{"id":"https://openalex.org/W4401164322","doi":"https://doi.org/10.1109/eit60633.2024.10609930","title":"Artificial Intelligence Techniques for Load Forecasting in an Electric Utility","display_name":"Artificial Intelligence Techniques for Load Forecasting in an Electric Utility","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4401164322","doi":"https://doi.org/10.1109/eit60633.2024.10609930"},"language":"en","primary_location":{"id":"doi:10.1109/eit60633.2024.10609930","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/eit60633.2024.10609930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Electro Information Technology (eIT)","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/A5090864323","display_name":"Sri R. Kolla","orcid":null},"institutions":[{"id":"https://openalex.org/I157417397","display_name":"Bowling Green State University","ror":"https://ror.org/00ay7va13","country_code":"US","type":"education","lineage":["https://openalex.org/I157417397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sri R. Kolla","raw_affiliation_strings":["Bowling Green State University,Electronics and Computer Engineering Technology,Bowling Green,OH,USA,43403"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bowling Green State University,Electronics and Computer Engineering Technology,Bowling Green,OH,USA,43403","institution_ids":["https://openalex.org/I157417397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086080320","display_name":"Xiaohan Ni","orcid":null},"institutions":[{"id":"https://openalex.org/I157417397","display_name":"Bowling Green State University","ror":"https://ror.org/00ay7va13","country_code":"US","type":"education","lineage":["https://openalex.org/I157417397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohan Ni","raw_affiliation_strings":["Bowling Green State University,Electronics and Computer Engineering Technology,Bowling Green,OH,USA,43403"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bowling Green State University,Electronics and Computer Engineering Technology,Bowling Green,OH,USA,43403","institution_ids":["https://openalex.org/I157417397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1856,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48562168,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2007","issue":null,"first_page":"580","last_page":"585"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9803000092506409,"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":0.9803000092506409,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9731000065803528,"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/computer-science","display_name":"Computer science","score":0.6375393867492676},{"id":"https://openalex.org/keywords/electric-utility","display_name":"Electric utility","score":0.5326316952705383},{"id":"https://openalex.org/keywords/technology-forecasting","display_name":"Technology forecasting","score":0.4194744825363159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37762629985809326},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19196176528930664},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.13918349146842957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6375393867492676},{"id":"https://openalex.org/C2777819126","wikidata":"https://www.wikidata.org/wiki/Q1326624","display_name":"Electric utility","level":2,"score":0.5326316952705383},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.4194744825363159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37762629985809326},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19196176528930664},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.13918349146842957}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eit60633.2024.10609930","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/eit60633.2024.10609930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Electro Information Technology (eIT)","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":19,"referenced_works":["https://openalex.org/W1991151501","https://openalex.org/W2001506129","https://openalex.org/W2017387812","https://openalex.org/W2039346833","https://openalex.org/W2061197278","https://openalex.org/W2081060955","https://openalex.org/W2099271915","https://openalex.org/W2139073438","https://openalex.org/W2147098681","https://openalex.org/W2613332262","https://openalex.org/W2788553534","https://openalex.org/W3084535629","https://openalex.org/W3113667525","https://openalex.org/W3120421331","https://openalex.org/W3162100006","https://openalex.org/W4254597214","https://openalex.org/W4285122835","https://openalex.org/W4319159759","https://openalex.org/W4367547502"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W144187051","https://openalex.org/W2036352780"],"abstract_inverted_index":{"It":[0],"is":[1,76,87,96],"important":[2],"to":[3,7],"implement":[4],"load":[5,36,48,80,100,110,136,146],"forecasting":[6,49,81,111,134],"provide":[8],"a":[9,120],"more":[10],"effective":[11,47],"electrical":[12],"power":[13],"allocation":[14],"in":[15,82],"the":[16,26,41,138,144,154],"electric":[17,52,114,139],"grid.":[18],"There":[19],"are":[20,59],"many":[21],"factors":[22],"that":[23],"can":[24],"affect":[25],"actual":[27],"load,":[28],"such":[29],"as":[30],"climatic":[31],"conditions,":[32],"special":[33],"events,":[34],"and":[35,64,117,129],"timing.":[37],"This":[38,102],"paper":[39,103],"applies":[40],"Artificial":[42,60],"Intelligence":[43],"(AI)":[44],"techniques":[45,57,107],"for":[46,50,78,99,108,137,153],"an":[51,113],"utility.":[53],"The":[54,85,92],"main":[55],"AI":[56,106],"considered":[58,98],"Neural":[61],"Networks":[62],"(ANN)":[63],"Support":[65],"Vector":[66],"Machines":[67],"(SVM).":[68],"A":[69],"feed":[70],"forward":[71],"layered":[72],"neural":[73],"network":[74],"model":[75],"used":[77],"short-term":[79,109],"this":[83],"paper.":[84],"ANN":[86,128,148],"trained":[88],"using":[89],"Levenberg-Marquardt":[90],"algorithm.":[91],"regression":[93],"SVM":[94,130],"technique":[95],"also":[97],"forecasting.":[101],"applied":[104],"these":[105,124],"of":[112,123,135],"utility":[115,140],"data":[116,141,155],"carried":[118],"out":[119],"comparative":[121],"study":[122],"methods.":[125],"While":[126],"both":[127],"methods":[131],"gave":[132,150],"favorable":[133],"based":[142],"on":[143],"prior":[145],"information,":[147],"method":[149],"better":[151],"prediction":[152],"considered.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}