{"id":"https://openalex.org/W4415991277","doi":"https://doi.org/10.3390/info16110964","title":"An AI Hybrid Building Energy Benchmarking Framework Across Two Time Scales","display_name":"An AI Hybrid Building Energy Benchmarking Framework Across Two Time Scales","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4415991277","doi":"https://doi.org/10.3390/info16110964"},"language":"en","primary_location":{"id":"doi:10.3390/info16110964","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info16110964","pdf_url":"https://www.mdpi.com/2078-2489/16/11/964/pdf?version=1762507184","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/16/11/964/pdf?version=1762507184","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100715384","display_name":"Yi Lu","orcid":"https://orcid.org/0000-0002-7561-7609"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Lu","raw_affiliation_strings":["Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA"],"raw_orcid":"https://orcid.org/0000-0002-7561-7609","affiliations":[{"raw_affiliation_string":"Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004984485","display_name":"Tian Li","orcid":"https://orcid.org/0000-0003-2123-1679"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tian Li","raw_affiliation_strings":["Architecture Program, University of Nebraska\u2013Lincoln, Lincoln, NE 68588, USA"],"raw_orcid":"https://orcid.org/0000-0003-2123-1679","affiliations":[{"raw_affiliation_string":"Architecture Program, University of Nebraska\u2013Lincoln, Lincoln, NE 68588, USA","institution_ids":["https://openalex.org/I114395901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004984485"],"corresponding_institution_ids":["https://openalex.org/I114395901"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1782},"fwci":0.5135,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67348632,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"16","issue":"11","first_page":"964","last_page":"964"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.7343000173568726,"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":0.7343000173568726,"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":0.15199999511241913,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.017000000923871994,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.9126999974250793},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6348999738693237},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.6051999926567078},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.569100022315979},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.45899999141693115},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4474000036716461},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.44609999656677246}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9126999974250793},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6348999738693237},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5708000063896179},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.569100022315979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5397999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4848000109195709},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44449999928474426},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4172999858856201},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.25769999623298645}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/info16110964","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info16110964","pdf_url":"https://www.mdpi.com/2078-2489/16/11/964/pdf?version=1762507184","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cc801045250648f0b6865bbe29ac0813","is_oa":true,"landing_page_url":"https://doaj.org/article/cc801045250648f0b6865bbe29ac0813","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 16, Iss 11, p 964 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info16110964","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info16110964","pdf_url":"https://www.mdpi.com/2078-2489/16/11/964/pdf?version=1762507184","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338290","display_name":"National Renewable Energy Laboratory","ror":"https://ror.org/036266993"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415991277.pdf","grobid_xml":"https://content.openalex.org/works/W4415991277.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1869076582","https://openalex.org/W1990517717","https://openalex.org/W2024848792","https://openalex.org/W2039873724","https://openalex.org/W2043466579","https://openalex.org/W2051607409","https://openalex.org/W2064173787","https://openalex.org/W2079810998","https://openalex.org/W2110802877","https://openalex.org/W2163167422","https://openalex.org/W2298779432","https://openalex.org/W2339603402","https://openalex.org/W2340972631","https://openalex.org/W2618194852","https://openalex.org/W2621900912","https://openalex.org/W2644490625","https://openalex.org/W2754029504","https://openalex.org/W2761875693","https://openalex.org/W2765907384","https://openalex.org/W2780163646","https://openalex.org/W2795498150","https://openalex.org/W2890367559","https://openalex.org/W2897784558","