=Paper=
{{Paper
|id=Vol-3375/paper8
|storemode=property
|title=Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach
|pdfUrl=https://ceur-ws.org/Vol-3375/paper8.pdf
|volume=Vol-3375
|authors=Somayeh Zamani,Hamed Talebi,Gunnar Stevens
|dblpUrl=https://dblp.org/rec/conf/amlts/ZamaniTS22
}}
==Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach==
https://ceur-ws.org/Vol-3375/paper8.pdf
Time Series Anomaly Detection in Smart Homes: A Deep
Learning Approach
Somayeh Zamani1,* , Hamed Talebi2 and Gunnar Stevens1
1
University of Siegen, Siegen, Germany
2
Amirkabir University of Technology, Tehran, Iran
Abstract
Fixing energy leakage caused by different anomalies can result in significant energy savings and extended appliance life.
Further, it assists grid operators in scheduling their resources to meet the actual needs of end users, while helping end users
reduce their energy costs. In this paper, we analyze the patterns pertaining to the power consumption of dishwashers used in
two houses of the REFIT dataset. Then two autoencoder (AE) architectures with 1D-CNN and TCN as backbones are trained
to differentiate the normal patterns from the abnormal ones. Our results indicate that TCN outperforms CNN1D in detecting
anomalies in energy consumption. Finally, the data from the Fridge_Freezer and the Freezer of house No. 3 in REFIT is also
used to evaluate our approach.
Keywords
Time series, Anomaly detection, Deep learning, Autoencoder, Temporal convolutional networks, Smart home, Sustainability
1. Introduction higher power consumption or damage in the most critical
cases [8].
Throughout recent years, the energy demand has signifi- Thus, for optimization purposes in smart homes, via
cantly gone up due to urban and industrial development implementing load monitoring systems and formulating
alongside an increase in population [1]. Therefore, cli- smart anomaly detection models using machine learning
mate change, global warming, and volatility in energy techniques, the abnormality can be mitigated [9]. To do
prices have fuelled the interest in smart systems [2]. In so, it is essential to analyze the energy consumption of
this regard, the huge potential increase of replacing tra- households in order to identify consumption patterns and
ditional home appliances with new in-operation power- extract valuable information from smart homes [8][10].
consuming ones by 2040 has caused the residential sector In this paper, the power consumption patterns of dish-
to account for roughly 60% over 2017-25 and 70% over washers used in houses No. 1 and 2 of the REFIT dataset
2025-40 of electricity demand increase of buildings. As are analyzed as examples of devices that are used based
such, household appliances need to be operating effi- on the needs of users. Then, the data for each usage of
ciently and used appropriately to achieve energy-saving the device is divided into different signals to properly
goals [3]. train autoencoders with different backbones, including
To this end, utilizing AI-based technologies and smart 1-dimensional CNN and TCN, to detect abnormal usage.
homes as novel interventions to recognize abnormal For this purpose, any predicted value that is greater than
power utilization and understand the reasons for each ab- twice the standard deviation of the electricity consump-
normality could pave the way for end-consumers both to tion the day before is considered abnormal.
renovate wasteful devices and adopt a more sustainable The rest of the paper is organized as follows. We pro-
energy consumption behavior [4][5][6][7]. Moreover, it vide related work on anomaly detection in energy con-
facilitates the prediction of end-users power demand as sumption in Section 2. In Section 3, our methodology is
well as performing an optimal energy distribution by presented in details. Section 4 concludes the paper and
grid operators depending on specific end-usersβ needs. discusses future work.
In addition, electrical anomalies are less likely to remain
unnoticed for a long period of time which would result in
2. Related work
AMLTSβ22: Workshop on Applied Machine Learning Methods for Time
Series Forecasting, co-located with the 31st ACM International Con- In the context of energy usage, anomalies are defined as
ference on Information and Knowledge Management (CIKM), October
deviations from expected behavior that occur when the
17-21, 2022, Atlanta, USA
*
Corresponding author. consumption of a household appliance does not corre-
$ somayeh.zamani@uni-siegen.de (S. Zamani); spond with its normal pattern [3][11]. Among the key
hamed.talebi.aut@gmail.com (H. Talebi); applications of anomaly detection by load monitoring,
gunnar.stevens@uni-siegen.de (G. Stevens) are forecasting maintenance and energy efficiency [12].
Β© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). Thus, a smart plug, smart appliance, and other appliance-
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
method, the feature representations are enforced to learn
important regularities of the data so that reconstruction
errors are minimized. Consequently, anomalies are dif-
ficult to reconstruct from the resulting representations
and are, therefore, subject to large reconstruction errors
[17].
