{"id":"https://openalex.org/W4416549644","doi":"https://doi.org/10.1145/3744255.3798121","title":"CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction","display_name":"CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W4416549644","doi":"https://doi.org/10.1145/3744255.3798121"},"language":null,"primary_location":{"id":"doi:10.1145/3744255.3798121","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3798121","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3744255.3798121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114044057","display_name":"K. Y. Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaiyuan Zhai","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0008-5308-3117","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015529661","display_name":"Jiacheng Cui","orcid":"https://orcid.org/0000-0003-1935-8574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiacheng Cui","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0005-4048-709X","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhehao Zhang","orcid":"https://orcid.org/0009-0004-5947-9375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhehao Zhang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0004-5947-9375","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102264128","display_name":"Junyu Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyu Xue","raw_affiliation_strings":["Southern University of Science and Technology, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0009-1119-326X","affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044266382","display_name":"Yang Deng","orcid":"https://orcid.org/0000-0002-9822-3346"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yang Deng","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-9822-3346","affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029039334","display_name":"Kui Wu","orcid":"https://orcid.org/0000-0002-6857-7231"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kui Wu","raw_affiliation_strings":["University of Victoria, Victoria, Canada"],"raw_orcid":"https://orcid.org/0000-0002-2069-0032","affiliations":[{"raw_affiliation_string":"University of Victoria, Victoria, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067691107","display_name":"Guoming Tang","orcid":"https://orcid.org/0000-0001-9801-1055"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guoming Tang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-9801-1055","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0044082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"75","last_page":"88"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.7062000036239624,"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"}},"topics":[{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.7062000036239624,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.05770000070333481,"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":0.026000000536441803,"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/overfitting","display_name":"Overfitting","score":0.7218999862670898},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.5889999866485596},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4440999925136566},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.37310001254081726},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3650999963283539},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.3610000014305115},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3587000072002411},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3580999970436096},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3122999966144562}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7218999862670898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6333000063896179},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5787000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5663999915122986},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37310001254081726},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2957000136375427},{"id":"https://openalex.org/C122346748","wikidata":"https://www.wikidata.org/wiki/Q1798773","display_name":"HVAC","level":3,"score":0.29159998893737793},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2800999879837036},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3744255.3798121","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3798121","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.06634","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.06634","pdf_url":"https://arxiv.org/pdf/2511.06634","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.06634","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.06634","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:10.1145/3744255.3798121","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3798121","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"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":{"Cross-domain":[0],"HVAC":[1],"energy":[2,9],"prediction":[3],"is":[4,21,192],"essential":[5],"for":[6,17,98],"scalable":[7],"building":[8,20],"management,":[10],"particularly":[11],"because":[12],"collecting":[13],"extensive":[14],"labeled":[15],"data":[16,39,76],"every":[18],"new":[19],"both":[22],"costly":[23],"and":[24,36,45,87,107,134,148,169],"impractical.":[25],"Yet,":[26],"this":[27],"task":[28],"remains":[29],"highly":[30],"challenging":[31],"due":[32],"to":[33,63,65,126,186],"the":[34,187],"scarcity":[35],"heterogeneity":[37],"of":[38],"across":[40],"different":[41],"buildings,":[42],"climate":[43],"zones,":[44],"seasonal":[46],"patterns.":[47],"In":[48,102],"particular,":[49],"buildings":[50,162],"situated":[51],"in":[52,164,179],"distinct":[53],"climatic":[54],"regions":[55],"introduce":[56],"variability":[57],"that":[58,92,140],"often":[59],"leads":[60],"existing":[61],"methods":[62],"overfit":[64],"spurious":[66],"correlations,":[67],"rely":[68],"heavily":[69],"on":[70,75,156],"expert":[71],"intervention,":[72],"or":[73],"compromise":[74],"diversity.":[77],"To":[78],"address":[79],"these":[80],"limitations,":[81],"we":[82],"propose":[83],"CaberNet,":[84],"a":[85,103,116,122,136,176],"causal":[86,129],"interpretable":[88],"deep":[89],"sequence":[90],"model":[91],"learns":[93],"invariant":[94],"(Markov":[95],"blanket)":[96],"representations":[97],"robust":[99],"cross-domain":[100,145],"prediction.":[101],"purely":[104],"data-driven":[105],"fashion":[106],"without":[108],"requiring":[109],"any":[110],"prior":[111],"knowledge,":[112],"CaberNet":[113,155],"integrates":[114],"i)":[115],"global":[117],"feature":[118],"gate":[119],"trained":[120],"with":[121],"self-supervised":[123],"Bernoulli":[124],"regularization":[125],"distinguish":[127],"superior":[128],"features":[130],"from":[131,160],"inferior":[132],"ones,":[133],"ii)":[135],"domain-wise":[137],"training":[138],"scheme":[139],"balances":[141],"domain":[142],"contributions,":[143],"minimizes":[144],"loss":[146],"variance,":[147],"promotes":[149],"latent":[150],"factor":[151],"independence.":[152],"We":[153],"evaluate":[154],"real-world":[157],"datasets":[158],"collected":[159],"three":[161,165],"located":[163],"climatically":[166],"diverse":[167],"cities,":[168],"it":[170],"consistently":[171],"outperforms":[172],"all":[173],"baselines,":[174],"achieving":[175],"22.9%":[177],"reduction":[178],"normalized":[180],"mean":[181],"squared":[182],"error":[183],"(NMSE)":[184],"compared":[185],"best":[188],"benchmark.":[189],"Our":[190],"code":[191],"available":[193],"at":[194],"https://github.com/SusCom-Lab/CaberNet-CRL.":[195]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-11-12T00:00:00"}
