An ensemble learning framework for anomaly detection in building energy consumption

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate ene...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Energy and buildings Ročník 144; s. 191 - 206
Hlavní autoři:	Araya, Daniel B., Grolinger, Katarina, ElYamany, Hany F., Capretz, Miriam A.M., Bitsuamlak, Girma
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Lausanne Elsevier B.V 01.06.2017 Elsevier BV
Témata:	Alarm systems Anomalies Anomaly detection Autoencoder Building automation Building energy consumption Classifiers Consumption patterns Energy conservation Energy consumption Energy efficiency Energy usage Ensemble learning Fault diagnosis Faults Random forest Sliding Smart buildings Support vector regression Canada Brampton Ontario Canada Support vector regression Random forest Building energy consumption Ensemble learning Autoencoder Anomaly detection
ISSN:	0378-7788, 1872-6178
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers using majority voting. To ensure diversity of anomaly classifiers, the EAD is implemented by combining pattern-based (e.g., CCAD-SW) and prediction-based anomaly classifiers. The research was evaluated using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada. Results show that the EAD framework improved the sensitivity of the CCAD-SW by 3.6% and reduced false alarm rate by 2.7%.
AbstractList	During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers using majority voting. To ensure diversity of anomaly classifiers, the EAD is implemented by combining pattern-based (e.g., CCAD-SW) and prediction-based anomaly classifiers. The research was evaluated using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada. Results show that the EAD framework improved the sensitivity of the CCAD-SW by 3.6% and reduced false alarm rate by 2.7%.
Author	Bitsuamlak, Girma Grolinger, Katarina Capretz, Miriam A.M. Araya, Daniel B. ElYamany, Hany F.
Author_xml	– sequence: 1 givenname: Daniel B. surname: Araya fullname: Araya, Daniel B. email: dberhane@uwo.ca organization: Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada N6A 5B9 – sequence: 2 givenname: Katarina surname: Grolinger fullname: Grolinger, Katarina email: kgroling@uwo.ca organization: Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada N6A 5B9 – sequence: 3 givenname: Hany F. surname: ElYamany fullname: ElYamany, Hany F. email: helyama@uwo.ca organization: Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada N6A 5B9 – sequence: 4 givenname: Miriam A.M. surname: Capretz fullname: Capretz, Miriam A.M. email: mcapretz@uwo.ca organization: Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada N6A 5B9 – sequence: 5 givenname: Girma surname: Bitsuamlak fullname: Bitsuamlak, Girma email: gbitsuam@uwo.ca organization: Department of Civil and Environmental Engineering, Western University, London, Ontario, Canada N6A 5B9
BookMark	eNqFkMtOwzAQRS1UJNrCJyBZYp1g52VHLFBV8ZIqsQDWlmNPKofELnYC6t-T0K7YdDWLuefO6CzQzDoLCF1TElNCi9smBlsNptVxQiiLSRKTnJ-hOeUsiQrK-AzNScp4xBjnF2gRQkMIKXJG5-htZTHYAF3VAm5BemvsFtdedvDj_CeuncfSuk62e6yhB9UbZ7Gx-O_glAULfrvHytkwdLtpfYnOa9kGuDrOJfp4fHhfP0eb16eX9WoTqZRnfUSVZFKVRVWVdclyILkGBiWrGMi61LnWNMuUThOWKuAZzZO0UFpzxVTBx0S6RDeH3p13XwOEXjRu8HY8KWiZJoTRgtExdXdIKe9C8FALZXo5_dl7aVpBiZgsikYcLYrJoiCJGC2OdP6P3nnTSb8_yd0fOBgFfBvwIigDVoE2fpQotDMnGn4Bfv6TtA
CitedBy_id	crossref_primary_10_1016_j_enbuild_2019_03_041 crossref_primary_10_1016_j_enbuild_2018_01_034 crossref_primary_10_1186_s42162_024_00351_1 crossref_primary_10_1016_j_enbuild_2018_04_008 crossref_primary_10_1016_j_rser_2025_115817 crossref_primary_10_1016_j_apenergy_2020_114877 crossref_primary_10_1016_j_buildenv_2020_107212 crossref_primary_10_1038_s41598_024_51849_3 crossref_primary_10_1016_j_enbuild_2020_110404 crossref_primary_10_1016_j_jclepro_2023_140040 crossref_primary_10_1088_1742_6596_2042_1_012018 crossref_primary_10_1016_j_enbuild_2020_109892 crossref_primary_10_1016_j_enbuild_2021_111618 crossref_primary_10_1016_j_jpdc_2020_05_004 crossref_primary_10_3389_fpls_2020_624273 crossref_primary_10_3389_fenrg_2022_861563 crossref_primary_10_1016_j_compeleceng_2021_107211 crossref_primary_10_1142_S0129156425403304 crossref_primary_10_1016_j_autcon_2020_103517 crossref_primary_10_1142_S0129156425402979 crossref_primary_10_1155_stc_7847074 crossref_primary_10_1007_s13369_021_05927_7 crossref_primary_10_1109_TRO_2023_3269380 crossref_primary_10_1016_j_tej_2017_08_005 crossref_primary_10_1016_j_ijar_2020_03_005 crossref_primary_10_1109_JIOT_2022_3144127 crossref_primary_10_1016_j_suscom_2020_100427 crossref_primary_10_1109_ACCESS_2020_2975738 crossref_primary_10_1016_j_future_2022_03_001 crossref_primary_10_1016_j_jobe_2021_102179 crossref_primary_10_1016_j_apenergy_2024_123228 crossref_primary_10_1109_ACCESS_2020_3042342 crossref_primary_10_1186_s13638_020_01807_0 crossref_primary_10_1007_s10207_023_00720_z crossref_primary_10_1109_TSG_2019_2922435 crossref_primary_10_1016_j_enbuild_2020_110492 crossref_primary_10_1016_j_engappai_2022_105775 crossref_primary_10_3390_en18184787 crossref_primary_10_3390_bdcc8080083 crossref_primary_10_1016_j_enbuild_2022_111872 