| Téléchargement | - Voir le manuscrit accepté : Model-based and data-driven anomaly detection for heating and cooling demands in office buildings (PDF, 709 Kio)
|
|---|
| Auteur | Rechercher : Ashouri, Araz1; Rechercher : Hu, Yitian1; Rechercher : Gunay, H. Burak1; Rechercher : Newsham, Guy R.1; Rechercher : Shen, Weiming1 |
|---|
| Affiliation | - Conseil national de recherches du Canada. Construction
|
|---|
| Format | Texte, Article |
|---|
| Conférence | ASHRAE Winter Conference 2019, 12-16 January 2019, Atlanta, GA, USA. |
|---|
| Sujet | fault detection and diagnosis; building energy management; energy auditing; data analysis; heating and cooling demand |
|---|
| Résumé | A considerable portion of total energy loss within the built environment originates from operational errors during the actual lifespan of a building. With the rise of fully automated commercial buildings, a large amount of sensory data is becoming available that can be leveraged to detect and predict such errors. However, processing these data on-site requires significant knowledge and effort by building operators. In this work, a combination of model-based and data-driven approaches are employed to facilitate the analysis of historical energy demand data. Using change-point models and symbolic quantisation techniques, a large dataset of heating and cooling demand profiles collected from several office buildings are transformed into a format that is easily interpreted by the building operator and is suitable for actionable anomaly detection. Further quantification of anomalies and calculation of potential savings are drawn from the results. |
|---|
| Date de publication | 2019 |
|---|
| Maison d’édition | ASHRAE |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Numéro du CNRC | NRCC-CONST-56288E |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 5956daae-1943-4194-a641-44f215d11c9e |
|---|
| Enregistrement créé | 2019-04-11 |
|---|
| Enregistrement modifié | 2020-06-03 |
|---|