3. Methodology
3.1. Dataset and preprocessing
The REFIT Electrical Load Measurements dataset con-
tain cleaned electrical consumption data in Watts for 20
households in the UK at both the aggregate and appli-
Figure 1: The architecture of the CNN-based autoencoder ance level. The data is related to a period of two years
(CNN-AE) comprising nine individual appliance measurements at
8-second intervals per house with 1,194,958,790 readings
[16]. The models proposed in this paper are trained using
level monitoring devices are needed to continuously mon- dishwasher data from houses No. 1 and 2. Furthermore,
itor the power consumption of individual appliances in data from the Fridge_Freezer and the Freezer of house
a house [8]. However, identifying anomalies, and their No. 3 is used to assess the effectiveness of our approach.
nature of them should also be considered, which can be To begin with, it is necessary to resample the data
categorized, based on different dimensions. In the data to convert it into equal time intervals r. Then using
science world, anomalies are seen as either single points the following formula, the average sampling time, π‘ of
that are not necessarily relevant to each other or a set the REFIT data is used to fill in a limited number of
of data points that constitute a pattern and, therefore, signals, n with no data. The remaining empty intervals
can be interpreted in relation to each other. The other are substituted with zero.
dimension of anomaly detection that should be taken
[οΈ ]οΈ
4*π‘
into account is the context which refers to a deviation in π= (1)
π
a particular context relating to the structure of the data
[3][13][14]. For example, in the context of a warm sea- Additionally, for devices used according to usersβ needs,
son, a temperature report of -30 degrees Celsius can be consumption data must first be differentiated. The power
anomalous; however, during a cold season, such a report consumption pattern may include turning the device on
may be more common [3]. To this end, understanding and off several times per usage. The matching data is
the available data will provide a solid foundation for im- therefore combined into relevant signals. Also, due to
proving energy efficiency. For this purpose, there are the possibility of failure in some devices that can result
thirty-one publicly available databases with several fea- in constant operation for an extended period, we assume
tures, such as the geographical location, period of col- a maximum period for a device to operate.
lection, number of monitored households, the sampling
rate of collected data, and number of sub-metered appli-
ances [15]. Regarding this, a valuable dataset is REFIT
which includes cleaned electrical consumption in Watts
for 20 households in the UK at both the aggregate and
appliance levels [16]. On the other hand, Pang [11] has
provided a comprehensive overview of current anomaly
detection methods to gain an important understanding of
their inherent capabilities and limitations in addressing
some largely unsolved challenges in anomaly detection. Figure 2: The architecture of the TCN-based autoencoder
According to his study, Autoencoders, which are a sub- (TCN-AE)
set of the generic normality feature learning category,
aim to learn some low-dimensional feature representa-
tion space on which the given data instances can be well 3.2. The proposed models
reconstructed. While this is a widely used method for
data compression or dimension reduction, by using this The development of time series anomaly detection al-
gorithms has recently received considerable attention.
Autoencoder-based approaches are often used to identify by a factor of π . To do so, groups of size π are averaged
anomalous behavior by analyzing the reconstruction er- along the time axis.
ror of the data [18][19][20]. Having learned a nonlinear In the decoder module, the downsampled sequence
transformation of the input data into a compressed rep- is returned to its original length by performing a near-
resentation, latent variables are used to reconstruct the est neighbor interpolation on the upsampled sequence.
original input. On the other hand, utilizing the convolu- Upsampled sequences are passed through a second TCN
tion mechanism in sequential models is computationally with independent weights parameterized similarly to the
optimal [21]. Also, due to CNNsβ equivariance proper- encoder-TCN. As a final step, the input sequence is re-
ties and sparse interactions, they are translated from constructed with a Conv1D layer that ensures that the
computer vision into the time domain using temporal dimensionality of the input is matched (by setting π = 1
convolutional networks (TCN)[20]. and ππ πππ‘πππ = π) [20]. As described in the next section,
In the following sections, we will describe how we the input sequence and its reconstruction will be used
used autoencoders (AEs) for time series data that utilize for detecting anomalies after TCN-AE has been trained.
1-dimensional CNNs and TCNs as building blocks to
detect energy anomalies in the REFIT dataset.
