SANDMAN: a Self-Adapted System for Anomaly Detection in Smart Buildings Data Streams
Overview & Synopsis
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.
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hal.science)
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Document Information
| Document Title | SANDMAN: a Self-Adapted System for Anomaly Detection in Smart Buildings Data Streams |
|---|---|
| Author / Organization | Maxime Houssin, Stéphanie Combettes, Marie-Pierre Gleizes, Bérangère Lartigue |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2020 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-128799 |
| Original Source Link | Visit Source Page on hal.science » |