133],"combines":[7],"machine":[8,176],"learning":[9,177],"with":[10,39,178],"sophisticated":[11],"optimization":[12,148,179],"approaches":[13],"to":[14,88,181,196],"improve":[15],"short-term":[16],"load":[17,152],"forecasting":[18,153,183],"in":[19],"the":[20,75,84,92,99,110,134,172],"electric":[21],"power":[22,190],"sector.":[23],"The":[24,49,95,146],"model":[25,93,106,136],"uses":[26],"Support":[27,112],"Vector":[28,113],"Regression":[29,114],"and":[30,35,45,62,68,83,125,158,185,193,200],"Radial":[31,101],"Basis":[32,102],"Function":[33],"together":[34],"then":[36],"improves":[37],"it":[38],"Aquila":[40],"Optimizer,":[41],"Particle":[42],"Swarm":[43],"Optimization,":[44],"Harris":[46],"Hawks":[47,104],"Optimization.":[48],"method":[50],"was":[51],"evaluated":[52],"on":[53],"dataset":[55],"includes":[57],"daily":[58],"electricity":[59],"usage":[60],"data":[61],"important":[63,72],"weather":[64],"variables":[65],"like":[66],"temperature":[67],"humidity.":[69],"We":[70],"used":[71],"measures,":[73],"including":[74],"Root":[76,121],"Mean":[77,80,122],"Squared":[78,123],"Error,":[79,82],"Absolute":[81],"Coefficient":[85],"of":[86,174],"Determination,":[87],"see":[89],"how":[90],"well":[91],"worked.":[94],"results":[96],"show":[97,132],"Function-Harris":[103],"Optimization":[105],"works":[107],"better":[108],"than":[109],"standalone":[111],"model.":[115],"It":[116],"has":[117],"20%":[119],"lower":[120],"Error":[124],"15%":[127],"higher":[128],"accuracy.":[129],"These":[130],"gains":[131],"makes":[137],"more":[138,156],"accurate":[139,157],"predictions,":[140],"especially":[141],"for":[142,165,189],"real-time":[143],"operational":[144],"planning.":[145,201],"techniques":[149],"help":[150],"make":[151],"models":[154],"much":[155],"efficient.":[159],"They":[160],"could":[161],"be":[162],"very":[163],"useful":[164],"managing":[166],"energy":[167],"systems.":[168],"study":[170],"underscores":[171],"significance":[173],"amalgamating":[175],"algorithms":[180],"augment":[182],"abilities":[184],"furnish":[186],"critical":[187],"insights":[188],"system":[191],"operators":[192],"academics":[194],"seeking":[195],"increase":[197],"grid":[198],"stability":[199]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-02-22T00:00:00"}