crossref_primary_10_1108_IJESM_11_2017_0006 crossref_primary_10_1016_j_egyr_2022_03_205 crossref_primary_10_3390_su122410259 crossref_primary_10_3390_s20102912 crossref_primary_10_1016_j_egyr_2022_08_225 crossref_primary_10_3390_electronics11101609 crossref_primary_10_3390_en13143730 crossref_primary_10_1016_j_apenergy_2017_12_005 crossref_primary_10_1002_int_22404 crossref_primary_10_1016_j_eswa_2023_120565 crossref_primary_10_1049_cit2_12216 crossref_primary_10_3390_en17174529 crossref_primary_10_1007_s42979_025_04352_z crossref_primary_10_1016_j_enbuild_2021_111193 crossref_primary_10_26634_jit_8_1_15717 crossref_primary_10_3390_en15228663 crossref_primary_10_1016_j_egyr_2023_06_025 crossref_primary_10_1007_s12273_025_1269_z crossref_primary_10_1016_j_ifacol_2021_10_006 crossref_primary_10_1109_TII_2017_2740324 crossref_primary_10_1016_j_enbuild_2024_114559 crossref_primary_10_1016_j_rser_2024_114975 crossref_primary_10_3390_en14227465 crossref_primary_10_1016_j_autcon_2022_104445 crossref_primary_10_1109_TCE_2024_3354189 crossref_primary_10_1080_1206212X_2018_1533613 crossref_primary_10_1109_TSG_2018_2818167 crossref_primary_10_1016_j_enbuild_2017_11_039 crossref_primary_10_3390_info12120499 crossref_primary_10_1631_FITEE_2200053 crossref_primary_10_1007_s10479_023_05247_z crossref_primary_10_1016_j_scs_2021_103445 crossref_primary_10_1109_ACCESS_2019_2915641 crossref_primary_10_1016_j_cose_2020_102055 crossref_primary_10_1080_24725854_2024_2417258 crossref_primary_10_1002_eng2_12449 crossref_primary_10_1007_s40518_018_0112_x crossref_primary_10_1016_j_knosys_2019_05_026 crossref_primary_10_1016_j_solener_2022_02_053 crossref_primary_10_1002_widm_70033 crossref_primary_10_1016_j_energy_2022_125575 crossref_primary_10_3390_f12020194 crossref_primary_10_1016_j_engappai_2025_111755 crossref_primary_10_1007_s12652_022_04376_w crossref_primary_10_1016_j_energy_2020_119505 crossref_primary_10_1016_j_desal_2024_118154 crossref_primary_10_1007_s11227_021_03787_4 crossref_primary_10_1016_j_scs_2021_103054 crossref_primary_10_1016_j_enbuild_2020_109864 crossref_primary_10_1016_j_apenergy_2021_116601 crossref_primary_10_1016_j_buildenv_2025_113257 crossref_primary_10_1016_j_enbuild_2024_113964 crossref_primary_10_1016_j_enbuild_2017_11_008 crossref_primary_10_1080_23744731_2019_1565550 crossref_primary_10_1007_s10586_024_04383_w crossref_primary_10_3390_app13010314 crossref_primary_10_1016_j_enbuild_2024_115069 crossref_primary_10_1016_j_buildenv_2021_108066 crossref_primary_10_1016_j_neucom_2020_10_084 crossref_primary_10_1016_j_apenergy_2024_125169 crossref_primary_10_1016_j_enbenv_2024_12_005 crossref_primary_10_3390_make6020045 crossref_primary_10_1051_e3sconf_201911105017 crossref_primary_10_1016_j_scs_2021_102804 crossref_primary_10_1016_j_autcon_2018_04_006 crossref_primary_10_1016_j_jobe_2021_102812 crossref_primary_10_3390_s21041395 crossref_primary_10_1016_j_egypro_2018_08_187 crossref_primary_10_3390_en14123623 crossref_primary_10_1016_j_egyr_2023_12_045 crossref_primary_10_1016_j_enbuild_2022_112763 crossref_primary_10_1051_matecconf_202032403002 crossref_primary_10_1371_journal_pone_0250665 crossref_primary_10_3390_en13123136 crossref_primary_10_1016_j_cosrev_2024_100665 crossref_primary_10_1016_j_apenergy_2023_121030 crossref_primary_10_1016_j_enbuild_2023_113419 crossref_primary_10_1016_j_jocs_2025_102685 crossref_primary_10_1016_j_rser_2025_115478 crossref_primary_10_3390_su131910963 crossref_primary_10_1016_j_aei_2024_102810 crossref_primary_10_1016_j_buildenv_2023_110328 crossref_primary_10_1016_j_enbuild_2020_110309 crossref_primary_10_1016_j_jnca_2018_12_006 crossref_primary_10_1016_j_enbuild_2025_116447 crossref_primary_10_1016_j_jobe_2021_103139 crossref_primary_10_1007_s40860_020_00129_8 crossref_primary_10_1016_j_buildenv_2021_108057 crossref_primary_10_1016_j_scs_2019_101587 crossref_primary_10_1080_15325008_2020_1834019 crossref_primary_10_1109_ACCESS_2021_3083060 crossref_primary_10_1007_s10586_024_04440_4 crossref_primary_10_3390_s21041044 crossref_primary_10_3390_en14123654 crossref_primary_10_3390_su13063227 crossref_primary_10_1016_j_buildenv_2025_113595 crossref_primary_10_1109_ACCESS_2021_3128749 crossref_primary_10_1109_TCCN_2024_3355433 crossref_primary_10_1007_s11831_023_09977_y crossref_primary_10_3390_en13051046 crossref_primary_10_3390_s19071568 crossref_primary_10_1016_j_enconman_2020_112535 crossref_primary_10_1088_1742_6596_2385_1_012012 crossref_primary_10_1016_j_tsep_2019_03_002 crossref_primary_10_3390_electronics13071391 crossref_primary_10_1016_j_enbuild_2025_115589 crossref_primary_10_1016_j_sajce_2025_09_001 crossref_primary_10_1093_jcde_qwad056 crossref_primary_10_1016_j_enbuild_2022_112096
Cites_doi	10.1016/j.enbuild.2015.02.052 10.1023/A:1022859003006 10.1186/s40537-014-0011-y 10.1016/j.eswa.2016.03.002 10.1016/j.apenergy.2014.02.057 10.1016/j.eswa.2015.01.010 10.1016/j.envsoft.2009.08.010 10.1007/978-1-4899-7488-4_293 10.1016/0003-2670(86)80028-9 10.1613/jair.614 10.1145/1541880.1541882 10.1007/BF00994018 10.1007/BF00058655 10.1016/j.inffus.2007.03.001 10.1023/B:MACH.0000027783.34431.42 10.4236/jbise.2013.65070 10.1007/s13312-011-0055-4 10.1016/j.neunet.2016.01.003 10.1561/2200000006 10.1016/j.enbuild.2007.06.001 10.1016/j.eswa.2010.08.067 10.1023/A:1010933404324 10.1016/j.asoc.2015.10.011 10.1088/0967-3334/35/11/2191 10.1177/0272989X8900900307 10.1016/j.rser.2014.01.088 10.1016/j.apenergy.2010.04.008 10.1016/j.enbuild.2015.11.005 10.1007/BF02985802 10.1016/j.knosys.2013.03.001
