{"id":"https://openalex.org/W2952648887","doi":"https://doi.org/10.1145/3307772.3328316","title":"A Bayesian Data Analytics Approach to Buildings' Thermal Parameter Estimation","display_name":"A Bayesian Data Analytics Approach to Buildings' Thermal Parameter Estimation","publication_year":2019,"publication_date":"2019-06-13","ids":{"openalex":"https://openalex.org/W2952648887","doi":"https://doi.org/10.1145/3307772.3328316","mag":"2952648887"},"language":"en","primary_location":{"id":"doi:10.1145/3307772.3328316","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3307772.3328316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Future Energy Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.13016/m2mzu2-mymg","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103348814","display_name":"Nilavra Pathak","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nilavra Pathak","raw_affiliation_strings":["Information Systems, University of Maryland, Baltimore County"],"affiliations":[{"raw_affiliation_string":"Information Systems, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010326834","display_name":"James R. Foulds","orcid":"https://orcid.org/0000-0003-0935-4182"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Foulds","raw_affiliation_strings":["Information Systems, University of Maryland, Baltimore County"],"affiliations":[{"raw_affiliation_string":"Information Systems, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068320631","display_name":"Nirmalya Roy","orcid":"https://orcid.org/0000-0003-4827-3393"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nirmalya Roy","raw_affiliation_strings":["Information Systems, University of Maryland, Baltimore County"],"affiliations":[{"raw_affiliation_string":"Information Systems, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012819177","display_name":"Nilanjan Banerjee","orcid":"https://orcid.org/0000-0003-4466-0898"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nilanjan Banerjee","raw_affiliation_strings":["Computer Science & Electrical Engineering, University of Maryland, Baltimore County"],"affiliations":[{"raw_affiliation_string":"Computer Science & Electrical Engineering, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034204308","display_name":"Ryan Robucci","orcid":"https://orcid.org/0000-0002-3638-1736"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Robucci","raw_affiliation_strings":["Computer Science & Electrical Engineering, University of Maryland, Baltimore County"],"affiliations":[{"raw_affiliation_string":"Computer Science & Electrical Engineering, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103348814"],"corresponding_institution_ids":["https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":0.8348,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72738892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"89","last_page":"99"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9851999878883362,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9847000241279602,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6913915276527405},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6869831085205078},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6301196217536926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.588584303855896},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5638182163238525},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.529380738735199},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4791642725467682},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45325854420661926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.453009694814682}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6913915276527405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869831085205078},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6301196217536926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.588584303855896},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5638182163238525},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.529380738735199},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4791642725467682},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45325854420661926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.453009694814682},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3307772.3328316","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3307772.3328316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Future Energy Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:mdsoar.org:11603/16299","is_oa":false,"landing_page_url":"http://hdl.handle.net/11603/16299","pdf_url":null,"source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.13016/m2mzu2-mymg","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m2mzu2-mymg","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"article"}],"best_oa_location":{"id":"doi:10.13016/m2mzu2-mymg","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m2mzu2-mymg","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8199999928474426,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G4765142072","display_name":"CPS: Breakthrough: Low-cost Continuous Virtual Energy Audits in Cyber-Physical Building Envelope","funder_award_id":"1544687","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W121191684","https://openalex.org/W173732400","https://openalex.org/W1512208174","https://openalex.org/W1805590745","https://openalex.org/W1988685575","https://openalex.org/W1990751728","https://openalex.org/W2002954424","https://openalex.org/W2022539494","https://openalex.org/W2023543162","https://openalex.org/W2040640105","https://openalex.org/W2045656233","https://openalex.org/W2047718938","https://openalex.org/W2049425685","https://openalex.org/W2067833575","https://openalex.org/W2068358064","https://openalex.org/W2090114421","https://openalex.org/W2098613108","https://openalex.org/W2103878673","https://openalex.org/W2115123452","https://openalex.org/W2115979064","https://openalex.org/W2129728285","https://openalex.org/W2161194249","https://openalex.org/W2161523118","https://openalex.org/W2217402295","https://openalex.org/W2225156818","https://openalex.org/W2345130294","https://openalex.org/W2384495648","https://openalex.org/W2478027467","https://openalex.org/W2495364486","https://openalex.org/W2746142811","https://openalex.org/W2754380514","https://openalex.org/W2754835109","https://openalex.org/W2792640994","https://openalex.org/W2798906382","https://openalex.org/W2962994101","https://openalex.org/W2963977107","https://openalex.org/W3101380508","https://openalex.org/W4235560058","https://openalex.org/W4252930359","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2753218748","https://openalex.org/W2774409638","https://openalex.org/W2464065341","https://openalex.org/W3165662366","https://openalex.org/W4364383560","https://openalex.org/W2146827233","https://openalex.org/W4318483226","https://openalex.org/W2027276540","https://openalex.org/W2033163835","https://openalex.org/W2789413038"],"abstract_inverted_index":{"Modeling":[0],"buildings'":[1,65,83,196],"heat":[2],"dynamics":[3],"is":[4,149],"a":[5,34,57,74,88,104,122,199],"complex":[6],"process":[7,142],"which":[8],"depends":[9],"on":[10,135,160,188],"various":[11],"factors":[12],"including":[13],"weather,":[14],"building":[15,50],"thermal":[16,70,84],"capacity,":[17],"insulation":[18],"preservation,":[19],"and":[20,67,81,86,143,164,174,190],"residents'":[21],"behavior.":[22],"Gray-box":[23],"models":[24],"offer":[25],"an":[26],"explanation":[27],"of":[28,49,60,121,157],"those":[29],"dynamics,":[30],"as":[31,119],"expressed":[32],"in":[33,181],"few":[35],"parameters":[36,163,170,197],"specific":[37],"to":[38,63,138,177,194],"built":[39],"environments":[40],"that":[41,78,91,103,120,145],"can":[42,79,92,114],"provide":[43],"compelling":[44],"insights":[45],"into":[46],"the":[47,98,140,146,155,161,179],"characteristics":[48],"artifacts.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,168],"present":[56],"systematic":[58],"study":[59,154],"Bayesian":[61,75,147],"approaches":[62],"modeling":[64],"parameters,":[66],"hence":[68],"their":[69],"characteristics.":[71],"We":[72,100,131,152,184],"build":[73],"state-space":[76],"model":[77,162,180],"adapt":[80,94],"incorporate":[82],"equations":[85],"postulate":[87],"generalized":[89],"solution":[90],"easily":[93],"prior":[95,158],"knowledge":[96],"regarding":[97],"parameters.":[99],"then":[101],"show":[102],"faster":[105],"approximate":[106],"approach":[107,148],"using":[108],"Variational":[109],"Inference":[110],"for":[111],"parameter":[112],"estimation":[113],"posit":[115],"similar":[116],"parameters'":[117],"quantification":[118],"more":[123,150],"time-consuming":[124],"Markov":[125],"Chain":[126],"Monte":[127],"Carlo":[128],"(MCMC)":[129],"approach.":[130],"perform":[132,185],"extensive":[133,186],"evaluations":[134,187],"two":[136],"datasets":[137],"understand":[139],"generative":[141],"attest":[144],"interpretable.":[151],"further":[153],"effects":[156],"selection":[159],"transfer":[165],"learning,":[166],"where":[167],"learn":[169],"from":[171],"one":[172],"season":[173],"reuse":[175],"them":[176],"fit":[178],"other":[182],"seasons.":[183],"controlled":[189],"real":[191],"data":[192],"traces":[193],"enumerate":[195],"within":[198],"95%":[200],"credible":[201],"interval.":[202]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}