Open AccessData analytics for smart buildings: a classification method for anomaly detection for measured data
Author(s) -
Enguerrand de Rautlin de la Roy,
Thomas Recht,
Akka Zemmari,
Pierre Bourreau,
Laurent Mora
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012015
Subject(s) - anomaly detection , computer science , outlier , data mining , analytics , anomaly (physics) , data quality , typology , data analysis , quality (philosophy) , data science , artificial intelligence , engineering , geography , metric (unit) , philosophy , operations management , physics , archaeology , epistemology , condensed matter physics
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.