Methods for benchmarking building energy consumption against its past or intended performance: An overview

•This paper reviews up to date methods for building energy benchmarking.•This paper summarizes the major characteristics of these methods.•This paper recommends a flow chart for reader to choose a proper method. Building sector consumes a significant portion of energy worldwide. One of the reasons i...

Full description

Saved in:
Bibliographic Details
Published inApplied energy Vol. 124; pp. 325 - 334
Main Authors Li, Zhengwei, Han, Yanmin, Xu, Peng
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.07.2014
Elsevier
Subjects
Online AccessGet full text

Cover

Loading…
Abstract •This paper reviews up to date methods for building energy benchmarking.•This paper summarizes the major characteristics of these methods.•This paper recommends a flow chart for reader to choose a proper method. Building sector consumes a significant portion of energy worldwide. One of the reasons is that the performance of building and its components degrades over the years. It is found that by improving the performance of existing systems through continuous commissioning, significant energy saving can be achieved. In a continuous commissioning process, energy benchmarking is extremely important for tracking, monitoring and detecting abnormal energy consumption behavior of a building. In this paper, up to date methods and tools available for energy benchmarking purpose are reviewed. It is hoped that with this paper, researchers and building operators are more confident in choosing a proper method (or tool) during the commissioning process.
AbstractList Building sector consumes a significant portion of energy worldwide. One of the reasons is that the performance of building and its components degrades over the years. It is found that by improving the performance of existing systems through continuous commissioning, significant energy saving can be achieved. In a continuous commissioning process, energy benchmarking is extremely important for tracking, monitoring and detecting abnormal energy consumption behavior of a building. In this paper, up to date methods and tools available for energy benchmarking purpose are reviewed. It is hoped that with this paper, researchers and building operators are more confident in choosing a proper method (or tool) during the commissioning process.
•This paper reviews up to date methods for building energy benchmarking.•This paper summarizes the major characteristics of these methods.•This paper recommends a flow chart for reader to choose a proper method. Building sector consumes a significant portion of energy worldwide. One of the reasons is that the performance of building and its components degrades over the years. It is found that by improving the performance of existing systems through continuous commissioning, significant energy saving can be achieved. In a continuous commissioning process, energy benchmarking is extremely important for tracking, monitoring and detecting abnormal energy consumption behavior of a building. In this paper, up to date methods and tools available for energy benchmarking purpose are reviewed. It is hoped that with this paper, researchers and building operators are more confident in choosing a proper method (or tool) during the commissioning process.
Author Xu, Peng
Li, Zhengwei
Han, Yanmin
Author_xml – sequence: 1
  givenname: Zhengwei
  orcidid: 0000-0001-5941-5874
  surname: Li
  fullname: Li, Zhengwei
  email: zhengwei_li@tongji.edu.cn
– sequence: 2
  givenname: Yanmin
  surname: Han
  fullname: Han, Yanmin
– sequence: 3
  givenname: Peng
  surname: Xu
  fullname: Xu, Peng
BackLink http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28475777$$DView record in Pascal Francis
BookMark eNqFkc1u1DAUhS3USkxbXgF5g8QmqX8ydoxYUFVAkYq6KWvLca6nHjJ2sD2t-vY4TLthMyvfxTnn-n7nDJ2EGACh95S0lFBxuW3NDAHS5rllhHYt4S1h5A1a0V6yRlHan6AV4UQ0TFD1Fp3lvCWEMMrICm1_QnmIY8YuJjxAsA87k377sMHD3k_jMhyysY0h73dz8TFgszE-5IJ9yXg2dahmHwqEEUY8Q6phOxMsfMJXAcdHSI8eni7QqTNThncv7zn69e3r_fVNc3v3_cf11W1jO9KXhjpFmKLO9B01ggsjlO3WarCS930n13I0pAPGnLRyAOfo2gkrQPLB8GHsGD9HHw-5c4p_9pCL3vlsYZpMgLjPmlVqnEul5FEpXXeE8b4Tqko_vEhNtmZyqd7ns56Tr8CeNfv3NblEfj7obIo5J3Da-mIWaiUZP2lK9NKa3urX1vTSmiZc19aqXfxnf91w1PjlYISKtuJOOltf-4TRJ7BFj9Efi_gLsme5ag
CODEN APENDX
CitedBy_id crossref_primary_10_1016_j_adapen_2022_100084
crossref_primary_10_1016_j_enbuild_2022_112307
crossref_primary_10_1016_j_enbuild_2021_110886
crossref_primary_10_1016_j_apenergy_2018_10_025
crossref_primary_10_1016_j_enbuild_2020_109810
crossref_primary_10_1016_j_jobe_2019_02_003
crossref_primary_10_3390_buildings15060879
crossref_primary_10_15377_2409_5818_2018_05_3
crossref_primary_10_3390_ijerph17228354
crossref_primary_10_3390_su16030964
crossref_primary_10_1016_j_knosys_2022_108970
crossref_primary_10_1016_j_enbuild_2021_111219
crossref_primary_10_1080_19401493_2024_2444334
crossref_primary_10_1061__ASCE_ME_1943_5479_0000741
crossref_primary_10_2139_ssrn_4180073
crossref_primary_10_1016_j_jobe_2023_106148
crossref_primary_10_1016_j_segan_2018_05_004
crossref_primary_10_1016_j_jclepro_2022_131040
crossref_primary_10_1080_23744731_2019_1693208
crossref_primary_10_1016_j_enbuild_2023_113195
crossref_primary_10_1016_j_tsep_2023_101835
crossref_primary_10_1016_j_jobe_2023_108287
crossref_primary_10_1016_j_jclepro_2019_04_064
crossref_primary_10_1016_j_enbuild_2018_08_031
crossref_primary_10_1016_j_tust_2022_104655
crossref_primary_10_1016_j_enbuild_2021_111208
crossref_primary_10_1016_j_apenergy_2017_08_153
crossref_primary_10_1016_j_apenergy_2016_03_083
crossref_primary_10_1016_j_applthermaleng_2016_01_154
crossref_primary_10_1016_j_rser_2017_09_108
crossref_primary_10_1051_e3sconf_202235601031
crossref_primary_10_1016_j_apenergy_2015_06_043
crossref_primary_10_1016_j_seta_2020_100770
crossref_primary_10_1016_j_enbuild_2016_11_037
crossref_primary_10_1016_j_apenergy_2016_04_035
crossref_primary_10_1016_j_enbuild_2021_111683
crossref_primary_10_1016_j_jobe_2022_105468
crossref_primary_10_1016_j_rser_2017_04_095
crossref_primary_10_1016_j_ijrefrig_2022_12_027
crossref_primary_10_3390_atmos10120743
crossref_primary_10_1016_j_apenergy_2017_07_100
crossref_primary_10_3390_en11030631
crossref_primary_10_1016_j_enbuild_2019_04_029
crossref_primary_10_1016_j_energy_2015_02_014
crossref_primary_10_1016_j_energy_2017_05_148
crossref_primary_10_1016_j_enbuild_2018_07_039