ContentType	Journal Article
Copyright	2017 The Author(s) Copyright Elsevier BV Jun 1, 2017
Copyright_xml	– notice: 2017 The Author(s) – notice: Copyright Elsevier BV Jun 1, 2017
DBID	6I. AAFTH AAYXX CITATION 7ST 8FD C1K F28 FR3 KR7 SOI
DOI	10.1016/j.enbuild.2017.02.058
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Environment Abstracts Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Civil Engineering Abstracts Environment Abstracts
DatabaseTitle	CrossRef Civil Engineering Abstracts Engineering Research Database Technology Research Database Environment Abstracts ANTE: Abstracts in New Technology & Engineering Environmental Sciences and Pollution Management
DatabaseTitleList	Civil Engineering Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1872-6178
EndPage	206
ExternalDocumentID	10_1016_j_enbuild_2017_02_058 S0378778817306904
GeographicLocations	Canada Brampton Ontario Canada
GeographicLocations_xml	– name: Brampton Ontario Canada – name: Canada
GroupedDBID	--M -~X .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JM 9JN AABNK AACTN AAEDT AAEDW AAFTH AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AAXUO ABFYP ABJNI ABLST ABMAC ABYKQ ACDAQ ACGFS ACIWK ACRLP ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BJAXD BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE JJJVA KCYFY KOM LY6 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL SDF SDG SES SPC SPCBC SSJ SSR SST SSZ T5K ~02 ~G- --K 29G 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HVGLF HZ~ R2- RPZ SAC SET SEW WUQ ZMT ZY4 ~HD 7ST 8FD AGCQF C1K F28 FR3 KR7 SOI
ID	FETCH-LOGICAL-c384t-1ca7ac96bb9f975e05de7e97b7eaf9d5dd144cd3273ce8415236cdd8c7c68af93
ISICitedReferencesCount	174
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000401393000016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0378-7788
IngestDate	Wed Aug 13 08:39:53 EDT 2025 Tue Nov 18 22:14:58 EST 2025 Sat Nov 29 02:27:34 EST 2025 Fri Feb 23 02:27:57 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Support vector regression Random forest Building energy consumption Ensemble learning Autoencoder Anomaly detection
Language	English
License	This is an open access article under the CC BY-NC-ND license.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c384t-1ca7ac96bb9f975e05de7e97b7eaf9d5dd144cd3273ce8415236cdd8c7c68af93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
OpenAccessLink	https://dx.doi.org/10.1016/j.enbuild.2017.02.058
PQID	1932071671
PQPubID	2045483
PageCount	16
ParticipantIDs	proquest_journals_1932071671 crossref_citationtrail_10_1016_j_enbuild_2017_02_058 crossref_primary_10_1016_j_enbuild_2017_02_058 elsevier_sciencedirect_doi_10_1016_j_enbuild_2017_02_058
PublicationCentury	2000
PublicationDate	2017-06-01 2017-06-00 20170601
PublicationDateYYYYMMDD	2017-06-01
PublicationDate_xml	– month: 06 year: 2017 text: 2017-06-01 day: 01
PublicationDecade	2010
PublicationPlace	Lausanne
PublicationPlace_xml	– name: Lausanne
PublicationTitle	Energy and buildings
PublicationYear	2017
Publisher	Elsevier B.V Elsevier BV
Publisher_xml	– name: Elsevier B.V – name: Elsevier BV
References	Lam, Wan, Cheung, Yang (bib0040) 2008; 40 Fontugne, Ortiz, Tremblay, Borgnat, Flandrin, Fukuda, Culler, Esaki (bib0155) 2013 Folino, Pisani, Sabatino (bib0210) 2016 O, H Tsai (bib0255) 2011; 38 Chandola, Banerjee, Kumar (bib0010) 2009; 41 O.ai Opitz, Maclin (bib0030) 1999 Jovanović, Sretenović, Živković (bib0100) 2015; 94 Grandvalet (bib0250) 2004; 55 Peña, Biscarri, Guerrero, Monedero, Léon (bib0185) 2016; 56 Nguyen, Wang, Nguyen (bib0080) 2013; 6 Capozzoli, Lauro, Khan (bib0190) 2015; 42 Breiman (bib0065) 2001; 45 Jiang, Zeng, Xu, Li (bib0180) 2014 Wrinch, El-Fouly, Wong (bib0140) 2012 Zhang, Chen, Black (bib0165) 2011 McClish (bib0125) 1989; 9 Hill, Minsker (bib0145) 2010; 25 Kumar, Indrayan (bib0115) 2011; 48 Powersmiths, Powersmiths: Power for the Future Bellala, Marwah, Arlitt (bib0150) 2012 Team, R Core, R: A Language and Environment for Statistical Computing Polikar (bib0035) 2012 Vapnik, Kotz (bib0095) 1982; vol. 40 Araya, Grolinger, ElYamany, Capretz, Bitsuamlak (bib0020) 2016 Jain, Smith, Culligan, Taylor (bib0110) 2014; 123 . Geladi, Kowalski (bib0045) 1986; 185 Didaci, Giacinto, Roli (bib0205) 2002 Amozegar, Khorasani (bib0220) 2016; 76 Drăgoicea, Bucur, Pătraşcu (bib0015) 2013 Ellis, Kerr, Godbole, Lanckriet, Wing, Marshall (bib0070) 2014; 35 UNEP, United Nations Environment Program Chou, Telaga (bib0130) 2014; 33 Dietterich (bib0265) 2000 Cabrera, Gutiérrez, Mehra (bib0200) 2008; 9 Breiman (bib0240) 1996; 24 Cortes, Vapnik (bib0090) 1995; 20 H Zhou (bib0025) 2015 Kusiak, Li, Tang (bib0075) 2010; 87 Janetzko, Stoffel, Mittelstädt, Keim (bib0135) 2013; 38 Torres-Sospedra, Hernández-Espinosa, Fernández-Redondo (bib0245) 2011 Hastie, Tibshirani, Friedman, Franklin (bib0085) 2005; 27 Shoemaker, Hall (bib0230) 2011 