{"id":"https://openalex.org/W7165613292","doi":"https://doi.org/10.48550/arxiv.2606.23425","title":"Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting","display_name":"Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165613292","doi":"https://doi.org/10.48550/arxiv.2606.23425"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23425","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23425","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139215021","display_name":"Jinhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139149763","display_name":"Hao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":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":0.9078999757766724,"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":0.9078999757766724,"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":0.027300000190734863,"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":0.01140000019222498,"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/interpretability","display_name":"Interpretability","score":0.9573000073432922},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7631000280380249},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49630001187324524},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.4941999912261963},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.48030000925064087},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4000999927520752},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.36559998989105225}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9573000073432922},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7631000280380249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7049000263214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6198999881744385},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5613999962806702},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.36559998989105225},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23425","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23425","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"electricity":[1,151,186],"load":[2,97,128],"forecasting":[3,53,98,222],"is":[4],"a":[5,38,90,102,109,172],"crucial":[6],"prerequisite":[7],"for":[8,96,140],"stable":[9],"power":[10],"system":[11],"operations.":[12],"While":[13,30],"prevalent":[14],"deep":[15,164],"learning":[16,165],"models":[17],"present":[18],"competitive":[19,157],"performance,":[20],"they":[21],"often":[22,216],"operate":[23],"as":[24,37,69,71,126],"black":[25],"boxes":[26],"and":[27,93,129,185,201],"lack":[28],"interpretability.":[29],"the":[31,137,176,208],"Kolmogorov-Arnold":[32],"network":[33],"(KAN)":[34],"has":[35],"emerged":[36],"promising":[39],"alternative":[40],"because":[41],"of":[42,73,175,210],"its":[43,48],"learnable":[44],"activation":[45,190],"function":[46],"design,":[47],"direct":[49],"application":[50],"to":[51,116,136,160,213],"time-series":[52],"faces":[54],"challenges":[55],"in":[56],"modeling":[57],"complex":[58,200],"temporal":[59,105,118],"data":[60],"patterns.":[61],"Also,":[62],"simple":[63],"integration":[64],"into":[65],"existing":[66],"architectures,":[67],"such":[68,125],"serving":[70],"replacement":[72],"neural":[74,221],"modules,":[75],"cannot":[76],"fully":[77],"leverage":[78],"KAN's":[79],"interpretability":[80,170],"strengths.":[81],"To":[82],"address":[83],"these":[84],"gaps,":[85],"this":[86],"study":[87],"develops":[88],"LoadKAN,":[89],"novel":[91],"hybrid":[92],"interpretable":[94,141],"framework":[95],"that":[99],"synergistically":[100],"combines":[101],"specifically-designed":[103],"feature-isolated":[104],"attention":[106,113],"mechanism":[107],"with":[108],"KAN":[110,138],"module.":[111],"The":[112],"stage":[114],"aims":[115],"extract":[117],"dynamics":[119],"from":[120,147],"each":[121],"input":[122],"feature":[123,134],"independently,":[124],"historical":[127],"human":[130],"mobility,":[131],"providing":[132],"distilled":[133],"representations":[135],"module":[139],"predictions.":[142],"When":[143],"evaluated":[144],"on":[145,196],"datasets":[146],"three":[148],"representative":[149],"U.S.":[150],"markets,":[152],"our":[153,192,211],"LoadKAN":[154,212],"remains":[155],"highly":[156],"when":[158],"compared":[159],"extensively-tuned,":[161],"state-of-the-art,":[162],"black-box":[163,220],"benchmarks.":[166],"More":[167],"importantly,":[168],"LoadKAN's":[169],"enables":[171],"granular":[173],"analysis":[174],"learned":[177],"non-linear":[178],"relationships":[179],"between":[180],"six":[181],"distinct":[182],"mobility":[183,197],"patterns":[184],"load.":[187],"Through":[188],"KAN-learned":[189],"functions,":[191],"quantitative":[193],"sensitivity":[194],"analyses":[195],"features":[198],"reveal":[199],"market-specific":[202],"dependencies.":[203],"These":[204],"findings":[205],"further":[206],"demonstrate":[207],"ability":[209],"generate":[214],"insights":[215],"obscured":[217],"by":[218],"opaque":[219],"models.":[223]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