crossref_primary_10_1016_j_apenergy_2020_114614
crossref_primary_10_3390_en14133900
crossref_primary_10_1016_j_enbuild_2023_112920
crossref_primary_10_1016_j_enbuild_2018_03_066
crossref_primary_10_1016_j_enbenv_2019_11_003
crossref_primary_10_1177_1420326X211054429
crossref_primary_10_1016_j_apenergy_2019_113500
crossref_primary_10_1016_j_scs_2024_105844
crossref_primary_10_1088_1757_899X_609_7_072006
crossref_primary_10_1016_j_apenergy_2016_02_030
crossref_primary_10_1016_j_enbuild_2020_110238
crossref_primary_10_1016_j_rser_2022_113045
crossref_primary_10_1016_j_enbuild_2020_110635
crossref_primary_10_1186_s40327_018_0064_7
crossref_primary_10_1016_j_enbuild_2020_110230
crossref_primary_10_1016_j_scs_2019_101835
crossref_primary_10_3390_en15051741
crossref_primary_10_1016_j_enbuild_2018_06_056
crossref_primary_10_1016_j_apenergy_2016_07_043
crossref_primary_10_1016_j_enbuild_2016_07_018
crossref_primary_10_1016_j_enbuild_2022_112502
crossref_primary_10_1016_j_jclepro_2018_04_270
crossref_primary_10_1016_j_enbuild_2024_114803
crossref_primary_10_1016_j_energy_2022_123161
crossref_primary_10_1109_JPROC_2016_2520638
crossref_primary_10_1016_j_enpol_2016_01_012
crossref_primary_10_1007_s12053_017_9582_8
crossref_primary_10_1016_j_enbuild_2022_112508
crossref_primary_10_1016_j_jobe_2017_01_005
crossref_primary_10_1002_gch2_201900065
crossref_primary_10_1016_j_energy_2016_08_028
crossref_primary_10_1016_j_enbuild_2014_07_028
crossref_primary_10_3390_data10030035
crossref_primary_10_1016_j_apenergy_2017_09_060
crossref_primary_10_1016_j_apenergy_2020_115413
crossref_primary_10_3390_technologies10060131
crossref_primary_10_1016_j_enbuild_2021_111486
crossref_primary_10_1016_j_enbuild_2022_112238
crossref_primary_10_3390_en16196879
crossref_primary_10_1016_j_scs_2020_102101
crossref_primary_10_1007_s12053_018_9717_6
crossref_primary_10_1016_j_proeng_2015_09_097
crossref_primary_10_1016_j_eswa_2021_116293
crossref_primary_10_1016_j_comcom_2019_12_020
crossref_primary_10_1016_j_apenergy_2018_05_023
crossref_primary_10_1016_j_enbuild_2019_07_012
crossref_primary_10_1016_j_apenergy_2017_05_052
crossref_primary_10_1016_j_eswa_2022_117649
crossref_primary_10_1016_j_buildenv_2019_04_016
crossref_primary_10_1016_j_rser_2016_01_086
crossref_primary_10_1016_j_rser_2025_115471
crossref_primary_10_3390_en14164850
crossref_primary_10_1016_j_ijrefrig_2018_10_017
crossref_primary_10_1016_j_energy_2015_02_084
crossref_primary_10_1016_j_apenergy_2017_08_237
crossref_primary_10_1016_j_jclepro_2020_125623
crossref_primary_10_1016_j_rser_2016_11_132
crossref_primary_10_1016_j_enbuild_2020_110304
crossref_primary_10_1016_j_apenergy_2019_113548
crossref_primary_10_1016_j_apenergy_2022_119989
crossref_primary_10_1016_j_enbuild_2018_08_040
crossref_primary_10_1007_s10586_022_03573_8
crossref_primary_10_1016_j_egypro_2015_11_754
crossref_primary_10_3390_app10186489
crossref_primary_10_1016_j_autcon_2020_103188
crossref_primary_10_1016_j_enbuild_2023_113419
crossref_primary_10_1016_j_ijepes_2021_106916
crossref_primary_10_1016_j_apenergy_2017_09_116
crossref_primary_10_1016_j_autcon_2020_103344
crossref_primary_10_1002_er_7376
crossref_primary_10_1016_j_jobe_2023_105915
crossref_primary_10_1108_BIJ_02_2023_0099
crossref_primary_10_3390_buildings13112685
crossref_primary_10_1016_j_enbuild_2022_112761
crossref_primary_10_1016_j_enbuild_2015_04_017
crossref_primary_10_1016_j_jobe_2022_104103
crossref_primary_10_1016_j_apenergy_2021_117960
crossref_primary_10_1016_j_enbuild_2024_114581
crossref_primary_10_1016_j_energy_2019_116552
crossref_primary_10_3390_land11111986
crossref_primary_10_1016_j_apenergy_2019_02_056
crossref_primary_10_3390_en17020376
crossref_primary_10_3390_w14030473
crossref_primary_10_3992_1943_4618_14_3_1
crossref_primary_10_1016_j_enbuild_2021_111149
crossref_primary_10_3390_en13195171
crossref_primary_10_1007_s12053_018_9639_3
crossref_primary_10_1016_j_aej_2020_08_022
crossref_primary_10_1061__ASCE_LA_1943_4170_0000532
crossref_primary_10_1016_j_jobe_2023_108185
crossref_primary_10_1016_j_apenergy_2023_121165
crossref_primary_10_5327_z2176_947820200722
crossref_primary_10_1016_j_energy_2022_123341
crossref_primary_10_1016_j_buildenv_2024_112366
crossref_primary_10_1016_j_proeng_2017_10_055
crossref_primary_10_1016_j_apenergy_2015_02_048
crossref_primary_10_1016_j_jobe_2021_102837
crossref_primary_10_1016_j_rser_2016_04_052
crossref_primary_10_1016_j_apenergy_2015_04_036
crossref_primary_10_1109_ACCESS_2019_2906311
Cites_doi 10.1016/j.enbuild.2011.05.020
10.1016/j.buildenv.2007.11.004
10.1016/j.enconman.2008.08.033
10.1016/j.rser.2012.02.049
10.2172/787155
10.1016/j.energy.2006.11.010
10.1080/10789669.2007.10390952
10.1016/j.enbuild.2013.02.050
10.1016/j.apenergy.2012.09.005
10.1016/j.apenergy.2013.05.040
10.1016/j.apenergy.2006.04.002
10.1016/j.enconman.2007.03.018
10.1016/j.enbuild.2010.07.027
10.1016/j.enbuild.2013.03.035
10.1109/59.99410
10.1016/j.rser.2007.11.007
10.1016/j.ijthermalsci.2005.06.009
10.1016/j.enbuild.2010.11.002
10.1016/j.enbuild.2011.12.029
10.1080/10789669.2002.10391290
10.1016/j.enbuild.2012.08.038
10.1016/j.buildenv.2006.10.027
10.1016/S0378-7788(01)00137-2
10.1016/S0378-7788(01)00092-5
10.1016/j.apenergy.2003.12.006
10.1016/j.apenergy.2012.10.031
10.1016/j.apenergy.2010.11.022
10.1080/19401490903414454
10.1016/j.simpat.2010.11.002
10.1016/S0360-1323(00)00026-3
10.1016/j.rser.2008.03.006
10.1016/j.enbuild.2005.11.005
10.1016/j.enbuild.2004.09.009
10.1016/j.apenergy.2013.08.093
10.1016/j.enbuild.2010.04.006
10.1080/0961321042000325327
10.1016/j.apenergy.2008.11.035
10.1016/j.enbuild.2012.06.024
ContentType Journal Article
Copyright 2014 Elsevier Ltd
2015 INIST-CNRS
Copyright_xml – notice: 2014 Elsevier Ltd
– notice: 2015 INIST-CNRS
DBID AAYXX
CITATION
IQODW
7ST
7U6
C1K
SOI
7S9
L.6
DOI 10.1016/j.apenergy.2014.03.020
DatabaseName CrossRef
Pascal-Francis
Environment Abstracts
Sustainability Science Abstracts
Environmental Sciences and Pollution Management
Environment Abstracts
AGRICOLA
AGRICOLA - Academic
DatabaseTitle CrossRef
Environment Abstracts
Sustainability Science Abstracts
Environmental Sciences and Pollution Management
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList AGRICOLA

Environment Abstracts
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Environmental Sciences
Applied Sciences
EISSN 1872-9118
EndPage 334
ExternalDocumentID 28475777
10_1016_j_apenergy_2014_03_020
S0306261914002505
GroupedDBID --K
--M
.~1
0R~
1B1
1~.
