TITLE:
Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics
AUTHORS:
Patrick Krawiec, Mark Junge, Jens Hesselbach
KEYWORDS:
Energy Efficiency, Anomaly Detection, Load Profiles, LSTM, PEWMA
JOURNAL NAME:
Open Journal of Energy Efficiency,
Vol.10 No.2,
April
30,
2021
ABSTRACT: 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, Limón 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 Limón GmbH. The
evaluation shows that for strategy 1 a maximum F1-value of
0.4 (1 M) and for all examined companies an average F1-value
of maximum 0.24 and standard deviation of 0.09 (1 M) could be achieved for the investigation on single residuals. In
variant 3 M the highest F1-value could be achieved with an
average F1-value of 0.21 and standard deviation of 0.06 (3 M) for summed residuals of the partial sequence length of four hours. The
PEWMA-based strategy 2 did not show a higher anomaly detection efficacy
compared to strategy 1 in any of the investigated companies.