{"id":"https://openalex.org/W7165910021","doi":"https://doi.org/10.48550/arxiv.2606.25301","title":"Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification","display_name":"Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7165910021","doi":"https://doi.org/10.48550/arxiv.2606.25301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25301","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.2606.25301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133762996","display_name":"Nam T. Nguyen","orcid":"https://orcid.org/0000-0001-8475-2530"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Nam T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139346520","display_name":"Truong X. Nghiem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nghiem, Truong X.","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/T10121","display_name":"Building Energy and Comfort Optimization","score":0.5741999745368958,"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":0.5741999745368958,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.13379999995231628,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.033399999141693115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5264000296592712},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4880000054836273},{"id":"https://openalex.org/keywords/hvac","display_name":"HVAC","score":0.4593000113964081},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.4560000002384186},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.446399986743927},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.43959999084472656},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4162999987602234}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6100000143051147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.539900004863739},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5264000296592712},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C122346748","wikidata":"https://www.wikidata.org/wiki/Q1798773","display_name":"HVAC","level":3,"score":0.4593000113964081},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.4560000002384186},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4156999886035919},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.34290000796318054},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2791000008583069},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.275299996137619},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2671999931335449},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25450000166893005},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25301","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.2606.25301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25301","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":[{"score":0.7491076588630676,"id":"https://metadata.un.org/sdg/7","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":{"Machine":[0],"learning":[1,58,192],"(ML)":[2],"techniques":[3,109,129],"have":[4],"been":[5],"commonly":[6],"adopted":[7],"to":[8,18,22,63,67,204],"identify":[9],"the":[10,28,33,36,44,143,155,164,207],"dynamics":[11],"of":[12,30,35,88,202,211],"building":[13,93,157],"energy":[14,94],"systems":[15],"(BESs),":[16],"owing":[17],"their":[19],"flexibility":[20],"relative":[21],"first-principles,":[23],"physics-based":[24],"modeling":[25],"approaches.":[26],"Beyond":[27],"choice":[29],"ML":[31,112],"architecture,":[32],"quality":[34],"training":[37],"data":[38,133],"plays":[39],"an":[40],"essential":[41],"role":[42],"in":[43,53,65],"resulting":[45],"model":[46,113,139],"performance.":[47],"Optimal":[48],"experimental":[49],"design":[50],"(OED),":[51],"realized":[52],"this":[54,212],"work":[55],"through":[56],"active":[57],"(AL),":[59],"determines":[60],"which":[61],"experiments":[62],"conduct":[64],"order":[66],"collect":[68],"informative":[69],"data,":[70],"rather":[71],"than":[72],"relying":[73],"on":[74,101,154],"standard":[75],"approaches":[76],"such":[77],"as":[78,163],"uniformly":[79,195],"random":[80,196],"sampling.":[81],"This":[82],"paper":[83],"proposes":[84],"a":[85,98,116,122],"systematic":[86],"comparison":[87],"OED":[89],"via":[90,190],"AL":[91,108,144],"for":[92],"system":[95],"identification,":[96],"with":[97,173,194],"particular":[99],"focus":[100],"HVAC":[102],"thermal":[103],"dynamics.":[104],"We":[105],"investigate":[106],"fourteen":[107],"across":[110,169,215],"two":[111],"classes,":[114],"namely":[115],"deterministic":[117],"feedforward":[118],"neural":[119],"network":[120],"and":[121,126,138,151,178,209,218],"stochastic":[123],"Gaussian":[124],"process,":[125],"classify":[127],"these":[128],"into":[130],"four":[131],"categories:":[132],"space,":[134],"uncertainty,":[135],"information":[136],"gain,":[137],"change.":[140],"To":[141],"examine":[142],"algorithms":[145],"under":[146],"realistic":[147],"conditions,":[148],"we":[149],"implement":[150],"evaluate":[152],"them":[153],"high-fidelity":[156],"simulator":[158],"BOPTEST.":[159],"The":[160],"results,":[161],"reported":[162],"root":[165],"mean":[166],"square":[167],"error":[168,200],"multiple":[170],"test":[171],"scenarios":[172],"varying":[174],"initial":[175],"dataset":[176],"sizes":[177],"control":[179,197],"input":[180],"constraints,":[181],"show":[182],"that":[183],"AL-based":[184],"models":[185,188],"generally":[186],"outperform":[187],"trained":[189],"passive":[191],"(PL)":[193],"inputs,":[198],"achieving":[199],"reductions":[201],"up":[203],"54\\%,":[205],"although":[206],"magnitude":[208],"consistency":[210],"improvement":[213],"vary":[214],"acquisition":[216],"functions":[217],"operating":[219],"regimes.":[220]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-26T00:00:00"}