1~5
23M
4.4
457
4G.
5GY
5VS
7-5
71M
8P~
9JN
AABNK
AACTN
AAEDT
AAEDW
AAHCO
AAIAV
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AARJD
AAXUO
ABJNI
ABMAC
ABXDB
ABYKQ
ACDAQ
ACGFS
ACRLP
ADBBV
ADEZE
ADTZH
AEBSH
AECPX
AEKER
AENEX
AFKWA
AFTJW
AGHFR
AGUBO
AGYEJ
AHHHB
AHIDL
AHJVU
AIEXJ
AIKHN
AITUG
AJBFU
AJOXV
ALMA_UNASSIGNED_HOLDINGS
AMFUW
AMRAJ
AXJTR
BELTK
BJAXD
BKOJK
BLXMC
CS3
EBS
EFJIC
EFLBG
EJD
EO8
EO9
EP2
EP3
FDB
FIRID
FNPLU
FYGXN
G-Q
GBLVA
IHE
J1W
JARJE
JJJVA
KOM
LY6
M41
MO0
N9A
O-L
O9-
OAUVE
OZT
P-8
P-9
P2P
PC.
Q38
RIG
ROL
RPZ
SDF
SDG
SES
SPC
SPCBC
SSR
SST
SSZ
T5K
TN5
~02
~G-
AAHBH
AAQXK
AATTM
AAXKI
AAYOK
AAYWO
AAYXX
ABEFU
ABFNM
ABWVN
ACNNM
ACRPL
ACVFH
ADCNI
ADMUD
ADNMO
AEIPS
AEUPX
AFJKZ
AFPUW
AFXIZ
AGCQF
AGQPQ
AGRNS
AIGII
AIIUN
AKBMS
AKRWK
AKYEP
ANKPU
APXCP
ASPBG
AVWKF
AZFZN
BNPGV
CITATION
FEDTE
FGOYB
G-2
HVGLF
HZ~
R2-
SAC
SEW
SSH
WUQ
ZY4
ABTAH
IQODW
7ST
7U6
C1K
SOI
7S9
L.6
ID FETCH-LOGICAL-c408t-1f90291fa841a636a69c459bc73884757da04e22f7c7beff15f6c6e73ba3bd423
IEDL.DBID .~1
ISSN 0306-2619
IngestDate Thu Jul 10 17:45:23 EDT 2025
Fri Jul 11 06:38:42 EDT 2025
Wed Apr 02 07:25:22 EDT 2025
Tue Jul 01 03:05:23 EDT 2025
Thu Apr 24 23:02:03 EDT 2025
Fri Feb 23 02:36:56 EST 2024
IsPeerReviewed true
IsScholarly true
Keywords Building
Overview
Energy benchmarking
Energy consumption
Performance
Buildings
Language English
License CC BY 4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c408t-1f90291fa841a636a69c459bc73884757da04e22f7c7beff15f6c6e73ba3bd423
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0001-5941-5874
PQID 1540238469
PQPubID 23462
PageCount 10
ParticipantIDs proquest_miscellaneous_2101337997
proquest_miscellaneous_1540238469
pascalfrancis_primary_28475777
crossref_citationtrail_10_1016_j_apenergy_2014_03_020
crossref_primary_10_1016_j_apenergy_2014_03_020
elsevier_sciencedirect_doi_10_1016_j_apenergy_2014_03_020
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2014-07-01
PublicationDateYYYYMMDD 2014-07-01
PublicationDate_xml – month: 07
  year: 2014
  text: 2014-07-01
  day: 01
PublicationDecade 2010
PublicationPlace Kidlington
PublicationPlace_xml – name: Kidlington
PublicationTitle Applied energy
PublicationYear 2014
Publisher Elsevier Ltd
Elsevier
Publisher_xml – name: Elsevier Ltd
– name: Elsevier
References Li, Meng, Cai (b0135) 2009; 86
Curtin JM. The development and testing of an automated building commissioning analysis tool (ABCAT), Master thesis, Texas A&M University, College Station, U.S.; 2007.
Li, Meng, Cai (b0140) 2009; 50
Lee, Braun (b0210) 2008; 43
Saltelli, Chan, Scott (b0330) 2000
Papalexopoulos, Hesterberg (b0115) 1990; 5
Chung (b0060) 2011; 88
Katipamula, Pratt, Chassin (b0095) 1999; 105
Claridge DE, Culp CH, Liu M, et al. Campus-wide continuous commissioning of university buildings, ACEEE 2000 Summer Study on Energy Efficiency in Buildings, Pacific Grove, U.S., August 20–25, 2000.
Reddy, Maor, Panjapornpon (b0280) 2007; 13
Santos J, Brightbill L. Automated diagnostics from DDC data – PACRAT. In: Proceedings of the 8th National conference on building commissioning, Kansas City, U.S., May 3–5, 2000.
2013.
Wang, Greenberg, Flegel (b0270) 2013; 102
Lin, Hong (b0265) 2013; 111
O’Neill Z, Shashanka M, Pang X et al. Real time model-based energy diagnostics in buildings. In: 12th IBPSA Conference, Sydney, Australia, November 14–16, 2011.
