Physics-informed machine learning framework to model buildings from incomplete information

Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five mod...

Full description

Saved in:
Bibliographic Details
Published inJournal of physics. Conference series Vol. 2600; no. 7; pp. 72013 - 72018
Main Authors Kuo, T, Manikkan, S, Bilionis, I, Liu, X, Karava, P
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.11.2023
Subjects
Buildings
Machine learning
Physics
Statistical methods
Online AccessGet full text

Cover

Abstract Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five modules: building survey, building asset database, building information schema, multi-class classification, and physics-based energy model. To illustrate the framework’s effectiveness, we present a case study involving a building with two possible baselines. The results demonstrate that our developed framework successfully generates comprehensive building energy models even when faced with incomplete, effectively capturing baseline scenarios.
AbstractList Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five modules: building survey, building asset database, building information schema, multi-class classification, and physics-based energy model. To illustrate the framework’s effectiveness, we present a case study involving a building with two possible baselines. The results demonstrate that our developed framework successfully generates comprehensive building energy models even when faced with incomplete, effectively capturing baseline scenarios.
This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five modules: building survey, building asset database, building information schema, multi-class classification, and physics-based energy model. To illustrate the framework’s effectiveness, we present a case study involving a building with two possible baselines. The results demonstrate that our developed framework successfully generates comprehensive building energy models even when faced with incomplete, effectively capturing baseline scenarios.
Author Manikkan, S
Liu, X
Karava, P
Kuo, T
Bilionis, I
Author_xml – sequence: 1
  givenname: T
  surname: Kuo
  fullname: Kuo, T
  organization: Purdue University, West Lafayette , USA
– sequence: 2
  givenname: S
  surname: Manikkan
  fullname: Manikkan, S
  organization: Purdue University, West Lafayette , USA
– sequence: 3
  givenname: I
  surname: Bilionis
  fullname: Bilionis, I
  organization: Purdue University, West Lafayette , USA
– sequence: 4
  givenname: X
  surname: Liu
  fullname: Liu, X
  organization: University of Nebraska-Lincoln, Lincoln , USA
– sequence: 5
  givenname: P
  surname: Karava
  fullname: Karava, P
  organization: Purdue University, West Lafayette , USA
BookMark eNqFkF1LwzAUhoMouE1_gwXvhNokTZv0UoafDByoN96END11mW1Skw3Zv7elMhEEc3MC73PeA88UHVpnAaEzgi8JFiIhnNE4z4o8oTnGCU8wp5ikB2iyTw73fyGO0TSENcZp__gEvS5Xu2B0iI2tnW-hilqlV8ZC1IDy1ti3qPaqhU_n36ONi1pXQROVW9NUfRb60LWRsdq1XQMbiMYatTHOnqCjWjUBTr_nDL3cXD_P7-LF4-39_GoRa8pZGudcF5lgpIQMaspKRTXWuqKFSHNRpVDqnBUCBGRlVlVlyVmmOOCCMSoAU5bO0PnY23n3sYWwkWu39bY_KakockIKmuY9xUdKexeCh1p23rTK7yTBchApB0Vy0CUHkZLLUWS_eTFuGtf9VD8s50-_QdlVdQ-nf8D_nfgCmUiFug
ContentType Journal Article
Copyright Published under licence by IOP Publishing Ltd
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: Published under licence by IOP Publishing Ltd
– notice: Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID O3W
TSCCA
AAYXX
CITATION
8FD
8FE
8FG
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
H8D
HCIFZ
L7M
P5Z
P62
PIMPY
PQEST
PQQKQ
PQUKI
PRINS
DOI 10.1088/1742-6596/2600/7/072013
DatabaseName IOP Publishing
IOPscience (Open Access)
CrossRef
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni)
ProQuest Central
Advanced Technologies & Aerospace Database‎ (1962 - current)
ProQuest Central Essentials
AUTh Library subscriptions: ProQuest Central
Technology Collection
ProQuest One Community College
ProQuest Central
Aerospace Database
SciTech Premium Collection (Proquest) (PQ_SDU_P3)
Advanced Technologies Database with Aerospace
