{"id":"https://openalex.org/W7154608898","doi":"https://doi.org/10.48550/arxiv.2604.13714","title":"An End-to-end Building Load Forecasting Framework with Patch-based Information Fusion Network and Error-weighted Adaptive Loss","display_name":"An End-to-end Building Load Forecasting Framework with Patch-based Information Fusion Network and Error-weighted Adaptive Loss","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154608898","doi":"https://doi.org/10.48550/arxiv.2604.13714"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13714","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13714","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":null,"license_id":null,"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.2604.13714","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133795720","display_name":"Hang Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133739898","display_name":"Ying Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133776854","display_name":"Weican Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Weican","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133801690","display_name":"Dunnan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Dunnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101602103","display_name":"Xiaotao Chen","orcid":"https://orcid.org/0000-0002-3553-4016"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiaotao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133802364","display_name":"Shengwei Mei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mei, Shengwei","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":0.900600016117096,"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":0.900600016117096,"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":0.05689999833703041,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.004800000227987766,"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/outlier","display_name":"Outlier","score":0.5534999966621399},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5529999732971191},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4837999939918518},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.44909998774528503},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4097000062465668},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3756999969482422},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.36890000104904175},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.34299999475479126},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.32679998874664307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6703000068664551},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5748000144958496},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5534999966621399},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5529999732971191},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3765999972820282},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C30905978","wikidata":"https://www.wikidata.org/wiki/Q815598","display_name":"Metering mode","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13714","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13714","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.13714","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13714","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8634945750236511,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"building":[1,26,42,84,110],"load":[2,43,85,111,214],"forecasting":[3,44,112],"plays":[4],"a":[5,39,138,155,179,188],"critical":[6,102],"role":[7],"in":[8,81],"facilitating":[9],"demand":[10],"response":[11],"aggregation":[12],"and":[13,22,78,192,222],"optimizing":[14],"energy":[15],"management.":[16],"However,":[17],"the":[18,29,47,57,69,82,95,109,116,160,166,174,209,220],"complex":[19],"temporal":[20,135,170],"dependencies":[21],"high":[23],"volatility":[24],"of":[25,31,97,168,224],"loads":[27],"limit":[28],"improvement":[30],"prediction":[32,203],"accuracy.":[33],"To":[34],"this":[35],"end,":[36],"we":[37,67,88,114],"propose":[38,115],"novel":[40,180],"end-to-end":[41],"framework.":[45,227],"Specifically,":[46],"framework":[48,175],"can":[49],"be":[50],"divided":[51],"into":[52,131],"two":[53],"main":[54],"stages.":[55],"In":[56,108],"two-stage":[58],"data":[59],"preprocessing":[60],"module":[61,152],"enhanced":[62],"by":[63],"interpretable":[64],"feature":[65,91,103],"selection,":[66],"utilize":[68],"Local":[70],"Outlier":[71],"Factor":[72],"(LOF)":[73],"algorithm":[74],"to":[75,93,105,127,164,195],"accurately":[76],"detect":[77],"correct":[79],"anomalies":[80],"original":[83],"series.":[86],"Furthermore,":[87],"employ":[89],"SVM-SHAP":[90],"analysis":[92],"quantify":[94],"impact":[96],"environmental":[98],"variables,":[99],"filtering":[100],"out":[101],"combinations":[104],"mitigate":[106],"redundancy.":[107],"module,":[113],"patch-based":[117],"information":[118,150],"fusion":[119,151],"network":[120,144],"(PIF-Net).":[121],"This":[122],"model":[123],"applies":[124],"patching":[125],"technology":[126],"process":[128],"input":[129],"series":[130],"local":[132],"blocks,":[133],"extracting":[134],"features":[136],"through":[137],"shared":[139],"Gated":[140],"Recurrent":[141],"Unit":[142],"(GRU)":[143],"with":[145],"residual":[146],"connections.":[147],"Subsequently,":[148],"an":[149],"based":[153,200],"on":[154,201],"customized":[156],"gating":[157],"mechanism":[158],"integrates":[159],"ensemble":[161],"hidden":[162],"states":[163],"weight":[165],"importance":[167],"different":[169],"patches":[171],"dynamically.":[172],"Additionally,":[173],"is":[176],"trained":[177],"using":[178],"Error-weighted":[181],"Adaptive":[182],"Loss":[183],"(EWAL)":[184],"function.":[185],"By":[186],"combining":[187],"rational":[189],"quadratic":[190],"function":[191],"logarithmic":[193],"loss":[194],"dynamically":[196],"adjust":[197],"penalty":[198],"weights":[199],"real-time":[202],"error":[204],"distributions,":[205],"EWAL":[206],"significantly":[207],"enhances":[208],"model's":[210],"robustness":[211],"under":[212],"extreme":[213],"conditions.":[215],"Finally,":[216],"extensive":[217],"experiments":[218],"demonstrate":[219],"effectiveness":[221],"superiority":[223],"our":[225],"proposed":[226]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-17T00:00:00"}