Machine Learning for the Detection of Electricity Theft

Patrick GLAUNER

SnT - Interdisciplinary Centre for Security, Reliability and Trust,University of Luxembourg

February 21, 2018

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Biography

PhD Student at the University of Luxembourg

Adjunct Lecturer at two German universities

MSc in Machine Learning from Imperial College London

BSc in Computer Science from Karlsruhe University of AppliedSciences

Previously worked at CERN and SAP

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Motivation: how to detect electricity theft?

Figure 1: That is what electricity theft looks.

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Motivation: how to detect electricity theft?

Figure 2: That is what electricity theft looks1.

1http://extra.globo.com/incoming/13321838-a74-9d3/w448/

Eletrotraficante-Rio-das-Pedras.jpgPatrick GLAUNER ML for Electricity Theft Detection February 21, 2018 4 / 35

Motivation: how to detect electricity theft?

Example (Fraudulent behavior)

Figure 3: Two assumed occurrences of NTL due to significant consumption dropsfollowed by inspections (visualized by a vertical bar).

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Agenda

1 Anomaly detection

2 Electricity theft

3 Electricity theft detection

4 Conclusions and outreach

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Anomaly detection

Definition (Anomaly detection)

Anomaly detection allows to find data that does not conform to anexpected pattern. Anomaly detection is used for a very small number ofpositive examples and large number of negative exempts. It is also usedfor many different kinds of anomalies as it is hard for any algorithm tolearn from just a few positive examples what the anomalies might looklike. There may be also future anomalies which may look completelydifferent to any of the anomalous examples learned so far.

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Anomaly detection

Example (Anomaly detection)

Figure 4: Fraud detection in online shoppinga.

ahttps://docs.microsoft.com/en-us/azure/machine-learning/

machine-learning-algorithm-choice

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Anomaly detection

Example (Anomaly detection)

Credit card fraud

Theft of credentials

...

Electricity theft

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Electricity theft

Collaboration with Choice Technologies Holding

Company focuses on providing revenue assurance solutions for energyutilities

More than twenty years of experience in this sector

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Electricity theft

Definition (Technical losses)

Technical losses occur mostly due to power dissipation. This is naturallycaused by internal electrical resistance and the affected componentsinclude generators, transformers and transmission lines.

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Electricity theft

Definition (Non-technical losses)

The opposite class of losses are non-technical losses (NTL), which areprimarily caused by electricity theft. In most countries, NTL account forthe predominant part of the overall losses. Therefore, it is most beneficialto first reduce NTL before reducing technical losses. Nonetheless, reducingtechnical losses is challenging, too. In particular, NTL include, but are notlimited to, the following causes:

Meter tampering in order to record lower consumptions

Bypassing meters by rigging lines from the power source

Arranged false meter readings by bribing meter readers

Faulty or broken meters

Un-metered supply

Technical and human errors in meter readings, data processing andbilling

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Electricity theft

Impact of NTL

NTL cause significant harm to economies, including loss of revenue andprofit of electricity providers, decrease of the stability and reliability ofelectrical power grids and extra use of limited natural resources which inturn increases pollution. There are different estimates of the losses causedby NTL. For example, in India, NTL are estimated at US$ 4.5 billion. NTLalso reported to range up to 40% of the total electricity distributed incountries such as Brazil, India, Malaysia or Lebanon. They are also ofrelevance in developed countries, for example estimates of NTL in the UKand US range from US$ 1-6 billion.

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Electricity theft

Example (Fraudulent behavior)

Figure 5: Two assumed occurrences of NTL due to significant consumption dropfollowed by inspections (visualized by a vertical bar).

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Electricity theft detection

Industrial detection of NTL:

To date, most NTL detection systems deployed in industry are basedon expert knowledge rules

In contrast, the predominant research direction reported in the recentresearch literature is the use of machine learning/data miningmethods, which learn from customer data and known irregularbehavior that was reported through inspection results

Due to the high costs per inspection and the limited number ofpossible inspections, electricity providers aim to maximize the returnon investment (ROI) of inspections

It has previously been shown that the neighborhoods of customersyield significant information in order to decide whether a customercauses a NTL or not

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Electricity theft detection

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Electricity theft detection

The data used in this paper comes from an electricity provider inBrazil

Consists of 3.6M customers

Contains 820K inspection results

There are 195M meter readings from 2011 to 2016

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Electricity theft detection

Extraction of hundreds of features from the consumption time series:

Features specific to NTL detectionGeneric time series features using tsfresh2

Feature selection that handles the noise in this industrial data

Training of various classifiers, random forest proved to perform thebest

2http://github.com/blue-yonder/tsfreshPatrick GLAUNER ML for Electricity Theft Detection February 21, 2018 18 / 35

Electricity theft detection

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Challenges

High costs of false positives

Covariate shift

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Challenges: high costs of false positives

Costs of inspections

We have a very limited number of inspections we can carry out. Eachinspection costs around US$ 100.