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
Publicly Available Content Database
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
DatabaseTitle CrossRef
Publicly Available Content Database
Advanced Technologies & Aerospace Collection
Technology Collection
Technology Research Database
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
Advanced Technologies & Aerospace Database
Aerospace Database
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest One Academic
Advanced Technologies Database with Aerospace
DatabaseTitleList CrossRef
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: O3W
  name: IOP_英国物理学会OA刊
  url: http://iopscience.iop.org/
  sourceTypes:
    Enrichment Source
    Publisher
– sequence: 2
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 1742-6596
ExternalDocumentID 10_1088_1742_6596_2600_7_072013
JPCS_2600_7_072013
GroupedDBID 1JI
29L
2WC
4.4
5B3
5GY
5PX
5VS
7.Q
AAJIO
AAJKP
ABHWH
ACAFW
ACHIP
AEFHF
AEJGL
AFKRA
AFYNE
AIYBF
AKPSB
ALMA_UNASSIGNED_HOLDINGS
ARAPS
ASPBG
ATQHT
AVWKF
AZFZN
BENPR
BGLVJ
CCPQU
CEBXE
CJUJL
CRLBU
CS3
DU5
E3Z
EBS
EDWGO
EQZZN
F5P
FRP
GROUPED_DOAJ
GX1
HCIFZ
HH5
IJHAN
IOP
IZVLO
J9A
KNG
KQ8
LAP
N5L
N9A
O3W
OK1
P2P
PIMPY
PJBAE
RIN
RNS
RO9
ROL
SY9
T37
TR2
TSCCA
UCJ
W28
XSB
~02
AAYXX
CITATION
8FD
8FE
8FG
ABUWG
AZQEC
DWQXO
H8D
L7M
P62
PQEST
PQQKQ
PQUKI
PRINS
ID FETCH-LOGICAL-c2743-67c95841be5ef24ba2c0ccd298368d3ebc6498e8e5b5ddbb745a7e094428e0243
IEDL.DBID IOP
ISSN 1742-6588
IngestDate Thu Oct 10 17:11:05 EDT 2024
Fri Aug 23 21:12:04 EDT 2024
Sun Aug 18 14:50:26 EDT 2024
Tue Aug 20 22:15:36 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 7
Language English
License Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2743-67c95841be5ef24ba2c0ccd298368d3ebc6498e8e5b5ddbb745a7e094428e0243
OpenAccessLink https://proxy.k.utb.cz/login?url=https://iopscience.iop.org/article/10.1088/1742-6596/2600/7/072013
PQID 2896119236
PQPubID 4998668
PageCount 6
ParticipantIDs proquest_journals_2896119236
crossref_primary_10_1088_1742_6596_2600_7_072013
iop_journals_10_1088_1742_6596_2600_7_072013
PublicationCentury 2000
PublicationDate 20231101
PublicationDateYYYYMMDD 2023-11-01
PublicationDate_xml – month: 11
  year: 2023
  text: 20231101
  day: 01
PublicationDecade 2020
PublicationPlace Bristol
PublicationPlace_xml – name: Bristol
PublicationTitle Journal of physics. Conference series
PublicationTitleAlternate J. Phys.: Conf. Ser
PublicationYear 2023
Publisher IOP Publishing
Publisher_xml – name: IOP Publishing
References Deru (JPCS_2600_7_072013bib1) 2011
JPCS_2600_7_072013bib7
JPCS_2600_7_072013bib9
Mitchell (JPCS_2600_7_072013bib12) 2012
JPCS_2600_7_072013bib10
JPCS_2600_7_072013bib3
Illuminating Engineering Society of North America (JPCS_2600_7_072013bib6) 2022; 90
Thornton (JPCS_2600_7_072013bib13) 2013
File (JPCS_2600_7_072013bib8) 2015
Handbook (JPCS_2600_7_072013bib5) 1996; 39
Hilt (JPCS_2600_7_072013bib11) 1977
Moncef (JPCS_2600_7_072013bib4) 2020
Wilson (JPCS_2600_7_072013bib2) 2022
References_xml – year: 2011
  ident: JPCS_2600_7_072013bib1
  contributor:
    fullname: Deru
– year: 2022
  ident: JPCS_2600_7_072013bib2
  contributor:
    fullname: Wilson
– year: 1977
  ident: JPCS_2600_7_072013bib11
  contributor:
    fullname: Hilt
– year: 2013
  ident: JPCS_2600_7_072013bib13
  contributor:
    fullname: Thornton
– ident: JPCS_2600_7_072013bib10
– ident: JPCS_2600_7_072013bib9
– volume: 90
  year: 2022
  ident: JPCS_2600_7_072013bib6
  contributor:
    fullname: Illuminating Engineering Society of North America
– year: 2015
  ident: JPCS_2600_7_072013bib8
  contributor:
    fullname: File
– ident: JPCS_2600_7_072013bib7
– ident: JPCS_2600_7_072013bib3
– year: 2012
  ident: JPCS_2600_7_072013bib12
  contributor:
    fullname: Mitchell
– volume: 39
  year: 1996
  ident: JPCS_2600_7_072013bib5
  publication-title: HVAC systems and equipment
  contributor:
    fullname: Handbook
– year: 2020
  ident: JPCS_2600_7_072013bib4
  contributor:
    fullname: Moncef
SSID ssj0033337
Score 2.3914227
Snippet Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of...
