Principal component analysis for monitoring electrical consumption of academic buildings

In this paper Principal Component Analysis (PCA) is proposed for monitoring electric consumption of building. PCA allows modeling correlations between independent variables (weather, calendar) and energy consumption at different time scales (hourly, daily, weekly monthly). Multiway principal component analysis (MPCA) is used to model time dependencies of variables as it is commonly done in batch process monitoring. This approach allows defining simple statistic indices T2 and SPE to be used in monitoring charts. These indices are used to detect abnormal behaviours at selected time scales. After detection, contribution analysis is performed to isolate variables responsible of such misbehaviour. Exploitation of such models, obtained during normal operating conditions, can be used to detect both faults in sensors and misbehaviours in consumption patterns with respect to independent variables. The paper presents the methodology and illustrates it in a case study focused on academic buildings situated in the Campus of the University of Girona. (C) 2014 The Authors. Published by Elsevier Ltd.