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Challenges: high costs of false positives

Figure 6: Microsoft HoloLens3.

3http://www.microsoft.com/en-us/hololensPatrick GLAUNER ML for Electricity Theft Detection February 21, 2018 22 / 35

Challenges: high costs of false positives

Figure 7: Gesture interactions with the spatial hologram allow to select customersas well as to zoom into or rotate holograms. We also provide a future yellow labelthat depicts a borderline case, which requires a manual check by domain experts.

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Challenges: high costs of false positives

Figure 8: Zoomed and rotated view on the spatial hologram.

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Challenges: high costs of false positives

Figure 9: Detailed view of a customer depicted by a green dot predicted to have aregular power consumption pattern.

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Challenges: high costs of false positives

Figure 10: Multi-view on multiple customers’ power consumption history.

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Challenges: covariate shift

Figure 11: Example of spatial bias: The large city is close to the sea, whereas thesmall city is located in the interior of the country. The consumption profiles inboth cities are very different due to different climate. Most customers live in thelarge city and only few customers live in the small city. However, most inspectionsare carried out in the small city, which is denoted by the large magnifying glass.

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Challenges: covariate shift

This happens all the time in Machine Learning and statistics, including:

1936 US Presidential election

Gender bias in Machine Learning

...

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Challenges: covariate shift

Table 1: Global Covariate Shift of Single Features.

Feature MCCmax σ

Location 0.22367 0.03453

Class 0.16255 0.01371

Number of wires 0.14111 0.00794

Meter type 0.13158 0.00382

Voltage 0.07092 0.02375

Contract status 0.03744 0.09183

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Challenges: covariate shift

Figure 12: Municipal level.

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Work covered in New Scientist in September 2017

http://www.newscientist.com/article/2148308-ai-could-put-a-stop-to-electricity-theft-and-meter-misreadings/

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Cited by McKinsey study in June 2017

http://www.mckinsey.de/files/170620 studie ai.pdfPatrick GLAUNER ML for Electricity Theft Detection February 21, 2018 32 / 35

Conclusions and outreach

Non-technical losses (NTL) cause major financial losses to electricitysuppliers

Detecting NTL thrives significant economic value

Superiority performance of machine learning approaches compared toexpert system

Many challenges: costs of false positives, covariate shift, ...

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Bibliography I

[1] P. Glauner, J. Meira, P. Valtchev, R. State and F. Bettinger, “TheChallenge of Non-Technical Loss Detection using ArtificialIntelligence: A Survey”, International Journal of ComputationalIntelligence Systems (IJCIS), vol. 10, issue 1, pp. 760-775, 2017.

[2] P. Glauner, N. Dahringer, O. Puhachov, J. Meira, P. Valtchev, R.State and D. Duarte, “Identifying Irregular Power Usage byTurning Predictions into Holographic Spatial Visualizations”,Proceedings of the 17th IEEE International Conference on DataMining Workshops (ICDMW 2017), New Orleans, USA, 2017.

[3] P. Glauner, A. Migliosi, J. Meria, P. Valtchev, R. State and F.Bettinger, “Is Big Data Sufficient for a Reliable Detection ofNon-Technical Losses?”, Proceedings of the 19th InternationalConference on Intelligent System Applications to Power Systems(ISAP 2017), San Antonio, USA, 2017.

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Bibliography II

[4] P. Glauner, A. Boechat, L. Dolberg, R. State, F. Bettinger, Y.Rangoni and D. Duarte, “Large-Scale Detection ofNon-Technical Losses in Imbalanced Data Sets”, Proceedings ofthe Seventh IEEE Conference on Innovative Smart Grid Technologies(ISGT 2016), Minneapolis, USA, 2016.

[5] P. Glauner, J. Meira, L. Dolberg, R. State, F. Bettinger, Y.Rangoni and D. Duarte, “Neighborhood Features HelpDetecting Non-Technical Losses in Big Data Sets”, Proceedingsof the 3rd IEEE/ACM International Conference on Big DataComputing Applications and Technologies (BDCAT 2016),Shanghai, China, 2016.

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