This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information,...
SourceID proquest
crossref
iop
SourceType Aggregation Database
Enrichment Source
Publisher
StartPage 72013
SubjectTerms Buildings
Machine learning
Physics
Statistical methods
SummonAdditionalLinks – databaseName: AUTh Library subscriptions: ProQuest Central
  dbid: BENPR
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3NS8MwFA9uQ_AifuJ0SkCPhn6laXISHRtj4BjqYHgJTfoqHtyqrf-_SZsyhqA9lfadfi95H8l7v4fQDffTKIWIEgaaEwoxkDTPBaGxSoMQUgiF7R1-nLHJgk6X8dIduJWurLK1ibWhztbanpF7JjFggQ1H2F3xSezUKHu76kZodFAvNJmC30W9h9Fs_tTa4sg8SdMSGRLja3lb4WXSPvdNMM9ytHuJ5yfGGUZb_qnzvi5-Gena84wP0L4LGfF9o-NDtAOrI7Rbl27q8hi9ujfScKBChj_qAknAbiLEG87bCixcrXE9-wYrNw67xLbBBFuOBssTXAF2VKpWYSdoMR69DCfETUwgOrRcoyzRwkQUgTKY5yFVaah9rbNQ8IjxLAKlGRUcOMQqzjKlEhqnCZgMzyQhYLkJT1F3tV7BGcI0N-pVShuxhPp5INJcRAZjFUQgMmB95Lc4yaIhxpD1hTbn0kIrLbTSQisT2UDbR7cGT-k2Sfm_-PWW-HQ-fN6WkEWW99GgVc9GdLNYzv_-fYH27Pz4prlwgLrV1zdcmiijUlduKf0ARrzMrA
  priority: 102
  providerName: ProQuest
Title Physics-informed machine learning framework to model buildings from incomplete information
URI https://iopscience.iop.org/article/10.1088/1742-6596/2600/7/072013
https://www.proquest.com/docview/2896119236
Volume 2600
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB51QZV6gT7VBbqy1B7JbhI7jn0ExJYiFVZtUVEvVuxMeqi6u2LDhV_POHZEl6qqEDlEPowTZzyehzLzDcAHlVa8Qi4SiU4lAgtMqqbRiShsleVYYa597fDnM3lyIU4vi8s_a2EWy6j6xzQMQMGBhTEhTk3Ih84TWWg58eDqk3KSlmTF-AA2OZ0YH4B9Op_12pjTVYaiSD9JqT7H698PWrNQA1rFX2q6sz3TbXD9qkPKya_xdWvH7uYeoOPjPus5bEXXlB2EGS_gCc5fwtMuRdStXsGPOEoC1irW7HeXiIksdp74yZo-04u1C9b12GE2tt1eMV_IwjwWhMcjbpFFyFYvGK_hYnr87egkiZ0ZEpd7TFNZOk2eS2Zpb5tc2Cp3qXN1rhWXquZonRRaocLCFnVtbSmKqkSKJCnYQY-B-AY25os5vgUmGhIjax2RlSJtMl01mlPUZzOOukY5hLTfDbMMABym-3GulPEsM55lxrPMlCawbAj7xGQTD-Pq_-Tv18hPZ0df1ynMsm6GsNcLwR0pRasy8z6y3HnYO3fhme9bH4oa92CjvbrGd-TdtHYEAzX9OILNw-Oz2ZdRJ8x0P-ffbwFLdO8R
link.rule.ids 315,783,787,12777,21400,27936,27937,33385,33756,38877,38902,43612,43817,53854,53880
