Paper Info
Title
Statistical Anomaly Detection in Mean and Variation of Energy Consumption
Abstract
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.
Year
DOI
Venue
2014
10.1109/ICPR.2014.614
ICPR
Keywords
DocType
ISSN
statistical distributions,pi,building energy consumption,autoregressive processes,hypothetical distributions,energy management systems,energy consumption,baseline prediction interval,arch,autoregressive conditional heteroscedastic model,gam,statistical anomaly detection,abnormal energy usage detection,statistical predictive method,building management systems,generalized additive model
Conference
1051-4651
Citations 
PageRank 
References 
1
0.38
0
Authors
4
Name
Order
Citations
PageRank
Bei Chen1269.11
Mathieu Sinn2103.65
Joern Ploennigs330032.75
Anika Schumann410313.12