SANDMAN: a Self-Adapted System for Anomaly Detection in Smart Buildings Data Streams - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

SANDMAN: a Self-Adapted System for Anomaly Detection in Smart Buildings Data Streams

Résumé

Currently, energy management within buildings is essential to mitigate climate change. To this end, buildings are increasingly equipped with sensors to assist the building manager. Yet, the heterogeneity and the large amount of generated data make this task quite difficult. The SANDMAN multi-agent system, described in this paper, aims to assist in the automatic detection, in constrained time, of several types of anomalies using raw and heterogeneous data. SANDMAN features a semisupervised learning by considering some feedback from an expert in the field. The results show that SANDMAN detects different types of anomalies, is resilient to noise and is scalable.
Fichier principal
Vignette du fichier
2020_WETICE_ID#29_Houssin&Al.pdf (1.66 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03024015 , version 1 (25-11-2020)

Identifiants

  • HAL Id : hal-03024015 , version 1

Citer

Maxime Houssin, Stéphanie Combettes, Marie-Pierre Gleizes, Bérangère Lartigue. SANDMAN: a Self-Adapted System for Anomaly Detection in Smart Buildings Data Streams. 18th Adaptive Computing (and Agents) for Enhanced Collaboration at IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (ACEC @ WETICE 2020), Jun 2020, Bayonne, France. ⟨hal-03024015⟩
80 Consultations
79 Téléchargements

Partager

Gmail Facebook X LinkedIn More