Classification of Energy Consumption in Buildings with Outlier Detection.

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.