The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational costThis research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the ‘Smart IT Engineering and Solutions’ accredited research group (Generalitat de Catalunya, 2014 SGR 1052

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