2. Contents
1. Introduction of project
2. Problem statement
3. Scope of project
4. Methodology
5. Literature Survey
6. Propose System
7. System Architecture
8. Limitations, advantage, disadvantage
9. Conclusion
Project Group No: 38 ELECTRICITY THEFT DETECTION 2
3. Introduction of project
โข Many electric utilities have financial loss due to electricity theft.
โข Here are various types of electrical power theft, including Tapping a line or bypassing the
energy meter.
โข According to a study [citation needed], 80% of worldwide theft occurs in private dwellings
and 20% on commercial and industrial premises.
โข From user fundamental data it is easy task to analyze user behavior.
โข we implement a supervised ML-based theft detection model that identifies whether an
abnormal/fraudulent usage pattern has occurred in the SG(smart grid)meter.
4. Problem Statement
โข India loses more money to theft than any other country in the world.
โข In this proposed system we use dataset having electricity usage of a smart grid (SG) meter
(or simply smart meter).
โข Using this dataset we does feature selection and preprocessing on dataset.
โข When we have large number of features in dataset then feature selection is very important
part in our Machine Learning.
โข As we use feature selection it gives us most important feature and this feature selection
gives us more accuracy.
5. Scope of Project
โข Cost-Benefit Analysis
โข Risk Assessment And solutions
โข Control Efficiency And Services
โข Timeline Estimations And optimizing resources
โข Scope of project includes we can use this in electricity utilities.
โข to save Electricity
โข The project model reduces the manual manipulation work and theft.
โข The government saves the money by the control of theft in energy and also beneficial for
customer side and the government side.
โข The metering IC ensures the accurate and reliable measurement of power consumed. Cost
wise low
6. Methodology
โข In this proposed system we use dataset having electricity usage of a smart meter.
โข Using this dataset we does feature selection and preprocessing on dataset.
โข When we have large number of features in dataset then feature selection is very important part in our
Machine Learning.
โข As we use feature selection it gives us most important feature and this feature selection gives us more
accuracy.
โข Then we perform the preprocessing on that data.
โข After that we use the superiority of XGBoost, a gradient boosting classifier (GBC), over other ML
algorithms for nontechnical loss (NTL) detection
โข Gradient boosting is called gradient boosting because it uses a gradient descent algorithm to minimize
loss when adding new trees.
โข This approach supports both regression and classification predictive
7. Literature Survey
Sr.NO Title Of The
Project
Year Method Metric Used Usage
1 PPETD: Privacy-
Preserving
Electricity Theft
Detection Scheme
with Load
Monitoring and
Billing for AMI
Networks.
2019 CNN-GRU algorithm Accuracy of theft
detection is 93.2%
Used to classify normal users
and electricity thefts.
2 Electricity theft
detection based on
extreme gradient
boosting in AMI
2020 SVM Algorithm Accuracy of 89.2% To detect electricity thefts
among dataset.
3 Electricity theft
detection in AMI
Based on Clustering
and local Outlier
Factor.
2021 Using Clustering and Local
Outlier Factor(LOF)
Nearly 12% people
in area were
electricity thefts on
average.
To reduce losses due to
electricity thefts.
8. Literature Survey
Sr. No Title Of The
Project
Year Method Metric Used Usage
4 Deep Attention-
based Neural
Network for
Electricity Theft
Detection
2020 CNN(Deep
Learning )
Accuracy of theft
detection is more
than 90%
To make electricity theft
detection system more
effective.
5 Research and
Implementation of
Current Detection
Technology for
Electricity Stealing
and Omission .
2021 XGBoost
Algorithm
Accuracy of 87.2%
and 83.5 on
training and testing
data.
To detect electricity thefts
among dataset.
9. Proposed System
โข India loses more money to theft than any other country in the world.
โข In this proposed system we use dataset having electricity usage of a smart grid (SG) meter
(or simply smart meter).
โข Using this dataset we does feature selection and preprocessing on dataset.
โข When we have large number of features in dataset then feature selection is very important
part in our Machine Learning.
Project Group No: 38 ELECTRICITY THEFT DETECTION 9
17. Limitations
โข Billing system fails if no GSM network coverage.
โข Charges may be applicable for network use.
โข Requires fixed GSM number.
18. Advantages
โข This method reduce energy wastage save lot of energy for future use.
โข We can detect the location from where the power is being stolen which was not possible
before.
โข Customized rates and billing dates.
โข Accurate information from network load to optimize maintenance and investigation.
19. Disadvantages
โข If Implemented on large scale it may take a lot of time and manual input
โข It is not Capable of Detecting the exact location
โข It is costly
20. Conclusion
Project Group No: 38 ELECTRICITY THEFT DETECTION
โข It helps electricity utilities to detect electricity theft and they will not have to bare loss.
โข This proposed system detects the electricity theft using xg boost machine learning
method.
โข However, Light GBM appeared to be the fastest classifier.
โข This proposed system helps to electricity utilities to detect electricity theft and they will not
have to bare loss. This is most important application of this project.
20
21. References
Project Group No: 38 ELECTRICITY THEFT DETECTION
1.J. Nagi, K. Yap, S. Tiong, S. Ahmed, and M. Mohamad, โNontechnical loss de-
tection for metered customers in power utility using support vector machines,โ
IEEE Transactions on Power Delivery, vol. 25, no. 2, pp. 1162โ1171, 2010,
cited By 104.
2 .S. McLaughlin, D. Podkuiko, and P. McDaniel, โEnergy theft in the advanced
metering infrastructure,โ Lecture Notes in Computer Science (including sub-
series Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinfor-
matics), vol. 6027 LNCS, pp. 176โ187, 2010, cited By 99.
3. G. Tsekouras, N. Hatziargyriou, and E. Dialynas, โTwostage pattern recogni-
tion of load curves for classification of electricity customers,โ IEEE Transac-
tions on Power Systems, vol. 22, no. 3, pp. 1120โ1128, 2007, cited By 122.
[Online].
4. Y. Zhang, W. Chen, and J. Black, โAnomaly detection in premise energy con-
sumption data,โ 2011, cited By 12. [Online].
5. S. Dua and X. Du, Data Mining and Machine Learning in Cybersecurity, 1st
ed. Boston, MA, USA: Auerbach Publications, 2011.
21
