Data analytics for smart buildings: a classification method for anomaly detection for measured data
| dc.rights.license | open | en_US |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | ROY, Enguerrand De Rautlin De La | |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | RECHT, Thomas
IDREF: 202411974 | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | ZEMMARI, Akka
IDREF: 157264491 | |
| dc.contributor.author | BOURREAU, Pierre | |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | MORA, Laurent
IDREF: 077660870 | |
| dc.date.accessioned | 2021-12-21T10:25:06Z | |
| dc.date.available | 2021-12-21T10:25:06Z | |
| dc.date.issued | 2021-11-01 | |
| dc.identifier.issn | 1742-6588 | en_US |
| dc.identifier.uri | oai:crossref.org:10.1088/1742-6596/2042/1/012015 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/124290 | |
| dc.description.abstractEn | Abstract Data generated by the increasingly frequent use of sensors in housing provide the opportunity to monitor, manage and optimize the energy consumption of a building and the user comfort. These data are often strewn with rare or anomalous events, considered as anomalies (or outliers), that must be detected and ultimately corrected in order to improve the data quality. However, many approaches are used or might be used (for the most recent ones) to achieve this purpose. This paper proposes a classification methodology of anomaly detection techniques applied to building measurements. This classification methodology uses a well-suited anomaly typology and measurement typology in order to provide, in the future, a classification of the most adapted anomaly detection techniques for different types of building measurements, anomalies and needs. | |
| dc.language.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.source | crossref | |
| dc.title.en | Data analytics for smart buildings: a classification method for anomaly detection for measured data | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1088/1742-6596/2042/1/012015 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Autre | en_US |
| bordeaux.journal | Journal of Physics: Conference Series | en_US |
| bordeaux.page | 012015 | en_US |
| bordeaux.volume | 2042 | en_US |
| bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | en_US |
| bordeaux.issue | 1 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.institution | INRAE | en_US |
| bordeaux.institution | Arts et Métiers | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | dissemin | |
| hal.identifier | hal-03498868 | |
| hal.version | 1 | |
| hal.date.transferred | 2021-12-21T10:25:09Z | |
| hal.export | true | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Physics:%20Conference%20Series&rft.date=2021-11-01&rft.volume=2042&rft.issue=1&rft.spage=012015&rft.epage=012015&rft.eissn=1742-6588&rft.issn=1742-6588&rft.au=ROY,%20Enguerrand%20De%20Rautlin%20De%20La&RECHT,%20Thomas&ZEMMARI,%20Akka&BOURREAU,%20Pierre&MORA,%20Laurent&rft.genre=article |
