| Titre : | Classification of energy consumption in buildings with outlier detection (2011) |
| Auteurs : | Xiaoli Li, Auteur ; Bowers, Chris P., Auteur ; Schnier, Thorsten, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | IEEE transactions on industrial electronics (Vol. 57 N° 11, Novembre 2010) |
| Article en page(s) : | pp. 3639 - 3644 |
| Note générale : | Génie électrique |
| Langues : | Anglais |
| Index. décimale : | 621.38 (Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X) |
| Tags : | Canonical variate analysis (CVA) Electricity data Energy management Modeling Outlier detection Prediction |
| Résumé : | In this paper, we propose an intelligent data-analysis method for modeling and prediction of daily electricity consumption in buildings. The objective is to enable a building-management system to be used for forecasting and detection of abnormal energy use. First, an outlier-detection method is proposed to identify abnormally high or low energy use in a building. Then a canonical variate analysis is employed to describe latent variables of daily electricity-consumption profiles, which can be used to group the data sets into different clusters. Finally, a simple classifier is used to predict the daily electricity-consumption profiles. A case study, based on a mixed-use environment, was studied. The results demonstrate that the method proposed in this paper can be used in conjunction with a building-management system to identify abnormal utility consumption and notify building operators in real time. |
| DEWEY : | 621.38 |
| ISSN : | 0278-0046 |
| En ligne : | http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5175339 |
