Title :
Classification of Energy Consumption in Buildings With Outlier Detection
Author :
Li, Xiaoli ; Bowers, Chris P. ; Schnier, Thorsten
Author_Institution :
Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China
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.
Keywords :
building management systems; energy consumption; statistical analysis; building management system; buildings energy consumption; canonical variate analysis; cluster analysis; data sets; electricity consumption; energy usage detection; energy usage forecasting; intelligent data analysis method; Data analysis; Energy consumption; Energy efficiency; Energy management; Feedforward neural networks; Intelligent structures; Load forecasting; Neural networks; Permission; Predictive models; Canonical variate analysis (CVA); electricity data; energy management; modeling; outlier detection; prediction;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2009.2027926