Data analytics for smart buildings: a classification method for anomaly detection for measured data

Abstract Data generated by the increasingly frequent use of sensors in housing provide opportunity to monitor, manage and optimize energy consumption a building user comfort. These data are often strewn with rare or anomalous events, considered as anomalies (or outliers), that must be detected ultimately corrected order improve quality. However, many approaches used might (for most recent ones) achieve this purpose. This paper proposes classification methodology anomaly detection techniques applied measurements. uses well-suited typology measurement provide, future, adapted for different types measurements, needs.