Raftery, Keane, O’Nonnell (b0285) 2011; 43
Heo, Choudhary, Augenbroe (b0075) 2012; 47
Holcomb, Li, Seshia (b0305) 2009
Li X, Lu J, Ding L et al. Building cooling load forecasting model based on LS-SVM. In: Asia-Pacific conference on information processing, Shenzhen, China, July 18–19, 2009.
Wang, Xu (b0200) 2006; 45
Lee SH, Zhao F, Augenbroe G. The use of normative energy calculation beyond building performance rating systems. In: 12th IBPSA Conference, Sydney, Australia, November 14–16, 2011.
Fumo, Mago, Luck (b0260) 2010; 42
Djuric, Novakovic (b0030) 2009; 13
Mui, Wong, Law (b0085) 2007; 84
Augenbroe, Park (b0070) 2005; 33
Mihalakakou, Santamouris, Tsangrassoulis (b0170) 2002; 34
CEN, EN 15241:2007, Ventilation for buildings. Calculation methods for energy losses due to ventilation and infiltration in buildings; 2007.
ISO, ISO 13789:2007, Thermal performance of buildings – Transmission and ventilation heat transfer coefficients – calculation method; 2007.
Li, Huang (b0120) 2013; 62
Bynum, Claridge, Curtin (b0240) 2012; 55
Jacob, Dietz, Komhard (b0125) 2010; 3
Yiu, Wang (b0130) 2007; 48
Braun, Chaturvedi (b0205) 2002; 8
Lv, Lu, Kibert (b0230) 2014; 114
Knebel DE. Simplified energy analysis using the modified bin method – prepared for the American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, Atlanta, GA; 1983.
Crawley, Lawrie, Pedersen (b0040) 2000; 42
Tso, Yau (b0185) 2007; 32
ASHRAE, ASHRAE Guideline 14: measurement of energy and demand savings, American Society of Heating, Refrigerating, and Air-conditioning Engineers Inc., Atlanta, GA, 2002.
Friedman H, Piette MA. Comparative guide to emerging diagnostic tools for large commercial HVAC systems, Lawrence Berkeley National Laboratory Report (No. 48629); 2001.
Federal Energy Management Program (FEMP). Continuous Commissioning Guidebook for Federal Managers.
Federspiel, Zhang, Arens (b0080) 2002; 34
Roth, Llana, Westphalen (b0055) 2005; 47
Chidiac, Catania, Morofsky (b0255) 2011; 43
Magoules, Zhao, Elizondo (b0180) 2013; 62
Philp Haves. Fault modelling in component-based HVAC simulation. In: Fifth international IBPSA conference, Prague, Czech, September 8–10, 1997.
Crawley, Hand, Kummert (b0090) 2008; 43
Torrens, Keane, Costa (b0250) 2011; 19
IEA, Key world energy statistics; 2009.
Zhao, Magoules (b0065) 2012; 16
Manfren, Aste, Moshksar (b0160) 2013; 103
Yu, Haghighat, Fung (b0190) 2010; 42
Aydinalp, Ugursal, Fung (b0165) 2004; 79
Heo, Zavala (b0155) 2012; 53
Tarlow D, Peterman A, Schwegler BR, et al. Automatically calibrating a probabilistic graphical model of building energy consumption. In: 11th IBPSA Conference, Glasgow, Scotland, July 27–30, 2009.
Al-Homoud (b0050) 2001; 36
Facility dynamics.
Dong, Cao, Lee (b0150) 2005; 37
Karatasou, Santamouris, Geros (b0175) 2006; 38
Xiao, Wang (b0015) 2009; 13
Mui (10.1016/j.apenergy.2014.03.020_b0085) 2007; 84
Li (10.1016/j.apenergy.2014.03.020_b0140) 2009; 50
Li (10.1016/j.apenergy.2014.03.020_b0120) 2013; 62
Heo (10.1016/j.apenergy.2014.03.020_b0075) 2012; 47
10.1016/j.apenergy.2014.03.020_b0320
Fumo (10.1016/j.apenergy.2014.03.020_b0260) 2010; 42
Zhao (10.1016/j.apenergy.2014.03.020_b0065) 2012; 16
10.1016/j.apenergy.2014.03.020_b0315
Raftery (10.1016/j.apenergy.2014.03.020_b0285) 2011; 43
Dong (10.1016/j.apenergy.2014.03.020_b0150) 2005; 37
Reddy (10.1016/j.apenergy.2014.03.020_b0280) 2007; 13
Yiu (10.1016/j.apenergy.2014.03.020_b0130) 2007; 48
Karatasou (10.1016/j.apenergy.2014.03.020_b0175) 2006; 38
Chung (10.1016/j.apenergy.2014.03.020_b0060) 2011; 88
Lee (10.1016/j.apenergy.2014.03.020_b0210) 2008; 43
Crawley (10.1016/j.apenergy.2014.03.020_b0090) 2008; 43
Braun (10.1016/j.apenergy.2014.03.020_b0205) 2002; 8
Saltelli (10.1016/j.apenergy.2014.03.020_b0330) 2000
Wang (10.1016/j.apenergy.2014.03.020_b0270) 2013; 102
10.1016/j.apenergy.2014.03.020_b0310
10.1016/j.apenergy.2014.03.020_b0110
Djuric (10.1016/j.apenergy.2014.03.020_b0030) 2009; 13
10.1016/j.apenergy.2014.03.020_b0105
Xiao (10.1016/j.apenergy.2014.03.020_b0015) 2009; 13
10.1016/j.apenergy.2014.03.020_b0225
Federspiel (10.1016/j.apenergy.2014.03.020_b0080) 2002; 34
Roth (10.1016/j.apenergy.2014.03.020_b0055) 2005; 47
Jacob (10.1016/j.apenergy.2014.03.020_b0125) 2010; 3
Katipamula (10.1016/j.apenergy.2014.03.020_b0095) 1999; 105
Chidiac (10.1016/j.apenergy.2014.03.020_b0255) 2011; 43
Augenbroe (10.1016/j.apenergy.2014.03.020_b0070) 2005; 33
Li (10.1016/j.apenergy.2014.03.020_b0135) 2009; 86
Bynum (10.1016/j.apenergy.2014.03.020_b0240) 2012; 55
10.1016/j.apenergy.2014.03.020_b0145
Heo (10.1016/j.apenergy.2014.03.020_b0155) 2012; 53
10.1016/j.apenergy.2014.03.020_b0025
10.1016/j.apenergy.2014.03.020_b0300
10.1016/j.apenergy.2014.03.020_b0220
10.1016/j.apenergy.2014.03.020_b0100
Lin (10.1016/j.apenergy.2014.03.020_b0265) 2013; 111
Yu (10.1016/j.apenergy.2014.03.020_b0190) 2010; 42
Tso (10.1016/j.apenergy.2014.03.020_b0185) 2007; 32
Manfren (10.1016/j.apenergy.2014.03.020_b0160) 2013; 103
Wang (10.1016/j.apenergy.2014.03.020_b0200) 2006; 45
10.1016/j.apenergy.2014.03.020_b0290
10.1016/j.apenergy.2014.03.020_b0295
10.1016/j.apenergy.2014.03.020_b0010
Al-Homoud (10.1016/j.apenergy.2014.03.020_b0050) 2001; 36
10.1016/j.apenergy.2014.03.020_b0325
10.1016/j.apenergy.2014.03.020_b0005
Holcomb (10.1016/j.apenergy.2014.03.020_b0305) 2009
Aydinalp (10.1016/j.apenergy.2014.03.020_b0165) 2004; 79
Torrens (10.1016/j.apenergy.2014.03.020_b0250) 2011; 19
Magoules (10.1016/j.apenergy.2014.03.020_b0180) 2013; 62
Lv (10.1016/j.apenergy.2014.03.020_b0230) 2014; 114
Mihalakakou (10.1016/j.apenergy.2014.03.020_b0170) 2002; 34
Crawley (10.1016/j.apenergy.2014.03.020_b0040) 2000; 42
Papalexopoulos (10.1016/j.apenergy.2014.03.020_b0115) 1990; 5
References_xml – reference: IEA, Key world energy statistics; 2009.