Ploennigs, Chen, Schumann, Brady (bib0175) 2013 Bengio (bib0055) 2009; 2 Arjunan, Khadilkar, Ganu, Charbiwala, Singh, Singh (bib0160) 2015 Aburomman, Reaz (bib0225) 2016; 38 Pyle (bib0235) 1999 Kuncheva, Whitaker (bib0270) 2003; 51 Orsenigo, Vercellis (bib0260) 2013; 47 Hayes, Capretz (bib0195) 2015; 2 Smola, Vapnik (bib0105) 1997; 9 Hajian-Tilaki (bib0120) 2013; 4 Zhao, Mehrotra, Mohan (bib0215) 2015 Zorita, Fernández-Temprano, García-Escudero, Duque-Perez (bib0170) 2016; 110 Sakurada, Yairi (bib0060) 2014 Praus (bib0050) 2005; 3 Sakurada (10.1016/j.enbuild.2017.02.058_bib0060) 2014 Peña (10.1016/j.enbuild.2017.02.058_bib0185) 2016; 56 Cortes (10.1016/j.enbuild.2017.02.058_bib0090) 1995; 20 Orsenigo (10.1016/j.enbuild.2017.02.058_bib0260) 2013; 47 Kuncheva (10.1016/j.enbuild.2017.02.058_bib0270) 2003; 51 10.1016/j.enbuild.2017.02.058_bib0280 Jain (10.1016/j.enbuild.2017.02.058_bib0110) 2014; 123 10.1016/j.enbuild.2017.02.058_bib0275 Shoemaker (10.1016/j.enbuild.2017.02.058_bib0230) 2011 Smola (10.1016/j.enbuild.2017.02.058_bib0105) 1997; 9 Kusiak (10.1016/j.enbuild.2017.02.058_bib0075) 2010; 87 Drăgoicea (10.1016/j.enbuild.2017.02.058_bib0015) 2013 Chou (10.1016/j.enbuild.2017.02.058_bib0130) 2014; 33 Folino (10.1016/j.enbuild.2017.02.058_bib0210) 2016 Zhao (10.1016/j.enbuild.2017.02.058_bib0215) 2015 Zhang (10.1016/j.enbuild.2017.02.058_bib0165) 2011 Zhou (10.1016/j.enbuild.2017.02.058_bib0025) 2015 Aburomman (10.1016/j.enbuild.2017.02.058_bib0225) 2016; 38 Hajian-Tilaki (10.1016/j.enbuild.2017.02.058_bib0120) 2013; 4 Bengio (10.1016/j.enbuild.2017.02.058_bib0055) 2009; 2 Breiman (10.1016/j.enbuild.2017.02.058_bib0065) 2001; 45 Grandvalet (10.1016/j.enbuild.2017.02.058_bib0250) 2004; 55 Hastie (10.1016/j.enbuild.2017.02.058_bib0085) 2005; 27 Lam (10.1016/j.enbuild.2017.02.058_bib0040) 2008; 40 Dietterich (10.1016/j.enbuild.2017.02.058_bib0265) 2000 Amozegar (10.1016/j.enbuild.2017.02.058_bib0220) 2016; 76 Hayes (10.1016/j.enbuild.2017.02.058_bib0195) 2015; 2 Torres-Sospedra (10.1016/j.enbuild.2017.02.058_bib0245) 2011 Hill (10.1016/j.enbuild.2017.02.058_bib0145) 2010; 25 Vapnik (10.1016/j.enbuild.2017.02.058_bib0095) 1982; vol. 40 Opitz (10.1016/j.enbuild.2017.02.058_bib0030) 1999 Jiang (10.1016/j.enbuild.2017.02.058_bib0180) 2014 Geladi (10.1016/j.enbuild.2017.02.058_bib0045) 1986; 185 Araya (10.1016/j.enbuild.2017.02.058_bib0020) 2016 Kumar (10.1016/j.enbuild.2017.02.058_bib0115) 2011; 48 Jovanović (10.1016/j.enbuild.2017.02.058_bib0100) 2015; 94 Zorita (10.1016/j.enbuild.2017.02.058_bib0170) 2016; 110 Ploennigs (10.1016/j.enbuild.2017.02.058_bib0175) 2013 Capozzoli (10.1016/j.enbuild.2017.02.058_bib0190) 2015; 42 Tsai (10.1016/j.enbuild.2017.02.058_bib0255) 2011; 38 Chandola (10.1016/j.enbuild.2017.02.058_bib0010) 2009; 41 Nguyen (10.1016/j.enbuild.2017.02.058_bib0080) 2013; 6 Breiman (10.1016/j.enbuild.2017.02.058_bib0240) 1996; 24 Janetzko (10.1016/j.enbuild.2017.02.058_bib0135) 2013; 38 Ellis (10.1016/j.enbuild.2017.02.058_bib0070) 2014; 35 Bellala (10.1016/j.enbuild.2017.02.058_bib0150) 2012 Cabrera (10.1016/j.enbuild.2017.02.058_bib0200) 2008; 9 Pyle (10.1016/j.enbuild.2017.02.058_bib0235) 1999 Arjunan (10.1016/j.enbuild.2017.02.058_bib0160) 2015 10.1016/j.enbuild.2017.02.058_bib0285 McClish (10.1016/j.enbuild.2017.02.058_bib0125) 1989; 9 Praus (10.1016/j.enbuild.2017.02.058_bib0050) 2005; 3 Didaci (10.1016/j.enbuild.2017.02.058_bib0205) 2002 10.1016/j.enbuild.2017.02.058_bib0005 Polikar (10.1016/j.enbuild.2017.02.058_bib0035) 2012 Wrinch (10.1016/j.enbuild.2017.02.058_bib0140) 2012 Fontugne (10.1016/j.enbuild.2017.02.058_bib0155) 2013
References_xml	– volume: 2 start-page: 1 year: 2015 end-page: 22 ident: bib0195 article-title: Contextual anomaly detection framework for big sensor data publication-title: J. Big Data – start-page: 23 year: 2014 end-page: 30 ident: bib0180 article-title: Real time contextual collective anomaly detection over multiple data streams publication-title: SIGKDD Workshop on Outlier Detection and Description under Data Diversity – volume: 33 start-page: 400 year: 2014 end-page: 411 ident: bib0130 article-title: Real-time detection of anomalous power consumption publication-title: Renew. Sustain. Energy Rev. – start-page: 315 year: 2016 end-page: 331 ident: bib0210 article-title: A distributed intrusion detection framework based on evolved specialized ensembles of classifiers publication-title: Applications of Evolutionary Computation – start-page: 514 year: 2015 end-page: 525 ident: bib0215 article-title: Ensemble algorithms for unsupervised anomaly detection publication-title: Current Approaches in Applied Artificial Intelligence – year: 1999 ident: bib0235 article-title: Data Preparation for Data Mining – reference: O.ai, – start-page: 994 year: 2012 end-page: 1002 ident: bib0150 article-title: Following the electrons: methods for power management in commercial buildings publication-title: 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD '12 – volume: 38 start-page: 2766 year: 2011 end-page: 2773 ident: bib0255 article-title: Dimensionality reduction techniques for blog visualization publication-title: Expert Syst. Appl. – volume: 48 start-page: 277 