Classification of energy consumption in buildings with outlier detection / Xiaoli Li in IEEE transactions on industrial electronics, Vol. 57 N° 11 (Novembre 2010)  

Public ISBD [article]  inIEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3639 - 3644 Titre : Classification of energy consumption in buildings with outlier detection Type de document : texte imprimé Auteurs : Xiaoli Li, Auteur ; Bowers, Chris P., Auteur ; Schnier, Thorsten, Auteur Année de publication : 2011 Article en page(s) : pp. 3639 - 3644 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Canonical variate analysis (CVA) Electricity data Energy management Modeling Outlier detection Prediction Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X 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 [article] Classification of energy consumption in buildings with outlier detection [texte imprimé] / Xiaoli Li, Auteur ; Bowers, Chris P., Auteur ; Schnier, Thorsten, Auteur . - 2011 . - pp. 3639 - 3644. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3639 - 3644 Mots-clés : Canonical variate analysis (CVA) Electricity data Energy management Modeling Outlier detection Prediction Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X 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

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