%0 Journal Article
%T Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics
%A Patrick Krawiec
%A Mark Junge
%A Jens Hesselbach
%J Open Journal of Energy Efficiency
%P 37-49
%@ 2169-2645
%D 2021
%I Scientific Research Publishing
%R 10.4236/ojee.2020.102003
%X The
Federal Office for Economic Affairs and Export Control (BAFA) of Germany promotes digital
concepts for increasing energy efficiency as part of the ¡°Pilotprogramm
Einsparzähler¡±. Within this program, Lim¨®n GmbH is developing software
solutions in cooperation with the University of Kassel to identify efficiency
potentials in load profiles by means of automated anomaly detection. Therefore,
in this study two strategies for anomaly detection in load profiles are
evaluated. To estimate the monthly load profile, strategy 1 uses the artificial
neural network LSTM (Long Short-Term Memory), with a data period of one month
(1 M) or three months (3 M), and strategy 2
uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving
Average). By comparing with original load profile data, residuals or summed
residuals of the sequence lengths of two, four, six and eight hours are
identified as an anomaly by exceeding a predefined threshold. The thresholds
are defined by the Z-Score test, i.e.,
residuals greater than 2, 2.5 or 3 standard deviations are considered
anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to
set thresholds by means of three significance level values of 0.05, 0.10 and
0.15, with a maximum of k = 40 iterations. Five load profiles are
examined, which were obtained by the cluster method k-Means as a
representative sample from all available data sets of the Lim¨®n GmbH. The
evaluation shows that for strategy 1 a maximum F1