year: 2011 end-page: 287 ident: bib0115 article-title: Receiver operating characteristic (ROC) curve for medical research. publication-title: Indian Pediatr. – volume: 55 start-page: 251 year: 2004 end-page: 270 ident: bib0250 article-title: Bagging equalizes influence publication-title: Mach. Learn. – start-page: 129 year: 2013 end-page: 140 ident: bib0155 article-title: Strip, bind, and search: a method for identifying abnormal energy consumption in buildings publication-title: 12th International Conference on Information Processing in Sensor Networks – start-page: 169 year: 1999 end-page: 198 ident: bib0030 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. – start-page: 1 year: 2000 end-page: 15 ident: bib0265 article-title: Ensemble methods in machine learning publication-title: Multiple Classifier Systems – start-page: 411 year: 2015 end-page: 416 ident: bib0025 article-title: Ensemble learning publication-title: Encycl. Biom. – volume: 87 start-page: 3092 year: 2010 end-page: 3102 ident: bib0075 article-title: Modeling and optimization of hvac energy consumption publication-title: Appl. Energy – start-page: 4 year: 2014 ident: bib0060 article-title: Anomaly detection using autoencoders with nonlinear dimensionality reduction publication-title: MLSDA 2014, 2nd Workshop on Machine Learning for Sensory Data Analysis – volume: 41 start-page: 1 year: 2009 end-page: 58 ident: bib0010 article-title: Anomaly detection publication-title: ACM Comput. Surv. – volume: 6 start-page: 551 year: 2013 end-page: 560 ident: bib0080 article-title: Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic publication-title: J. Biomed. Sci. Eng. – volume: 9 start-page: 155 year: 1997 end-page: 161 ident: bib0105 article-title: Support vector regression machines publication-title: Adv. Neural Inf. Process. Syst. – volume: 40 start-page: 828 year: 2008 end-page: 836 ident: bib0040 article-title: Principal component analysis of electricity use in office buildings publication-title: Energy Build. – volume: 9 start-page: 190 year: 1989 end-page: 195 ident: bib0125 article-title: Analyzing a portion of the ROC curve publication-title: Med. Decis. Mak. – volume: 25 start-page: 1014 year: 2010 end-page: 1022 ident: bib0145 article-title: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach publication-title: Environ. Model. Softw. – volume: 38 start-page: 360 year: 2016 end-page: 372 ident: bib0225 article-title: A novel SVM-KNN-PSO ensemble method for intrusion detection system publication-title: Appl. Soft Comput. – start-page: 1 year: 2011 end-page: 8 ident: bib0165 article-title: Anomaly detection in premise energy consumption data publication-title: 2011 IEEE Power and Energy Society General Meeting – volume: 185 start-page: 1 year: 1986 end-page: 17 ident: bib0045 article-title: Partial least-squares regression: a tutorial publication-title: Anal. Chim. Acta – start-page: 17:1 year: 2013 end-page: 17:8 ident: bib0175 article-title: Exploiting generalized additive models for diagnosing abnormal energy use in buildings publication-title: 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings – start-page: 35 year: 2015 end-page: 44 ident: bib0160 article-title: Multi-user energy consumption monitoring and anomaly detection with partial context information publication-title: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: bib0090 article-title: Support-vector networks publication-title: Mach. Learn. – volume: 51 start-page: 181 year: 2003 end-page: 207 ident: bib0270 article-title: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy publication-title: Mach. Learn. – volume: 47 start-page: 14 year: 2013 end-page: 22 ident: bib0260 article-title: Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction publication-title: Knowl. Based Syst. – volume: 4 start-page: 627 year: 2013 end-page: 635 ident: bib0120 article-title: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation publication-title: Casp. J. Internal Med. – start-page: 1 year: 2012 end-page: 34 ident: bib0035 article-title: Ensemble learning publication-title: Ensemble Machine Learning – volume: 3 start-page: 731 year: 2005 end-page: 741 ident: bib0050 article-title: SVD-based principal component analysis of geochemical data publication-title: Cent. Eur. J. Chem. – reference: UNEP, United Nations Environment Program, – volume: 38 start-page: 1 year: 2013 end-page: 11 ident: bib0135 article-title: Computers and graphics anomaly detection for visual analytics of power consumption data publication-title: Comput. Graph. – reference: Powersmiths, Powersmiths: Power for the Future, – start-page: 588 year: 2011 end-page: 595 ident: bib0245 article-title: Using bagging and cross-validation to improve ensembles based on penalty terms publication-title: Neural Information Processing – start-page: 14 year: 2013 end-page: 28 ident: bib0015 article-title: A service oriented simulation architecture for intelligent building management publication-title: Exploring Services Science – volume: vol. 40 year: 1982 ident: bib0095 publication-title: Estimation of Dependences Based on Empirical Data – reference: Team, R Core, R: A Language and Environment