| Téléchargement | - Voir le manuscrit accepté : Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation (PDF, 604 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/COASE.2018.8560433 |
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| Auteur | Rechercher : Ashouri, Araz1; Rechercher : Hu, Yitian1; Rechercher : Newsham, Guy R.1; Rechercher : Shen, Weiming1 |
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| Affiliation | - Conseil national de recherches du Canada. Construction
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| Format | Texte, Article |
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| Conférence | 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 20-24 August 2018, Munich, Germany |
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| Sujet | fault detection and diagnosis; building energy management; energy auditing; data analysis; electricity demand |
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| Résumé | Building system faults in commercial and office buildings can result in a reduced occupant comfort and increased utility bills. Energy performance-based anomaly detection helps operators efficiently identify faults. In this work, a data-driven method for anomaly detection is presented. Using a symbolic aggregate method, the weekly energy demand profiles are statistically quantised and labeled to determine normal and abnormal building behaviours. A case study with three federal office buildings has been conducted to demonstrate the proposed method. The resulting technology provides building operators with easily-interpreted and actionable information for optimised building performance. |
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| Date de publication | 2018-12-06 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro du CNRC | NRCC-CONST-56261E |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 4a2c0581-f009-4692-a91c-c6c91a1f71d9 |
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| Enregistrement créé | 2019-04-11 |
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| Enregistrement modifié | 2020-06-03 |
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