– volume: 47
  start-page: 550
  year: 2012
  end-page: 560
  ident: b0075
  article-title: Calibration of building energy models for retrofit analysis under uncertainty
  publication-title: Energy Build
– volume: 5
  start-page: 1535
  year: 1990
  end-page: 1547
  ident: b0115
  article-title: A regression-based approach to short-term system load forecasting
  publication-title: IEEE Trans Power Syst
– reference: Facility dynamics. <
– volume: 53
  start-page: 7
  year: 2012
  end-page: 18
  ident: b0155
  article-title: Gaussian process modelling for measurement and verification of building energy savings
  publication-title: Energy Build
– reference: Curtin JM. The development and testing of an automated building commissioning analysis tool (ABCAT), Master thesis, Texas A&M University, College Station, U.S.; 2007.
– reference: ASHRAE, ASHRAE Guideline 14: measurement of energy and demand savings, American Society of Heating, Refrigerating, and Air-conditioning Engineers Inc., Atlanta, GA, 2002.
– reference: Philp Haves. Fault modelling in component-based HVAC simulation. In: Fifth international IBPSA conference, Prague, Czech, September 8–10, 1997.
– volume: 102
  start-page: 1382
  year: 2013
  end-page: 1390
  ident: b0270
  article-title: Monitoring-based HVAC commissioning of an existing office building for energy efficiency
  publication-title: Appl Energy
– volume: 33
  start-page: 159
  year: 2005
  end-page: 172
  ident: b0070
  article-title: Quantification methods of technical building performance
  publication-title: Build Res Inf
– reference: Friedman H, Piette MA. Comparative guide to emerging diagnostic tools for large commercial HVAC systems, Lawrence Berkeley National Laboratory Report (No. 48629); 2001.
– reference: Tarlow D, Peterman A, Schwegler BR, et al. Automatically calibrating a probabilistic graphical model of building energy consumption. In: 11th IBPSA Conference, Glasgow, Scotland, July 27–30, 2009.
– volume: 105
  start-page: 555
  year: 1999
  end-page: 567
  ident: b0095
  article-title: Automated fault detection and diagnostics for outdoor-air ventilation systems and economizers: methodology and results from field testing
  publication-title: ASHRAE Trans
– volume: 47
  start-page: 82
  year: 2005
  end-page: 84
  ident: b0055
  article-title: Automated whole building diagnostics
  publication-title: ASHRAE J
– volume: 114
  start-page: 91
  year: 2014
  end-page: 103
  ident: b0230
  article-title: A novel dynamic modelling approach for predicting building energy performance
  publication-title: Appl Energy
– volume: 45
  start-page: 419
  year: 2006
  end-page: 432
  ident: b0200
  article-title: Simplified building model for transient thermal performances estimation using GA-based parameter identification
  publication-title: Int J Therm Sci
– volume: 13
  start-page: 1144
  year: 2009
  end-page: 1149
  ident: b0015
  article-title: Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability
  publication-title: Renew Sustain Energy Rev
– volume: 37
  start-page: 545
  year: 2005
  end-page: 553
  ident: b0150
  article-title: Applying support vector machines to predict building energy consumption in tropical region
  publication-title: Energy Build
– volume: 34
  start-page: 727
  year: 2002
  end-page: 736
  ident: b0170
  article-title: On the energy consumption in residential buildings
  publication-title: Energy Build
– volume: 88
  start-page: 1470
  year: 2011
  end-page: 1479
  ident: b0060
  article-title: Review of building energy-use performance benchmarking methodologies
  publication-title: Appl Energy
– volume: 16
  start-page: 3586
  year: 2012
  end-page: 3592
  ident: b0065
  article-title: A review on the prediction of building energy consumption
  publication-title: Renew Sustain Energy Rev
– reference: >; 2013.
– volume: 34
  start-page: 203
  year: 2002
  end-page: 214
  ident: b0080
  article-title: Model-based benchmarking with application to laboratory buildings
  publication-title: Energy Build
– volume: 13
  start-page: 486
  year: 2009
  end-page: 492
  ident: b0030
  article-title: Review of possibilities and necessities for building lifetime commissioning
  publication-title: Renew Sustain Energy Rev
– volume: 62
  start-page: 442
  year: 2013
  end-page: 449
  ident: b0120
  article-title: Re-evaluation of building cooling load prediction models for use in humid subtropical area
  publication-title: Energy Build
– volume: 3
  start-page: 53
  year: 2010
  end-page: 62
  ident: b0125
  article-title: Black-box models for fault detection and performance monitoring of buildings
  publication-title: J Build Perform Simul
– year: 2000
  ident: b0330
  article-title: Sensitivity analysis
– volume: 43
  start-page: 2356
  year: 2011
  end-page: 2364
  ident: b0285
  article-title: Calibrating whole building energy models: an evidence-based methodology
  publication-title: Energy Build
– volume: 43
  start-page: 661
  year: 2008
  end-page: 673
  ident: b0090
  article-title: Contrasting the capabilities of building energy performance simulation programs
  publication-title: Build Environ
– volume: 103
  start-page: 627
  year: 2013
  end-page: 641
  ident: b0160
  article-title: Calibration and uncertainty analysis for computer models – a meta-model based approach for integrated building energy simulation
  publication-title: Appl Energy
– volume: 79
  start-page: 159
  year: 2004
  end-page: 178
  ident: b0165
  article-title: Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks
  publication-title: Appl Energy
– volume: 43
  start-page: 1755
  year: 2008
  end-page: 1768
  ident: b0210
  article-title: Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements
  publication-title: Build Environ
– volume: 42
  start-page: 1637
  year: 2010
  end-page: 1646
  ident: b0190
  article-title: A decision tree method for building energy demand modelling
  publication-title: Energy Build
– reference: > ; 2013.