linkProvider IOP Publishing
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV1LSwMxEA7aInoRn1itGtCjofvIZpOTqFhq1VJ8gHgJm-yseLCttv5_M7tZShF0T8vunL5J5pHMfEPIqQyyOIOYMwFWMg4JsKwoFOOJycIIMogU9g7fD0Tvmfdfkhd_4Db1ZZW1TSwNdT62eEbecYmBCDEcEeeTT4ZTo_B21Y_QWCZNpKpyyVfz8nowfKhtceyetGqJjJjztbKu8HJpn_-mRAc52jtpJ0idM4wX_NPy-3jyy0iXnqe7QdZ9yEgvKh1vkiUYbZGVsnTTTrfJq39jFQcq5PSjLJAE6idCvNGirsCiszEtZ99Q48dhTyk2mFDkaECe4BlQT6WKCtshz93rp6se8xMTmI2Qa1SkVrmIIjQO8yLiJotsYG0eKRkLmcdgrOBKgoTEJHluTMqTLAWX4bkkBJCbcJc0RuMR7BHKC6deY6wTS3lQhCorVOyyMRPGoHIQLRLUOOlJRYyhywttKTVCqxFajdDqVFfQtsiZw1P7TTL9X_xkQbw_vHpclNCTvGiRdq2eueh8sez__fuYrPae7u_03c3g9oCs4Sz5qtGwTRqzr284dBHHzBz5ZfUDJQvPpg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dT9swED9RJhAviA0QhY5ZYo8L-bDj2I-IUTE2WKWtWsWLFTsXnmgrWv5_zokzVCGEyJMfLon1O-c-lLvfAXxVSclL5CKS6FQkMMeorGsdidyWaYYlZtr3Dl_fyMuxuJrkkzUY_u-Fmc2D6T-lZUsU3EIYCuJUTDF0Fslcy9iTq8dFnBTkxXg8r-oefMg9uwmd69_8X2eROV1F2xjpb1Sqq_N6_WErXqpHO3lhqhv_M9yB7RA4srN2mx9hDaefYKMp4HSLXbgNq6hlQsWK3TdlksjCXIg7Vnd1WGw5Y80EHGbDUOwF820mzDM1eLbgJbJAqOrVtgfj4cXf88sozE2IXOYZR2XhNMUVqSXk60zYMnOJc1WmFZeq4midFFqhwtzmVWVtIfKyQMrzKBVBz1C4D-vT2RQPgImalGytI7FCJHWqy1pzyslsylFXKPuQdDiZeUuPYZrf2koZD63x0BoPrSlMC20fvhGeJnwqi7fFT1bEr0bnf1YlDCm-D4NOPc-ilEvK1Eew8vB97_wCm6PvQ_Prx83PI9jyA-bb7sMBrC8fHvEzhSFLe9ycsSfeY89_
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Physics-informed+machine+learning+framework+to+model+buildings+from+incomplete+information&rft.jtitle=Journal+of+physics.+Conference+series&rft.au=Kuo%2C+T&rft.au=Manikkan%2C+S&rft.au=Bilionis%2C+I&rft.au=Liu%2C+X&rft.date=2023-11-01&rft.pub=IOP+Publishing&rft.issn=1742-6588&rft.eissn=1742-6596&rft.volume=2600&rft.issue=7&rft_id=info:doi/10.1088%2F1742-6596%2F2600%2F7%2F072013&rft.externalDocID=JPCS_2600_7_072013
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1742-6588&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1742-6588&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1742-6588&client=summon