{"id":"https://openalex.org/W7138885916","doi":"https://doi.org/10.1109/icacs69208.2026.11433156","title":"Hybrid Deep Learning Models for Enhanced Short-Term Load Forecasting at the Distribution Transformer Level","display_name":"Hybrid Deep Learning Models for Enhanced Short-Term Load Forecasting at the Distribution Transformer Level","publication_year":2026,"publication_date":"2026-02-10","ids":{"openalex":"https://openalex.org/W7138885916","doi":"https://doi.org/10.1109/icacs69208.2026.11433156"},"language":null,"primary_location":{"id":"doi:10.1109/icacs69208.2026.11433156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacs69208.2026.11433156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 7th International Conference on Advancements in Computational Sciences (ICACS)","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/A5129974280","display_name":"Muhammad Bilal","orcid":null},"institutions":[{"id":"https://openalex.org/I173207729","display_name":"University of Engineering and Technology Taxila","ror":"https://ror.org/03v00ka07","country_code":"PK","type":"education","lineage":["https://openalex.org/I173207729"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Bilal","raw_affiliation_strings":["University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan","institution_ids":["https://openalex.org/I173207729"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129857773","display_name":"Razia","orcid":null},"institutions":[{"id":"https://openalex.org/I173207729","display_name":"University of Engineering and Technology Taxila","ror":"https://ror.org/03v00ka07","country_code":"PK","type":"education","lineage":["https://openalex.org/I173207729"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Razia","raw_affiliation_strings":["University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan","institution_ids":["https://openalex.org/I173207729"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130210150","display_name":"Samra Riasat","orcid":null},"institutions":[{"id":"https://openalex.org/I173207729","display_name":"University of Engineering and Technology Taxila","ror":"https://ror.org/03v00ka07","country_code":"PK","type":"education","lineage":["https://openalex.org/I173207729"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Samra Riasat","raw_affiliation_strings":["University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan","institution_ids":["https://openalex.org/I173207729"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121763731","display_name":"Furqan Shaukat","orcid":null},"institutions":[{"id":"https://openalex.org/I173207729","display_name":"University of Engineering and Technology Taxila","ror":"https://ror.org/03v00ka07","country_code":"PK","type":"education","lineage":["https://openalex.org/I173207729"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Furqan Shaukat","raw_affiliation_strings":["University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan","institution_ids":["https://openalex.org/I173207729"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078940157","display_name":"Intisar Ali Sajjad","orcid":"https://orcid.org/0000-0002-8947-9729"},"institutions":[{"id":"https://openalex.org/I173207729","display_name":"University of Engineering and Technology Taxila","ror":"https://ror.org/03v00ka07","country_code":"PK","type":"education","lineage":["https://openalex.org/I173207729"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Intisar Ali Sajjad","raw_affiliation_strings":["University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering &#x0026; Technology,Department of Electrical Engineering,Taxila,Pakistan","institution_ids":["https://openalex.org/I173207729"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I173207729"],"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.6345000267028809,"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":0.6345000267028809,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.09109999984502792,"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":0.04650000110268593,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6421999931335449},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6122999787330627},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.512499988079071},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.3905999958515167},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.3617999851703644},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3407999873161316},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.31130000948905945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6909999847412109},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6421999931335449},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6122999787330627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5784000158309937},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42739999294281006},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacs69208.2026.11433156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacs69208.2026.11433156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 7th International Conference on Advancements in Computational Sciences (ICACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9113883376121521}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"distribution":[1,26,226],"transformer":[2,27],"(DT)":[3],"is":[4,97,139],"an":[5,20],"intrinsic":[6],"element":[7],"in":[8,110,190],"the":[9,17,25,30,91,102,117,136,191,195,198,235],"overall":[10],"power":[11],"system.":[12],"With":[13,29],"increasing":[14],"energy":[15],"demands,":[16],"system":[18],"requires":[19],"effective":[21],"forecasting":[22,43],"method":[23],"at":[24,234],"level.":[28],"recent":[31],"emergence":[32],"of":[33,156,164,197,207,225],"deep":[34,84],"learning":[35,85],"models":[36,55,86,158],"and":[37,51,113,116,133,179,193,209,213,241],"their":[38],"adaptation":[39],"to":[40,146],"this":[41,67,69],"task,":[42],"has":[44,71],"generally":[45],"improved;":[46],"however,":[47],"when":[48],"incorporating":[49,75,125],"weather":[50],"load":[52,239],"features,":[53,135,148],"these":[54],"often":[56],"overlook":[57],"granular":[58],"electrical":[59,77],"parameters":[60,78],"that":[61],"could":[62],"enhance":[63],"prediction":[64],"reliability.":[65],"In":[66],"paper,":[68],"gap":[70],"been":[72,88,184],"addressed":[73],"by":[74,124,167],"overlooked":[76],"alongside":[79],"conventional":[80],"features.":[81],"Afterwords,":[82],"state-of-the-art":[83],"have":[87,183],"deployed,":[89],"including":[90,174],"Recurrent":[92],"Neural":[93],"Network":[94],"(RNN),":[95],"which":[96,106,120,141,203],"beneficial":[98],"for":[99,220],"sequence":[100],"modelling;":[101],"Bidirectional":[103],"LSTM":[104],"(Bi-LSTM),":[105],"can":[107],"capture":[108],"dependencies":[109],"both":[111,131,211],"forward":[112],"backward":[114],"directions;":[115],"hybrid":[118],"RNN-Bi-LSTM,":[119,202],"increases":[121],"computational":[122],"efficiency":[123],"bi-directional":[126],"learning.":[127],"To":[128],"further":[129],"extract":[130,147],"spatial":[132],"temporal":[134],"CNN-Bi-LSTM":[137],"model":[138],"utilized,":[140],"leverages":[142],"its":[143],"deeper":[144],"architecture":[145],"thereby":[149,216],"improving":[150],"pattern":[151],"recognition.":[152],"An":[153],"extensive":[154],"evaluation":[155],"proposed":[157,199,229],"was":[159],"conducted":[160],"on":[161],"a":[162,205],"dataset":[163],"48,000":[165],"instances":[166],"MIRAD":[168],"LESCO":[169],"using":[170],"standard":[171],"performance":[172],"metrics,":[173],"MSE,":[175],"RMSE,":[176],"MAE,":[177],"MAPE,":[178],"R2.":[180],"The":[181,228],"results":[182],"compared":[185],"with":[186],"other":[187],"notable":[188],"studies":[189],"domain":[192],"indicate":[194],"superiority":[196],"method,":[200],"specifically":[201],"achieves":[204],"MAPE":[206],"9.476%":[208],"captures":[210],"short-":[212],"long-term":[214],"dependencies,":[215],"enhancing":[217,238],"predictive":[218],"accuracy":[219,233],"ShortTerm":[221],"Load":[222],"Forecasting":[223],"(STLF)":[224],"transformers.":[227],"work":[230],"enhances":[231],"forecast":[232],"DT":[236],"level,":[237],"management":[240],"operational":[242],"efficiency.":[243]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-03-20T00:00:00"}