– volume: 19
  start-page: 1258
  year: 2011
  end-page: 1265
  ident: b0250
  article-title: Multi-criteria optimisation using past, real time and predictive performance benchmarks
  publication-title: Simul Model Pract Theory
– year: 2009
  ident: b0305
  article-title: Algorithms for green buildings: learning-based techniques for energy prediction and fault diagnosis, UCB/EECS-2009-138
– reference: Knebel DE. Simplified energy analysis using the modified bin method – prepared for the American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, Atlanta, GA; 1983.
– reference: O’Neill Z, Shashanka M, Pang X et al. Real time model-based energy diagnostics in buildings. In: 12th IBPSA Conference, Sydney, Australia, November 14–16, 2011.
– volume: 36
  start-page: 421
  year: 2001
  end-page: 433
  ident: b0050
  article-title: Computer-aided building energy analysis techniques
  publication-title: Build Environ
– volume: 62
  start-page: 133
  year: 2013
  end-page: 138
  ident: b0180
  article-title: Development of an RDP neural network for building energy consumption fault detection and diagnosis
  publication-title: Energy Build
– volume: 84
  start-page: 89
  year: 2007
  end-page: 98
  ident: b0085
  article-title: An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates
  publication-title: Appl Energy
– volume: 48
  start-page: 2276
  year: 2007
  end-page: 2285
  ident: b0130
  article-title: Multiple ARMAX modelling scheme for forecasting air conditioning system performance
  publication-title: Energy Convers Manage
– reference: Santos J, Brightbill L. Automated diagnostics from DDC data – PACRAT. In: Proceedings of the 8th National conference on building commissioning, Kansas City, U.S., May 3–5, 2000.
– volume: 13
  start-page: 221
  year: 2007
  end-page: 241
  ident: b0280
  article-title: Calibrating detailed building energy simulation programs with measured data – part I: general methodology (RP-1051)
  publication-title: HVAC&R Res
– reference: Federal Energy Management Program (FEMP). Continuous Commissioning Guidebook for Federal Managers. <
– reference: ISO, ISO 13789:2007, Thermal performance of buildings – Transmission and ventilation heat transfer coefficients – calculation method; 2007.
– volume: 32
  start-page: 1761
  year: 2007
  end-page: 1768
  ident: b0185
  article-title: Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural network
  publication-title: Energy
– reference: CEN, EN 15241:2007, Ventilation for buildings. Calculation methods for energy losses due to ventilation and infiltration in buildings; 2007.
– volume: 50
  start-page: 90
  year: 2009
  end-page: 96
  ident: b0140
  article-title: Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks
  publication-title: Energy Convers Manage
– volume: 8
  start-page: 73
  year: 2002
  end-page: 99
  ident: b0205
  article-title: An inverse gray-box model for transient building load prediction
  publication-title: HVAC&R Res
– reference: Lee SH, Zhao F, Augenbroe G. The use of normative energy calculation beyond building performance rating systems. In: 12th IBPSA Conference, Sydney, Australia, November 14–16, 2011.
– volume: 43
  start-page: 614
  year: 2011
  end-page: 620
  ident: b0255
  article-title: A screening methodology for implementing cost effective energy retrofit measures in Canadian office buildings
  publication-title: Energy Build
– volume: 42
  start-page: 49
  year: 2000
  end-page: 56
  ident: b0040
  article-title: Energy plus: energy simulation program
  publication-title: ASHRAE J.
– volume: 111
  start-page: 515
  year: 2013
  end-page: 528
  ident: b0265
  article-title: On variations of space-heating energy use in office buildings
  publication-title: Appl Energy
– volume: 42
  start-page: 2331
  year: 2010
  end-page: 2337
  ident: b0260
  article-title: Methodology to estimate building energy consumption using EnergyPlus benchmark models
  publication-title: Energy Build
– volume: 86
  start-page: 2249
  year: 2009
  end-page: 2256
  ident: b0135
  article-title: Applying support vector machine to predict hourly cooling load in the building
  publication-title: Appl Energy
– volume: 55
  start-page: 607
  year: 2012
  end-page: 617
  ident: b0240
  article-title: Development and testing of an Automated Building Commissioning Analysis Tool (ABCAT)
  publication-title: Energy Build
– reference: Li X, Lu J, Ding L et al. Building cooling load forecasting model based on LS-SVM. In: Asia-Pacific conference on information processing, Shenzhen, China, July 18–19, 2009.
– reference: Claridge DE, Culp CH, Liu M, et al. Campus-wide continuous commissioning of university buildings, ACEEE 2000 Summer Study on Energy Efficiency in Buildings, Pacific Grove, U.S., August 20–25, 2000.
– volume: 38
  start-page: 949
  year: 2006
  end-page: 958
  ident: b0175
  article-title: Modeling and predicting building’s energy use with artificial neural networks: methods and results
  publication-title: Energy Build
– ident: 10.1016/j.apenergy.2014.03.020_b0225
– volume: 43
  start-page: 2356
  year: 2011
  ident: 10.1016/j.apenergy.2014.03.020_b0285
  article-title: Calibrating whole building energy models: an evidence-based methodology
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2011.05.020
– volume: 43
  start-page: 1755
  year: 2008
  ident: 10.1016/j.apenergy.2014.03.020_b0210
  article-title: Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements
  publication-title: Build Environ
  doi: 10.1016/j.buildenv.2007.11.004
– volume: 50
  start-page: 90
  year: 2009
  ident: 10.1016/j.apenergy.2014.03.020_b0140
  article-title: Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks
  publication-title: Energy Convers Manage
  doi: 10.1016/j.enconman.2008.08.033
– volume: 16
  start-page: 3586
  year: 2012
  ident: 10.1016/j.apenergy.2014.03.020_b0065
  article-title: A review on the prediction of building energy consumption
  publication-title: Renew Sustain Energy Rev
  doi: 10.1016/j.rser.2012.02.049
– ident: 10.1016/j.apenergy.2014.03.020_b0110
  doi: 10.2172/787155
– ident: 10.1016/j.apenergy.2014.03.020_b0320
– year: 2009
  ident: 10.1016/j.apenergy.2014.03.020_b0305
– volume: 32
  start-page: 1761
  year: 2007
  ident: 10.1016/j.apenergy.2014.03.020_b0185
  article-title: Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural network
  publication-title: Energy
  doi: 10.1016/j.energy.2006.11.010
– volume: 13
  start-page: 221
  year: 2007
  ident: 10.1016/j.apenergy.2014.03.020_b0280
  article-title: Calibrating detailed building energy simulation programs with measured data – part I: general methodology (RP-1051)
  publication-title: HVAC&R Res
  doi: 10.1080/10789669.2007.10390952
– year: 2000
  ident: 10.1016/j.apenergy.2014.03.020_b0330
– volume: 62
  start-page: 133
  year: 2013
  ident: 10.1016/j.apenergy.2014.03.020_b0180
  article-title: Development of an RDP neural network for building energy consumption fault detection and diagnosis
