A Novel Approach for Anomaly Detection in Power

Consumption Data

C. Chahla

, H. Snoussi

, L. Merghem

and M. Esseghir

University of Technology of Troyes, Institute Charles Delaunay-LM2S, Troyes, France

University of Technology of Troyes, Institute Charles Delaunay-ERA, Troyes, France

Keywords: Anomaly Detection, K-means, Auto-Encoders, LSTM, Power Consumption, Big Data.

Abstract: Anomalies are patterns in data that do not follow the expected behaviour and they are rarely encountered.

Anomaly detection has been widely used within diverse research areas such as credit card fraud detection,

image processing, and many other application domains. In this paper, we focus on detecting anomalies in

power consumption data. The identification of unusual behaviours is important in order to foresee

uncommon events and to improve energy efficiency. To this end, we propose a model to precisely identify

anomalous days and another one to localize the detected anomalies. Normal days are identified using a

simple Auto-Encoder reconstruction technique, whereas the localization of the anomaly throughout the day

is performed using a combination of LSTM and K-means algorithms. This hybrid model that combines

prediction and clustering techniques, permits to detect unusual behaviour based on the assumption that

identical daily consumption can appear repeatedly due to users’ living habits. The model is evaluated using

real-world power consumption data collected from Pecanstreet in the United States.

1 INTRODUCTION

Global demand for energy is rising, and the lack of

energy resources such as oil, has hindered the

progress of global economies (F. Jovane et al.,2008).

Improving the efficiency of power consumption is of

great importance, since the increase of energy

consumption may become environmentally

hazardous enhancing global warming (S. Bilgen,

2014) (F. Jovane et al., 2008). One promising

approach to improve energy efficiency is to identify

anomalies in building energy consumption. This

information can be useful to the building managers

in order to reduce wasting energies by applying

energy saving procedures.

The large amount of data generated makes the

problem of detecting anomalies and localizing it

very challenging. Although many studies have been

conducted to propose low energy buildings,

buildings often exceed the energy saving objectives

indicated by the buildings’ energy design. Thus,

building administrators want to know how to

minimize the failure rate and how to discover power

consumption measurements highly differing from

old observed data.

Figure 1: An example of an anomaly in a typical

household energy consumption.

In recent years, the weather conditions affected

the electric power demand, especially in heating and

cooling systems. Nowadays, it is widely accepted

that anomaly detection is of paramount importance

to reduce energy waste. Anomaly detection can be

used for example to determine if there is a faulty

equipment consuming more power than required.

Moreover, it can be used to detect power theft and

intrusions. Fig. 1 shows an example of an anomaly

in power consumption data, where a considerable

difference between the actual consumption and the

expected consumption can be detected (X. Liu et al.,

2016).

Chahla, C., Snoussi, H., Merghem, L. and Esseghir, M.

A Novel Approach for Anomaly Detection in Power Consumption Data.

DOI: 10.5220/0007361704830490

In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2019), pages 483-490

ISBN: 978-989-758-351-3

483

In this paper, we build an anomaly detection

mechanism to improve energy efficiency and to

detect abnormal behaviours. In particular, we

propose to use machine learning techniques to detect

anomalous days in order to avoid taking them into

account when building models representing the

normal behaviour of the users. K-means algorithm

are used to learn different scenarios representing the

energy consumption behaviour of each user, and

LSTM (Long Short Term Memory) is used to

predict the power consumption of the next hour.

Identifying outliers not only has the benefit to detect

abnormal events like power theft or a faulty

equipment, it can also be used as indicator for

residents to help them to change their living habits

and to warn them of device failures.

The rest of the paper is organized as follows:

Section 2 introduces the related work. Our proposed

framework as well as the experimental results are

presented in section 3. Section 4 concludes the paper

and provides the direction for the future works.

2 RELATED WORK

Anomaly detection also known as outlier detection is

the process of discovering patterns in data that do

not conform to expected behaviour (V. Chandola et

al., 2007). There is a tremendous research being

performed in anomaly detection in a wide variety of

application domains. Table 1 gives a summary of

anomaly detection categories (L. Li, 2013).

Table 1: Anomaly detection categories.

Problem Categories

Input

Binary, Univariate,

Multivariate, time series,

continuous…

Supervision

Supervised, Unsupervised,

semi-supervise

Anomalies

Pattern anomalies, context

anomalies, point anomalies,

correlation anomalies

In this paper, the data provided is not annotated

and cannot be annotated manually. Therefore the

approach we develop here is unsupervised. The

input of the algorithm is univariate and time series

and the outliers are context anomalies and point

anomalies. Context anomalies are data that are

considered abnormal in one context but normal in

another. For example, a lighting source in a school

might be anomalous on weekends but not on

weekdays when there are classes. Point anomalies

occur when an instance is considered abnormal

compared to the rest of the data.

