Effective feedback can reduce building power consumption and carbon emissions. Therefore, providing
information to building managers and tenants is the first step in identifying ways to reduce power
consumption. Since reducing anomalous consumption can have a large impact, this study proposes a
novel approach to using large sets of data for a building space to identify anomalous power consumption.
This method identifies anomalies in two stages: consumption prediction and anomaly detection.
Daily real-time consumption is predicted by using a hybrid neural net ARIMA (auto-regressive integrated
moving average) model of daily consumption. Anomalies are then identified by differences between
real and predicted consumption by applying the two-sigma rule. The experimental results for a
17-week study of electricity consumption in a building office space confirm that the method can detect
anomalous values in real time. Another contribution of the study is the development of a formalized
methodology for detecting anomalous patterns in large data sets for real-time of building office space
energy consumption. Moreover, the prediction component can be used to plan electricity usage while
the anomaly detection component can be used to understand the energy consumption behaviors of
tenants.