for Statistical Computing, – volume: 42 start-page: 4324 year: 2015 end-page: 4338 ident: bib0190 article-title: Fault detection analysis using data mining techniques for a cluster of smart office buildings publication-title: Expert Syst. Appl. – volume: 123 start-page: 168 year: 2014 end-page: 178 ident: bib0110 article-title: Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy publication-title: Appl. Energy – reference: H – volume: 27 start-page: 83 year: 2005 end-page: 85 ident: bib0085 article-title: The elements of statistical learning: data mining, inference and prediction publication-title: Math. Intell. – volume: 56 start-page: 242 year: 2016 end-page: 255 ident: bib0185 article-title: Rule-based system to detect energy efficiency anomalies in smart buildings: a data mining approach publication-title: Expert Syst. Appl. – volume: 2 start-page: 1 year: 2009 end-page: 127 ident: bib0055 article-title: Learning deep architectures for AI publication-title: Found. Trends Mach. Learn. – volume: 76 start-page: 106 year: 2016 end-page: 121 ident: bib0220 article-title: An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines publication-title: Neural Netw. – reference: . – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0065 article-title: Random forests publication-title: Mach. Learn. – reference: O, H – volume: 35 start-page: 2191 year: 2014 end-page: 2203 ident: bib0070 article-title: A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers publication-title: Physiol. Meas. – volume: 94 start-page: 189 year: 2015 end-page: 199 ident: bib0100 article-title: Ensemble of various neural networks for prediction of heating energy consumption publication-title: Energy Build. – start-page: 6 year: 2011 end-page: 15 ident: bib0230 article-title: Anomaly detection using ensembles publication-title: Multiple Classifier Systems – start-page: 1 year: 2016 end-page: 8 ident: bib0020 article-title: Collective contextual anomaly detection framework for smart buildings publication-title: International Joint Conference on Neural Networks (IJCNN) – volume: 110 start-page: 377 year: 2016 end-page: 386 ident: bib0170 article-title: A statistical modeling approach to detect anomalies in energetic efficiency of buildings publication-title: Energy Build. – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib0240 article-title: Bagging predictors publication-title: Mach. Learn. – start-page: 1 year: 2012 end-page: 6 ident: bib0140 article-title: Anomaly detection of building systems using energy demand frequency domain analysis publication-title: 2012 IEEE Power and Energy Society General Meeting – year: 2002 ident: bib0205 article-title: Ensemble learning for intrusion detection in computer networks publication-title: Workshop Machine Learning Methods Applications – volume: 9 start-page: 96 year: 2008 end-page: 119 ident: bib0200 article-title: Ensemble methods for anomaly detection and distributed intrusion detection in mobile ad-hoc networks publication-title: Inf. Fusion – volume: 94 start-page: 189 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0100 article-title: Ensemble of various neural networks for prediction of heating energy consumption publication-title: Energy Build. doi: 10.1016/j.enbuild.2015.02.052 – volume: 51 start-page: 181 issue: 2 year: 2003 ident: 10.1016/j.enbuild.2017.02.058_bib0270 article-title: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy publication-title: Mach. Learn. doi: 10.1023/A:1022859003006 – year: 1999 ident: 10.1016/j.enbuild.2017.02.058_bib0235 – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0195 article-title: Contextual anomaly detection framework for big sensor data publication-title: J. Big Data doi: 10.1186/s40537-014-0011-y – volume: 56 start-page: 242 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0185 article-title: Rule-based system to detect energy efficiency anomalies in smart buildings: a data mining approach publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.03.002 – start-page: 4 year: 2014 ident: 10.1016/j.enbuild.2017.02.058_bib0060 article-title: Anomaly detection using autoencoders with nonlinear dimensionality reduction – volume: 123 start-page: 168 year: 2014 ident: 10.1016/j.enbuild.2017.02.058_bib0110 article-title: Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy publication-title: Appl. Energy doi: 10.1016/j.apenergy.2014.02.057 – volume: 4 start-page: 627 issue: 2 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0120 article-title: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation publication-title: Casp. J. Internal Med. – start-page: 1 year: 2011 ident: 10.1016/j.enbuild.2017.02.058_bib0165 article-title: Anomaly detection in premise energy consumption data – volume: 42 start-page: 4324 issue: 9 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0190 article-title: Fault detection analysis using data mining techniques for a cluster of smart office buildings publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.01.010 – start-page: 23 year: 2014 ident: 10.1016/j.enbuild.2017.02.058_bib0180 article-title: Real time contextual collective anomaly detection over multiple data streams – volume: 25 start-page: 1014 issue: 9 year: 2010 ident: 10.1016/j.enbuild.2017.02.058_bib0145 article-title: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2009.08.010 – start-page: 411 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0025 article-title: Ensemble learning publication-title: Encycl. Biom. doi: 10.1007/978-1-4899-7488-4_293 – volume: 185 start-page: 1 year: 1986 ident: 10.1016/j.enbuild.2017.02.058_bib0045 article-title: Partial least-squares regression: a tutorial publication-title: Anal. Chim. Acta doi: 10.1016/0003-2670(86)80028-9 – start-page: 994 year: 2012 ident: 10.1016/j.enbuild.2017.02.058_bib0150 article-title: Following the electrons: methods for power management in commercial buildings – start-page: 169 year: 1999 ident: 10.1016/j.enbuild.2017.02.058_bib0030 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.614 – volume: 41 start-page: 1 issue: 3 year: 2009 ident: 10.1016/j.enbuild.2017.02.058_bib0010 article-title: Anomaly detection publication-title: ACM Comput. Surv. doi: 10.1145/1541880.1541882 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 10.1016/j.enbuild.2017.02.058_bib0090 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1007/BF00994018 – volume: vol. 40 year: 1982 ident: 10.1016/j.enbuild.2017.02.058_bib0095 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.enbuild.2017.02.058_bib0240 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – start-page: 514 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0215 article-title: Ensemble algorithms for unsupervised anomaly detection – ident: 10.1016/j.enbuild.2017.02.058_bib0275 – volume: 9 start-page: 96 issue: 1 year: 2008 ident: 10.1016/j.enbuild.2017.02.058_bib0200 article-title: Ensemble methods for anomaly detection and distributed intrusion detection in mobile ad-hoc networks publication-title: Inf. Fusion doi: 10.1016/j.inffus.2007.03.001 – start-page: 1 year: 2012 ident: 10.1016/j.enbuild.2017.02.058_bib0035 article-title: Ensemble learning – start-page: 129 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0155 article-title: Strip, bind, and search: a method for identifying abnormal energy consumption in buildings – start-page: 1 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0020 article-title: Collective contextual anomaly detection framework for smart buildings – start-page: 35 year: 2015 ident: 10.1016/j.enbuild.2017.02.058_bib0160 article-title: Multi-user energy consumption monitoring and anomaly detection with partial context information – volume: 55 start-page: 251 issue: 3 year: 2004 ident: 10.1016/j.enbuild.2017.02.058_bib0250 article-title: Bagging equalizes influence publication-title: Mach. Learn. doi: 10.1023/B:MACH.0000027783.34431.42 – ident: 10.1016/j.enbuild.2017.02.058_bib0005 – volume: 6 start-page: 551 issue: 5 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0080 article-title: Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic publication-title: J. Biomed. Sci. Eng. doi: 10.4236/jbise.2013.65070 – volume: 38 start-page: 1 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0135 article-title: Computers and graphics anomaly detection for visual analytics of power consumption data publication-title: Comput. Graph. – volume: 48 start-page: 277 issue: 17 year: 2011 ident: 10.1016/j.enbuild.2017.02.058_bib0115 article-title: Receiver operating characteristic (ROC) curve for medical research. publication-title: Indian Pediatr. doi: 10.1007/s13312-011-0055-4 – volume: 76 start-page: 106 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0220 article-title: An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines publication-title: Neural Netw. doi: 10.1016/j.neunet.2016.01.003 – year: 2002 ident: 10.1016/j.enbuild.2017.02.058_bib0205 article-title: Ensemble learning for intrusion detection in computer networks – start-page: 1 year: 2012 ident: 10.1016/j.enbuild.2017.02.058_bib0140 article-title: Anomaly detection of building systems using energy demand frequency domain analysis – start-page: 588 year: 2011 ident: 10.1016/j.enbuild.2017.02.058_bib0245 article-title: Using bagging and cross-validation to improve ensembles based on penalty terms – volume: 2 start-page: 1 issue: 1 year: 2009 ident: 10.1016/j.enbuild.2017.02.058_bib0055 article-title: Learning deep architectures for AI publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000006 – volume: 40 start-page: 828 issue: 5 year: 2008 ident: 10.1016/j.enbuild.2017.02.058_bib0040 article-title: Principal component analysis of electricity use in office buildings publication-title: Energy Build. doi: 10.1016/j.enbuild.2007.06.001 – start-page: 315 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0210 article-title: A distributed intrusion detection framework based on evolved specialized ensembles of classifiers – start-page: 17:1 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0175 article-title: Exploiting generalized additive models for diagnosing abnormal energy use in buildings – volume: 38 start-page: 2766 issue: 3 year: 2011 ident: 10.1016/j.enbuild.2017.02.058_bib0255 article-title: Dimensionality reduction techniques for blog