Several previous studies utilized historical

building power data to detect anomalies. Methods

used for these unsupervised anomaly detection

problems include: nearest neighbour, clustering and

information theory. The nearest neighbour

approaches try to analyse the neighbourhood of each

sample to determine if it’s normal or not. The

distance is calculated between samples, and

anomalies are detected based on the assumption that

samples with anomalies are distant form normal

samples (V. Chandola et al., 2009). These

approaches have the advantage of being applicable

without making assumptions on the data

distributions. The main disadvantage of these

techniques remains in the fact that the assumption

that samples with anomalies have no close

neighbours is not always true.

Clustering approaches can also be applied for

outlier detection. In (V. Chandola et al., 2009), a

detailed review of these approaches has been

presented. This approach is based on the idea that

anomalies will not fit into any cluster or they will

make sparse clusters. Moreover, even if they fit in a

particular cluster, they will be distant from its

centroid. The main disadvantage of these approaches

is the computational complexity. Finally, the

approaches based on information theory calculate

entropy or Kolomogorov complexity. The

performance of these approaches are determined by

the choice of the theoretic measures.

2.1 Auto-Encoder

Figure 2: Auto-Encoder.

The Auto-Encoder is an artificial neural network that

tries to reproduce input vectors {x1, x2,…, xm} as

ICPRAM 2019 - 8th International Conference on Pattern Recognition Applications and Methods

484

output vectors {x

1, x

2,…, x

m} (Sakurada et al.,

2014). An example of an Auto-Encoder is presented

in Fig.2. L1 and L5 are the input and the output

layers respectively whereas the others are hidden

layers. Supposing the vectors representing each

samples are made of D variables, the loss function

used for reconstructions is presented Eq. (1):

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







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 (1)

Eq. (2) represents the activation of unit k in layer l,

the sum is calculated in the (l-1) layer over all

neurons j:

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(2)

Where b and W are the bias and weight parameters,

respectively.

2.2 K-means Algorithm

K-mean is another well-known unsupervised

algorithm used for anomaly detection. Clustering

vectors is grouping these vectors according to some

characteristics. Given {x1, x2,…, xn}, this algorithm

searches to attribute each sample to one of the k

clusters that minimizes the distance between the data

point and the cluster:

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(3)

The algorithm summarizing k-means is presented in

Algorithm 1.

2.3 LSTM

Long Short Term Memory (LSTM) (Hochreiter et

al., 1997) is one of the most popular Recurrent

Neural Networks (RNN). It has presented an

effective and scalable model for several learning

problems related to sequential data by modelling

long range dependencies. For the t-th element in a

sequence, the LSTM takes as input the element x

the previous output h

t-1

and cell state c

t-1

and

computes the next output h

and cell state c

. Both h

and c are initialized with zeros.

3 PROPOSED METHOD AND

EXPERIMENTAL RESULTS

This research proposes a combination of recurrent

neural networks and clustering methods in order to

detect and predict anomalies in power consumption

data. We begin by building a LSTM with three

hidden layers and we trained it to predict the power

consumption of the next hour using the data of the

last 24 hours using Adam optimizer. We trained it

for up to 40 epochs with a batch size equal to 64.

The learning rate was set to 0.0001 and a dropout of

0.2 was applied at the output of each of the three

hidden layers.

The predicted value using LSTM is going to be

used to form a vector representing the power

consumption of the last 24 hours, then compared to

all the possible scenarios defining the typical normal

behaviour of this user at this specific hour. Thus, the

second step of our work is to learn these typical

behaviour scenarios using the k-means algorithm.

We start by applying some data rearrangement in

order to generate 24 groups representing the 24

hours of the day. Data rearrangement permits to

represent each input instance using sliding window

instead of single consumption value. To avoid

unpleasant impact of missing real world data, some

pre-processing techniques are used to adapt data to

the algorithms used. All values less than zero are

considered noisy as well as all the data points lying

further than 3 box lengths in the boxplot

representing the overall power consumption of the

user.

