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Classification of Energy Consumption in Buildings with Outlier Detection.

Li, X. and Bowers, Christopher and Schnier, T. (2010) Classification of Energy Consumption in Buildings with Outlier Detection. IEEE Transactions on Industrial Electronics, 57 (11). pp. 3639-3644. ISSN 0278-0046

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Abstract

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.

Item Type: Article
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Uncontrolled Keywords: energy management, outlier detection, electricity data, canonical variate analysis, modelling, prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > TH Building construction
Divisions: Academic Departments > Worcester Business School
Related URLs:
Depositing User: Christopher Bowers
Date Deposited: 06 Mar 2015 13:15
Last Modified: 06 Mar 2015 13:15
URI: https://eprints.worc.ac.uk/id/eprint/3627

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