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2013.02.050
– volume: 102
  start-page: 1382
  year: 2013
  ident: 10.1016/j.apenergy.2014.03.020_b0270
  article-title: Monitoring-based HVAC commissioning of an existing office building for energy efficiency
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2012.09.005
– ident: 10.1016/j.apenergy.2014.03.020_b0145
– volume: 111
  start-page: 515
  year: 2013
  ident: 10.1016/j.apenergy.2014.03.020_b0265
  article-title: On variations of space-heating energy use in office buildings
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2013.05.040
– ident: 10.1016/j.apenergy.2014.03.020_b0315
– volume: 84
  start-page: 89
  year: 2007
  ident: 10.1016/j.apenergy.2014.03.020_b0085
  article-title: An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2006.04.002
– volume: 48
  start-page: 2276
  year: 2007
  ident: 10.1016/j.apenergy.2014.03.020_b0130
  article-title: Multiple ARMAX modelling scheme for forecasting air conditioning system performance
  publication-title: Energy Convers Manage
  doi: 10.1016/j.enconman.2007.03.018
– volume: 42
  start-page: 2331
  year: 2010
  ident: 10.1016/j.apenergy.2014.03.020_b0260
  article-title: Methodology to estimate building energy consumption using EnergyPlus benchmark models
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2010.07.027
– volume: 62
  start-page: 442
  year: 2013
  ident: 10.1016/j.apenergy.2014.03.020_b0120
  article-title: Re-evaluation of building cooling load prediction models for use in humid subtropical area
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2013.03.035
– volume: 5
  start-page: 1535
  year: 1990
  ident: 10.1016/j.apenergy.2014.03.020_b0115
  article-title: A regression-based approach to short-term system load forecasting
  publication-title: IEEE Trans Power Syst
  doi: 10.1109/59.99410
– volume: 13
  start-page: 486
  year: 2009
  ident: 10.1016/j.apenergy.2014.03.020_b0030
  article-title: Review of possibilities and necessities for building lifetime commissioning
  publication-title: Renew Sustain Energy Rev
  doi: 10.1016/j.rser.2007.11.007
– ident: 10.1016/j.apenergy.2014.03.020_b0100
– volume: 45
  start-page: 419
  year: 2006
  ident: 10.1016/j.apenergy.2014.03.020_b0200
  article-title: Simplified building model for transient thermal performances estimation using GA-based parameter identification
  publication-title: Int J Therm Sci
  doi: 10.1016/j.ijthermalsci.2005.06.009
– volume: 43
  start-page: 614
  year: 2011
  ident: 10.1016/j.apenergy.2014.03.020_b0255
  article-title: A screening methodology for implementing cost effective energy retrofit measures in Canadian office buildings
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2010.11.002
– volume: 47
  start-page: 82
  year: 2005
  ident: 10.1016/j.apenergy.2014.03.020_b0055
  article-title: Automated whole building diagnostics
  publication-title: ASHRAE J
– volume: 47
  start-page: 550
  year: 2012
  ident: 10.1016/j.apenergy.2014.03.020_b0075
  article-title: Calibration of building energy models for retrofit analysis under uncertainty
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2011.12.029
– volume: 8
  start-page: 73
  year: 2002
  ident: 10.1016/j.apenergy.2014.03.020_b0205
  article-title: An inverse gray-box model for transient building load prediction
  publication-title: HVAC&R Res
  doi: 10.1080/10789669.2002.10391290
– ident: 10.1016/j.apenergy.2014.03.020_b0220
– volume: 55
  start-page: 607
  year: 2012
  ident: 10.1016/j.apenergy.2014.03.020_b0240
  article-title: Development and testing of an Automated Building Commissioning Analysis Tool (ABCAT)
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2012.08.038
– ident: 10.1016/j.apenergy.2014.03.020_b0005
– volume: 43
  start-page: 661
  year: 2008
  ident: 10.1016/j.apenergy.2014.03.020_b0090
  article-title: Contrasting the capabilities of building energy performance simulation programs
  publication-title: Build Environ
  doi: 10.1016/j.buildenv.2006.10.027
– volume: 34
  start-page: 727
  year: 2002
  ident: 10.1016/j.apenergy.2014.03.020_b0170
  article-title: On the energy consumption in residential buildings
  publication-title: Energy Build
  doi: 10.1016/S0378-7788(01)00137-2
– ident: 10.1016/j.apenergy.2014.03.020_b0290
– volume: 105
  start-page: 555
  year: 1999
  ident: 10.1016/j.apenergy.2014.03.020_b0095
  article-title: Automated fault detection and diagnostics for outdoor-air ventilation systems and economizers: methodology and results from field testing
  publication-title: ASHRAE Trans
– volume: 34
  start-page: 203
  year: 2002
  ident: 10.1016/j.apenergy.2014.03.020_b0080
  article-title: Model-based benchmarking with application to laboratory buildings
  publication-title: Energy Build
  doi: 10.1016/S0378-7788(01)00092-5
– ident: 10.1016/j.apenergy.2014.03.020_b0105
– volume: 79
  start-page: 159
  year: 2004
  ident: 10.1016/j.apenergy.2014.03.020_b0165
  article-title: Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2003.12.006
– ident: 10.1016/j.apenergy.2014.03.020_b0295
– volume: 103
  start-page: 627
  year: 2013
  ident: 10.1016/j.apenergy.2014.03.020_b0160
  article-title: Calibration and uncertainty analysis for computer models – a meta-model based approach for integrated building energy simulation
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2012.10.031
– volume: 88
  start-page: 1470
  year: 2011
  ident: 10.1016/j.apenergy.2014.03.020_b0060
  article-title: Review of building energy-use performance benchmarking methodologies
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2010.11.022
– volume: 3
  start-page: 53
  year: 2010
  ident: 10.1016/j.apenergy.2014.03.020_b0125
  article-title: Black-box models for fault detection and performance monitoring of buildings
  publication-title: J Build Perform Simul
  doi: 10.1080/19401490903414454
– ident: 10.1016/j.apenergy.2014.03.020_b0010
– ident: 10.1016/j.apenergy.2014.03.020_b0025
– ident: 10.1016/j.apenergy.2014.03.020_b0300
– volume: 19
  start-page: 1258
  year: 2011
  ident: 10.1016/j.apenergy.2014.03.020_b0250
  article-title: Multi-criteria optimisation using past, real time and predictive performance benchmarks
  publication-title: Simul Model Pract Theory
  doi: 10.1016/j.simpat.2010.11.002
– volume: 36
  start-page: 421
  year: 2001
  ident: 10.1016/j.apenergy.2014.03.020_b0050
  article-title: Computer-aided building energy analysis techniques
  publication-title: Build Environ
  doi: 10.1016/S0360-1323(00)00026-3
– ident: 10.1016/j.apenergy.2014.03.020_b0325
– volume: 13
  start-page: 1144
  year: 2009
  ident: 10.1016/j.apenergy.2014.03.020_b0015
  article-title: Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability
  publication-title: Renew Sustain Energy Rev
  doi: 10.1016/j.rser.2008.03.006
– volume: 42
  start-page: 49
  year: 2000
  ident: 10.1016/j.apenergy.2014.03.020_b0040
  article-title: Energy plus: energy simulation program
  publication-title: ASHRAE J.