Each hour is represented by a vector taking the

last 24 hours of power consumption data. Then in

each group, we apply the k-means algorithm in order

to represent each hour by several possible

behaviours. The value k for the clustering was set to

11. In other words, for each hour the user can have

11 different behaviours that can be considered

normal. Using this technique, we can predict

anomalies one hour before its occurrence. The

predicted value generated by the LSTM take in

consideration the changes in behaviours based on the

assumption that recent data weights more than old

data. And the k-mean algorithm searches for the

closest centroids representing the power

consumption at that time. When the predicted value

is higher than the measured one by a threshold

margin, an anomaly is detected. The threshold used

for this user was set to 13.

A Novel Approach for Anomaly Detection in Power Consumption Data

485

Figure 3: General characteristics of power consumption data (Weekdays and Weekends) using Boxplot.

Figure 4: Power consumption data for Day0 to Day8.

3.1 Database

The real-world data used in this work are collected

from Pecanstreet’s dataport (https://

dataport.pecanstreet.org). This database works on

smart meters owned by Pecan Street to provide real

data for researchers around the world. The total

database contains data from 67 devices in 820

households. The devices include lights plugs,

refrigerators, microwaves, air conditioners, ovens

heaters… A data sample is shown in Table 2 where

the ID represents the ID of the user, hour represents

the time of the reading, grid represents the energy

used in the grid, bedroom represents the energy used

by bedroom, and refrigerator represents the energy

used by the refrigerator. In this paper we used the

total usage of the user with 1 hour resolution. We

randomly chose one user (ID 59) during the period

January 2017 to December 2017. Fig. 3 presents an

illustration of the power consumption of this user

using Boxplots. The upper part represents Weekdays

consumption and the lower part represents the

Weekends. As can be seen, this user tends to use

more energy in weekdays. The peak hours are

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486

mainly at 11 o’clock and 12 o’clock. In the

following, we only use the data representing

weekdays.

Table 2: Data sample.

ID hour Bedroom Grid Refrigerator

499 2015-05-

01 07:00

0 0.67 0.2

499 2015-05-

01 08:00

0 0.39 0.12

499 2015-05-

01 09:00

0 0.04 0.17

3.2 Results and Discussions

Figure 5: Visualization of different test days using Auto-

encoders reconstruction. Different colors represent the

reconstruction MSE.

We applied two different approach for two different

scenarios. The first one is trying to detect anomalous

days without localizing the anomaly. This can be

useful to building managers to better understand

consumers’ behaviors and for making energy

efficient home improvements. Fig. 4 represents the

actual power consumption values of the test days.

Fig. 5 shows a visualization of the Auto-Encoder

reconstruction with 5 hidden layers as following [24,

50, 20, 2, 20, 50, 24]. Different colors represent the

reconstruction MSE (Mean Square Error). The

reconstruction error is then compared with a

threshold in order to determine if the day in normal

or not. For a threshold of 0.04, only days 4, 6 and 7

are considered as anomalous as can be seen in Fig.

In the second scenario, we tried to localize the

anomaly using the method we proposed in section 3.

Fig. 6, Fig.7 and Fig. 8 illustrate the results of our

proposed method on the same test days. As can be

seen, our method localized two anomalies for day 4

(at 8 a.m. and 10 a.m.) and one anomaly for day 7

(at 10 a.m.). This can explain why days 4 and 7 have

been considered anomalous by the auto encoders and

can also explain day 4 has a higher reconstruction

error than day 7 since day 4 has 2 anomalies

whereas day 7 has only one anomaly. Contrary to

the Auto-Encoder that considered the day number 6

as anomalous, our method conserves a consistent

overall prediction as can be seen in Fig.7.

4 CONCLUSION

Finding anomalies in time series data is a very

promising topic permitting to reduce the waste of

energy and to better monitor building energy

consumption. In this paper, we present a hybrid

model combining LSTM and K-means algorithm in

order to detect outliers in time series data. Auto-

Encoders detects abnormal days, whereas the

proposed algorithm identifies the typical scenario

permitting to localize the detected anomalies.

Despite of these encouraging results, this work

needs the assistance of real expert users and analysts

in order to better define the anomaly in this domain.

Experts can also provide some annotations for the

learning data in order to give us the possibility of

applying semi-supervised approaches in this domain.

ACKNOWLEDGEMENTS

This work has been supported by SOLOTEC project

which is financed by the European Union and

Champagne Ardenne region.

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Figure 6: Power Consumption values for five test days (Day0 to Day3). The actual power consumption, the predicted one

and the anomalies are presented.

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Figure 7: Power Consumption values for four test days (Day4 to Day7). The actual power consumption, the predicted one

and the anomalies are presented.

A Novel Approach for Anomaly Detection in Power Consumption Data

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Figure 8: Power Consumption values for four test days (Day8). The actual power consumption, the predicted one and the

anomalies are presented.

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