visualization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.08.067 – volume: 3 start-page: 731 issue: 4 year: 2005 ident: 10.1016/j.enbuild.2017.02.058_bib0050 article-title: SVD-based principal component analysis of geochemical data publication-title: Cent. Eur. J. Chem. – ident: 10.1016/j.enbuild.2017.02.058_bib0285 – start-page: 6 year: 2011 ident: 10.1016/j.enbuild.2017.02.058_bib0230 article-title: Anomaly detection using ensembles – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.enbuild.2017.02.058_bib0065 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 38 start-page: 360 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0225 article-title: A novel SVM-KNN-PSO ensemble method for intrusion detection system publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.10.011 – volume: 35 start-page: 2191 issue: 11 year: 2014 ident: 10.1016/j.enbuild.2017.02.058_bib0070 article-title: A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers publication-title: Physiol. Meas. doi: 10.1088/0967-3334/35/11/2191 – volume: 9 start-page: 190 issue: 3 year: 1989 ident: 10.1016/j.enbuild.2017.02.058_bib0125 article-title: Analyzing a portion of the ROC curve publication-title: Med. Decis. Mak. doi: 10.1177/0272989X8900900307 – volume: 9 start-page: 155 year: 1997 ident: 10.1016/j.enbuild.2017.02.058_bib0105 article-title: Support vector regression machines publication-title: Adv. Neural Inf. Process. Syst. – volume: 33 start-page: 400 year: 2014 ident: 10.1016/j.enbuild.2017.02.058_bib0130 article-title: Real-time detection of anomalous power consumption publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2014.01.088 – volume: 87 start-page: 3092 issue: 10 year: 2010 ident: 10.1016/j.enbuild.2017.02.058_bib0075 article-title: Modeling and optimization of hvac energy consumption publication-title: Appl. Energy doi: 10.1016/j.apenergy.2010.04.008 – volume: 110 start-page: 377 year: 2016 ident: 10.1016/j.enbuild.2017.02.058_bib0170 article-title: A statistical modeling approach to detect anomalies in energetic efficiency of buildings publication-title: Energy Build. doi: 10.1016/j.enbuild.2015.11.005 – start-page: 14 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0015 article-title: A service oriented simulation architecture for intelligent building management – ident: 10.1016/j.enbuild.2017.02.058_bib0280 – volume: 27 start-page: 83 issue: 2 year: 2005 ident: 10.1016/j.enbuild.2017.02.058_bib0085 article-title: The elements of statistical learning: data mining, inference and prediction publication-title: Math. Intell. doi: 10.1007/BF02985802 – start-page: 1 year: 2000 ident: 10.1016/j.enbuild.2017.02.058_bib0265 article-title: Ensemble methods in machine learning – volume: 47 start-page: 14 year: 2013 ident: 10.1016/j.enbuild.2017.02.058_bib0260 article-title: Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2013.03.001
SSID	ssj0006571
Score	2.620431
Snippet	During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	191
SubjectTerms	Alarm systems Anomalies Anomaly detection Autoencoder Building automation Building energy consumption Classifiers Consumption patterns Energy conservation Energy consumption Energy efficiency Energy usage Ensemble learning Fault diagnosis Faults Random forest Sliding Smart buildings Support vector regression
Title	An ensemble learning framework for anomaly detection in building energy consumption
URI	https://dx.doi.org/10.1016/j.enbuild.2017.02.058 https://www.proquest.com/docview/1932071671
Volume	144
WOSCitedRecordID	wos000401393000016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6178 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006571 issn: 0378-7788 databaseCode: AIEXJ dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLbKxgM8IK5iYyA_IF6qlKRxYvuxQh0XVQWJbuqb5dgO2pRmZe2mlV_BT-Y4tpNyHTzwElVJ3YvPl3PLd85B6LkChy03Oo6sOY0I-AARjwsIXBUppC4Tbpqc7vGETqdsPucfer2voRbmsqJ1za6u-PK_ihrOgbBt6ew_iLv9UDgBr0HocASxw_GvBD-q-xCamoUtiapC4qMMHCzPmjxbyGrT12ZtVGA7Fn5Adt-4ckDVFGcuW8GF9L27avPtYcWqQ43cyK5wvZvo_LqZDfSpJXBAgF639mBcbeQCdFJjBS39oJvDLC0d8ouj98PWLQIfwacpEtrRqVzurK2fOd7ScCmEsJS6uX4D4zQwo8OmbvE7FU3IlpJN3Hyvn5S_y0OcDmzTCPj_lrdHm4asGeusXXjCP30vDo8mEzEbz2cvlp8jO4fMPq_3Q1luoN0hzThozt3R2_H8XWvd86wJ4tuf3lWFvfzlN__O3_nB8jfuzOwuuuPjEDxy-LmHeqa-j25vdad8gD6OahyQhAOScIskDEjCHkm4RRI-qXHABXZIwltIeoiODsezV28iP4MjUikj6yhRkkrF86LgJaeZiTNtqOG0oEaWXGdag2yUTsELVoZZbzDNldZMUZUzeEf6CO3UZ7V5jLAiEJ7GkpVZAoogMwVhmhScEVXGOi_MHiJhp4TyDertnJRKBCbiqfAbLOwGi3goYIP30KBdtnQdWq5bwIIYhHcznfsoAEjXLT0IYhP-ll8JGwKBo57TZP_Pl5-gW92dcYB21ucX5im6qS7XJ6vzZx5o3wC4S6sm
linkProvider	Elsevier
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=An+ensemble+learning+framework+for+anomaly+detection+in+building+energy+consumption&rft.jtitle=Energy+and+buildings&rft.au=Araya%2C+Daniel+B&rft.au=Grolinger%2C+Katarina&rft.au=Elyamany%2C+Hany+F&rft.au=Capretz%2C+Miriam+AM&rft.date=2017-06-01&rft.pub=Elsevier+BV&rft.issn=0378-7788&rft.eissn=1872-6178&rft.volume=144&rft.spage=191&rft_id=info:doi/10.1016%2Fj.enbuild.2017.02.058&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-7788&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-7788&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-7788&client=summon