https://openalex.org/W2912647716","https://openalex.org/W2965392186","https://openalex.org/W2982435754","https://openalex.org/W2994947794","https://openalex.org/W3006796022","https://openalex.org/W3013295261","https://openalex.org/W3106394166","https://openalex.org/W3111640765","https://openalex.org/W3158049794","https://openalex.org/W3184938584","https://openalex.org/W4210372445","https://openalex.org/W4224235845","https://openalex.org/W4294559022","https://openalex.org/W4310071385","https://openalex.org/W4310881975","https://openalex.org/W4312497066","https://openalex.org/W4319166261","https://openalex.org/W4324143991","https://openalex.org/W4390658280","https://openalex.org/W4391333623","https://openalex.org/W4395057223","https://openalex.org/W4396614212","https://openalex.org/W4402742350","https://openalex.org/W4405834753","https://openalex.org/W4406972188","https://openalex.org/W4408938001","https://openalex.org/W4409994347"],"related_works":[],"abstract_inverted_index":{"Buildings":[0],"account":[1],"for":[2,19,72,314],"approximately":[3],"one-third":[4],"of":[5,49,62,271,294],"global":[6],"energy":[7,14,74,98,233],"usage":[8],"and":[9,59,80,90,96,120,127,146,153,219,232,257,274,305,311,321],"associated":[10],"carbon":[11,326],"emissions,":[12],"making":[13],"benchmarking":[15,23,75],"a":[16,68,136,163,308],"crucial":[17],"tool":[18,313],"advancing":[20],"decarbonization.":[21],"Current":[22],"studies":[24],"have":[25,155],"often":[26,287],"been":[27,156],"limited":[28],"to":[29,45,93,110,189,203],"mainly":[30],"the":[31,47,56,151,159,175,195,208,216,237,242,262,284,292],"annual":[32,95,126,217],"scale,":[33],"relied":[34],"heavily":[35],"on":[36],"simulation-based":[37],"approaches,":[38],"or":[39],"employed":[40],"regression":[41,205],"methods":[42],"that":[43,194,229,250,280],"fail":[44],"capture":[46],"complexity":[48],"diverse":[50],"building":[51,73,113,144,230,303],"stock.":[52],"These":[53],"limitations":[54],"hinder":[55],"interpretability,":[57],"generalizability,":[58],"actionable":[60],"value":[61,293],"existing":[63],"models.":[64],"This":[65],"study":[66],"introduces":[67],"hybrid":[69,196,299],"AI":[70],"framework":[71,83,300],"across":[76,222],"two":[77],"time":[78],"scales\u2014annual":[79],"monthly.":[81],"The":[82,115,131,297],"integrates":[84],"supervised":[85,116],"learning":[86,103,173],"models,":[87,92,174],"including":[88],"white-":[89],"gray-box":[91,243],"predict":[94],"monthly":[97,129,224],"consumption,":[99],"combined":[100],"with":[101,207,307],"unsupervised":[102,172],"through":[104],"neural":[105],"network-based":[106],"Self-Organizing":[107],"Maps":[108],"(SOM),":[109],"classify":[111],"heterogeneous":[112],"stocks.":[114],"models":[117],"provide":[118],"interpretable":[119],"accurate":[121],"predictions":[122],"at":[123,215],"both":[124],"aggregated":[125],"fine-grained":[128],"levels.":[130],"model":[132,210],"is":[133],"trained":[134],"using":[135,245],"six-year":[137],"dataset":[138,161],"from":[139,162],"Washington,":[140],"D.C.,":[141],"incorporating":[142],"multiple":[143],"attributes":[145],"high-resolution":[147],"weather":[148],"data.":[149],"Additionally,":[150],"generalizability":[152],"robustness":[154],"validated":[157],"via":[158],"real-world":[160],"different":[164],"climate":[165],"zone":[166],"in":[167,181],"Pittsburgh,":[168],"PA.":[169],"Followed":[170],"by":[171],"SOM":[176,277],"clustering":[177],"preserves":[178],"topological":[179],"relationships":[180],"high-dimensional":[182],"data,":[183],"enabling":[184],"more":[185],"nuanced":[186],"classification":[187],"compared":[188,202],"centroid-based":[190],"methods.":[191],"Results":[192],"demonstrate":[193],"approach":[197],"significantly":[198],"improves":[199],"predictive":[200],"accuracy":[201],"conventional":[204],"methods,":[206],"proposed":[209,298],"achieving":[211],"over":[212],"80%":[213],"R2":[214],"scale":[218],"robust":[220],"performance":[221],"seasonal":[223],"predictions.":[225],"White-box":[226],"sensitivity":[227],"highlights":[228],"type":[231],"use":[234],"patterns":[235],"are":[236,261],"most":[238,264],"influential":[239,265],"variables,":[240],"while":[241],"analysis":[244],"SHAP":[246,269],"values":[247,270],"further":[248],"reveals":[249],"Energy":[251],"Star\u00ae":[252],"rating,":[253],"Natural":[254],"Gas":[255],"(%),":[256],"Electricity":[258],"Use":[259],"(%)":[260],"three":[263],"predictors,":[266],"contributing":[267],"mean":[268],"8.69,":[272],"8.46,":[273],"6.47,":[275],"respectively.":[276],"results":[278],"reveal":[279],"categorized":[281],"buildings":[282],"within":[283],"same":[285],"cluster":[286],"share":[288],"similar":[289],"energy-use":[290],"patterns\u2014underscoring":[291],"data-driven":[295],"classification.":[296],"provides":[301],"policymakers,":[302],"managers,":[304],"designers":[306],"scalable,":[309],"transparent,":[310],"transferable":[312],"identifying":[315],"energy-saving":[316],"opportunities,":[317],"prioritizing":[318],"retrofit":[319],"strategies,":[320],"accelerating":[322],"progress":[323],"toward":[324],"net-zero":[325],"buildings.":[327]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-11-07T00:00:00"}