– volume: 38
  start-page: 949
  year: 2006
  ident: 10.1016/j.apenergy.2014.03.020_b0175
  article-title: Modeling and predicting building’s energy use with artificial neural networks: methods and results
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2005.11.005
– volume: 37
  start-page: 545
  year: 2005
  ident: 10.1016/j.apenergy.2014.03.020_b0150
  article-title: Applying support vector machines to predict building energy consumption in tropical region
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2004.09.009
– volume: 114
  start-page: 91
  year: 2014
  ident: 10.1016/j.apenergy.2014.03.020_b0230
  article-title: A novel dynamic modelling approach for predicting building energy performance
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2013.08.093
– ident: 10.1016/j.apenergy.2014.03.020_b0310
– volume: 42
  start-page: 1637
  year: 2010
  ident: 10.1016/j.apenergy.2014.03.020_b0190
  article-title: A decision tree method for building energy demand modelling
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2010.04.006
– volume: 33
  start-page: 159
  year: 2005
  ident: 10.1016/j.apenergy.2014.03.020_b0070
  article-title: Quantification methods of technical building performance
  publication-title: Build Res Inf
  doi: 10.1080/0961321042000325327
– volume: 86
  start-page: 2249
  year: 2009
  ident: 10.1016/j.apenergy.2014.03.020_b0135
  article-title: Applying support vector machine to predict hourly cooling load in the building
  publication-title: Appl Energy
  doi: 10.1016/j.apenergy.2008.11.035
– volume: 53
  start-page: 7
  year: 2012
  ident: 10.1016/j.apenergy.2014.03.020_b0155
  article-title: Gaussian process modelling for measurement and verification of building energy savings
  publication-title: Energy Build
  doi: 10.1016/j.enbuild.2012.06.024
SSID ssj0002120
Score 2.5009859
SecondaryResourceType review_article
Snippet •This paper reviews up to date methods for building energy benchmarking.•This paper summarizes the major characteristics of these methods.•This paper...
Building sector consumes a significant portion of energy worldwide. One of the reasons is that the performance of building and its components degrades over the...
SourceID proquest
pascalfrancis
crossref
elsevier
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 325
SubjectTerms Applied sciences
Building
Energy
Energy benchmarking
energy conservation
Exact sciences and technology
monitoring
Overview
Title Methods for benchmarking building energy consumption against its past or intended performance: An overview
URI https://dx.doi.org/10.1016/j.apenergy.2014.03.020
https://www.proquest.com/docview/1540238469
https://www.proquest.com/docview/2101337997
Volume 124
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELaqcgEhBIWK5bEyEtd0E9uJY26rqtUCai9QqTdrnMSwq5JGJL32t3fGcbqtAPXAMZZHGXk8M5_teTD2EQoHpnaQQO19ogyeWcEZkTjUPOcFgCspd_jktFidqS_n-fkOO5xyYSisMtr-0aYHax1HFnE1F916vfhGaJfwPx4RgiOnDHalaZcfXG_DPEQszYiTE5p9J0t4cwBdEzLsKMRLhWKn1Pf77w7qaQc9Lpsf-138YbqDPzp-zp5FIMmXI68v2E7T7rEnd8oL7rH9o20WG06Naty_ZJuT0De654hYucPBn78g3JlzF7tk85FjXoUMzWBWOPyANYJJvh56jlwOHImp2gTdofNum4DwiS9bTpGh9Orwip0dH30_XCWx6UJSqbQcksybVJjMQ6kyKGQBhalUblylZYmeLNc1pKoRwutKu8b7LPdFVTRaOpCuRnC2z3bby7Z5zXhqfFl6PIHpChRI4UQNlc9LAGW89GLG8mmlbRUrklNjjAs7hZ5t7CQhSxKyqbQooRlb3NJ1Y02OBynMJEh7b3dZdBwP0s7vSf72lyIsh9Yz9mHaChZ1kx5coG0ur3qL8JQgkSrMv-fgkTuTUhuj3_wHk2_ZY_oa44jfsd3h91XzHtHS4OZBHebs0fLz19XpDUJnGZE
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTxUxEG8QD0qIUZT4QLEmXpe323a3W26EQJ7K4yIk3Jppd4vvRZcNu1z92512uzyIGg5eu53spNOZ-bWdD0I-QWFAVQYSqJxLhMIzKxjFEoOaZxwDMKXPHZ6fFbML8eUyv1wjR2MujA-rjLZ_sOnBWseRaVzNabtYTL95tOvxPx4RgiN_Qp4KVF_fxmD_1yrOg8XajDg78dPvpQkv96GtQ4qdj_ESodqpb_z9dw-12UKH6-aGhhd_2O7gkE5ekhcRSdLDgdlXZK1utsjGvfqCW2T7eJXGhlOjHnevyXIeGkd3FCErNTj4_SeES3NqYptsOnBMbUjRDHaFwhUsEE3SRd9R5LKnSOzLTfhLdNquMhAO6GFDfWiof3Z4Qy5Ojs-PZknsupBYkZZ9kjmVMpU5KEUGBS-gUFbkyljJS3RluawgFTVjTlppauey3BW2qCU3wE2F6GybrDfXTf2W0FS5snR4BJMWBHBmWAXW5SWAUI47NiH5uNLaxpLkvjPGDz3Gni31KCHtJaRTrlFCEzK9o2uHohyPUqhRkPrB9tLoOR6l3Xsg-btfsrAcUk7Ix3EraFRO_-ICTX1922nEpx4TiUL9ew6euTPOpVJy5z-Y_ECezc7np_r089nXXfLcfxmCit-R9f7mtn6P0Kk3e0E1fgN__xsf
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=Methods+for+benchmarking+building+energy+consumption+against+its+past+or+intended+performance%3A+An+overview&rft.jtitle=Applied+energy&rft.au=Li%2C+Zhengwei&rft.au=Han%2C+Yanmin&rft.au=Xu%2C+Peng&rft.date=2014-07-01&rft.pub=Elsevier+Ltd&rft.issn=0306-2619&rft.eissn=1872-9118&rft.volume=124&rft.spage=325&rft.epage=334&rft_id=info:doi/10.1016%2Fj.apenergy.2014.03.020&rft.externalDocID=S0306261914002505
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-2619&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-2619&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-2619&client=summon