3.2.1. CNN-based autoencoder (CNN-AE)
We used TensorFlow to implement the architecture con-
sisting of two smaller sequential models, an encoder and
a decoder. Also, considering the speed of the model con-
vergence, our CNN-based autoencoder is comprised of
3 layers of Conv1D using the data of the householdsβ
dishwashers. Furthermore, a nonlinear ReLu activation
function is used in each convolution layer. In this model,
a standard rate of 0.2 is considered for the dropout layer
to randomly remove 20% of the upper layer during learn-
ing.
Figure 1. shows the layers and the number of input and
output parameters of each. The input layer is 320 Γ 1 Figure 3: (a) Examples of the normal energy consumption
(3200 seconds), calculated according to the maximum of the dishwasher (b) Examples of the abnormal energy con-
operation time of the device. sumption of the dishwasher
3.2.2. TCN-based autoencoder (TCN-AE)
The temporal convolutional network (TCN) combines 3.3. Experimental results
simplicity with auto-regressive prediction, residual 3.3.1. Anomaly detection
blocks, and a very long memory. In general, a TCN can
be broken down into three components: a list of dilation We compute a threshold value of 2π above the predicted
rates π· = {π1 , π2 , ..., πππ }, the number of ππ πππ‘πππ , and value to measure the trend in electricity consumption
the kernel size π, which is the same for all filters in a TCN over time, where 2π is the standard deviation on the
[20][22]. Inspired by a classical (deep) autoencoder, the day before the actual moment [23]. An abnormal state is
TCN autoencoder encodes sequences, along the temporal defined as a value exceeding the threshold for predicted
axis, of length π into a compressed representation of electricity consumption at the actual moment. Equations
length π /π (where π β Z+ ) and then tries to reconstruct (2) and (3) show the calculation of π and πΎπ‘βπππ βπππ :
the original sequence[20]. βοΈ βοΈπ
π=1 (π₯π β π₯)
2
In Figure 2, each layer of the TCN-AE is described π= (2)
by its parameters within the box. TCN-AE receives a π
sequence π₯[π] of length π and dimensionality π as its πΎπ‘βπππ βπππ = πΎ
ΜοΈ + 2π (3)
input. Using a TCN, the encoder first processes input
sequence π₯[π] of length π and dimension π. Afterward, a where π is the standard deviation, πΎπ‘βπππ βπππ is the
one-dimensional convolutional layer with π = 1, π = 1, threshold, πΎ
ΜοΈ is the predicted value, π₯π is the electricity
and ππ πππ‘πππ = 8 is used to reduce the dimensionality of consumption, π₯ is the average electricity consumption,
the TCNβs output. As the last layer in the encoder, the and π is the number of samples.
temporal average pooling layer downsamples the series A normal electricity usage pattern detection for the
dishwasher is shown in Figure 3 (a), where the real-time
Table 1
Evaluation of CNN1D-AE performance with a different data division ratio
Model Division Ratio MAE MAPE
CNN1D-AE 9:1 0.1781 %22.55
CNN1D-AE 8:2 0.1570 %20.07
CNN1D-AE 7:3 0.1702 %21.99
Table 2
Evaluation of TCN-AE performance with a different data division ratio
Model Division Ratio MAE MAPE
TCN-AE 9:1 0.1527 %21.39
TCN-AE 8:2 0.1371 %17.52
TCN-AE 7:3 0.1412 %19.88
threshold curve follows the sequence trend, indicating tection techniques, abnormal behaviors can be mitigated.
the model depicts the dishwasherβs normal electricity us- This is possible, especially if user-centric explainable
age. As can be seen in the figure, the real power consump- recommender systems are combined with anomaly de-
tion curve does not exceed the threshold range, which tection modules. However, there is no proper labeled
indicates a normal level of electricity consumption. As dataset available to develop accurate algorithms or de-
shown in Figure 3 (b), anomalous consumption patterns tect different types of anomalies. Accordingly, we plan to
occur when actual values exceed the threshold. Conse- run a laboratory to build the first appropriately labeled
quently, the method can distinguish between normal and energy anomaly dataset.
abnormal consumption behavior.
3.3.2. Evaluation
Acknowledgments
As Table 1. and Table 2. show, for both architectures, the This research has been funded by the EU Horizon 2020
best performance is obtained with the data division ratio Marie SkΕodowska-Curie International Training Network
of 8:2, and clearly, TCN-AE is more efficient than CNN- GECKO, Grant number 955422.
AE. Our unsupervised approach has also been evaluated
using the data from the Fridge_Freezer and the Freezer
of house No. 3 in REFIT. The results in Table 3. confirm
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