{"id":"https://openalex.org/W7166747057","doi":"https://doi.org/10.7148/2026-0568","title":"Cluster-based and expert specialization in short-term load forecasting: a capacity-matched empirical study","display_name":"Cluster-based and expert specialization in short-term load forecasting: a capacity-matched empirical study","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7166747057","doi":"https://doi.org/10.7148/2026-0568"},"language":null,"primary_location":{"id":"doi:10.7148/2026-0568","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0568","pdf_url":"https://doi.org/10.7148/2026-0568","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7148/2026-0568","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139687318","display_name":"Saleh Alaliyat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saleh Alaliyat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083319793","display_name":"Rachid Oucheikh","orcid":"https://orcid.org/0000-0001-9996-9759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rachid Oucheikh","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":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"568","last_page":"575"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9729999899864197,"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":0.9729999899864197,"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/T10603","display_name":"Smart Grid Energy Management","score":0.005200000014156103,"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":0.004800000227987766,"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/normalization","display_name":"Normalization (sociology)","score":0.5074999928474426},{"id":"https://openalex.org/keywords/norwegian","display_name":"Norwegian","score":0.503000020980835},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.49970000982284546},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.48170000314712524},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.38670000433921814},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.35519999265670776},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.3337000012397766},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.33320000767707825}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5471000075340271},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5074999928474426},{"id":"https://openalex.org/C63428231","wikidata":"https://www.wikidata.org/wiki/Q9043","display_name":"Norwegian","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4810999929904938},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2782999873161316},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7148/2026-0568","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0568","pdf_url":"https://doi.org/10.7148/2026-0568","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.7148/2026-0568","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0568","pdf_url":"https://doi.org/10.7148/2026-0568","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166747057.pdf","grobid_xml":"https://content.openalex.org/works/W7166747057.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Heterogeneity":[0],"across":[1,57],"electricity":[2],"consumers":[3],"poses":[4],"a":[5,36,103,112],"central":[6],"challenge":[7],"for":[8,137],"short-term":[9],"load":[10],"forecasting":[11,32],"(STLF),":[12],"particularly":[13],"when":[14,40],"models":[15],"must":[16],"operate":[17],"under":[18],"realistic":[19],"data":[20,54],"and":[21,44,60,82,152],"capacity":[22,43],"constraints.":[23],"This":[24],"study":[25],"investigates":[26],"whether":[27],"behavioral":[28],"segmentation":[29],"improves":[30],"multi-meter":[31],"accuracy":[33],"compared":[34],"with":[35],"single":[37],"global":[38,69,114],"model":[39,42,160],"total":[41],"feature":[45],"space":[46],"are":[47,134,155],"strictly":[48],"controlled.":[49],"Using":[50],"hourly":[51],"Norwegian":[52],"smart-meter":[53],"(342":[55],"meters":[56],"residential,":[58],"industrial,":[59],"cabin":[61],"categories),":[62],"we":[63],"evaluate":[64],"six":[65],"specialization":[66,154],"strategies,":[67],"including":[68],"learning,":[70],"cluster-specific":[71,88],"expert":[72],"models,":[73],"conditional":[74],"shared":[75],"representations,":[76],"mixture-of-experts":[77],"(MoE),":[78],"hierarchical":[79],"residual":[80],"correction,":[81],"forecasting-optimized":[83],"clustering.":[84],"Results":[85],"show":[86],"that":[87,125,148],"Random":[89],"Forest":[90],"experts":[91],"achieve":[92],"the":[93,120,129],"best":[94],"overall":[95],"performance":[96],"(MAE":[97],"0.8142":[98],"kWh),":[99],"followed":[100],"closely":[101],"by":[102],"learnable":[104],"MoE":[105],"model,":[106],"yielding":[107],"modest":[108],"improvements":[109],"(1\u20132\\%)":[110],"over":[111],"strong":[113],"baseline.":[115],"However,":[116],"per-meter":[117],"normalization":[118],"yields":[119],"largest":[121],"gain":[122],"(+4.5\\%),":[123],"indicating":[124],"scale":[126],"heterogeneity":[127,150],"is":[128,143],"dominant":[130],"modeling":[131],"challenge.":[132],"Improvements":[133],"most":[135],"pronounced":[136],"industrial":[138],"consumers,":[139],"where":[140],"structural":[141],"diversity":[142],"highest.":[144],"The":[145],"findings":[146],"suggest":[147],"careful":[149],"management":[151],"moderate":[153],"more":[156],"impactful":[157],"than":[158],"increased":[159],"complexity":[161],"in":[162],"practical":[163],"STLF":[164],"deployment.":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-07-01T00:00:00"}
