Statistical Anomaly Detection in Mean and Variation of Energy Consumption

The timely detection of abnormal energy usage is one of the major ad-hoc techniques to optimize energy efficiency. Typically an alarm is triggered either by a significant drift from the baseline consumption level or by a period of large variations. In this paper we propose a statistical predictive method for detecting anomalies both in mean and in variation. The criterion behind is based on the prediction intervals (PIs) of the baseline, which is estimated by the Generalized Additive Model (GAM), and of the variations of baseline, which is estimated by the Autoregressive Conditional Heteroscedastic Model (ARCH). Our proposal on systematically studying the time-dependent variations of energy consumption by ARCH is novel. This is of great importance to, technically, guarantee the resulting PIs of baseline is valid and, practically, to reduce the energy cost incurred by oscillation. As a key component of anomaly detection algorithm, we propose to use the residual based bootstrap for the construction of PIs to minimize the bias caused by imposing hypothetical distributions on observations. We illustrate the proposed method with a real-life example on building energy consumption throughout the paper and in addition, justify our approach is theoretically consistent.