IEEE International Conference Engineering Veracruz 2020

1. Machine Learning Model for the
Detection of Electric Energy Fraud
Using and Edge-Fog Computing
Architecture
Juan C. Olivares Rojas, Enrique Reyes Archundia, Noel
E. Rodríguez Maya, José A. Gutiérrez Gnecchi, Ismael
Molina Moreno, Jaime Cerda Jacobo

2. The importance of electricity

3. Industry 4.0

4. ¿Mejora o
empeora la
seguridad?DX: Digital
Transformation
Robotics
Digital Twins
Integration
Systems
IoT
CibersecurityCloud
Computing
3D Printing
AR/VR
Big Data
Technologies 4.0

5. Smart City / Smart Grid

6. Smart Metering System

7. PRODEREI 2018-2032
Reduce Non-Thecnical losses at 13.38%
1,407 Millions MXN of Thief and Energy Fraud
Non-Technical Losses

8. Tampering

9. Solution

10. Literature Review
• It’s a well-know problem
• The are different works using
different AI/ML/ Analytics
Techniques
• Most of the works are
focused in Big Data and
Cloud Computing

11. Edge-Fog-Cloud Computing Architecture

12. Smart Metering Systems Architecture
Edge Fog Cloud

13. Implementing a SMS Architecture

14. SM1
DC1
DC2
SM2
SM3
SM4
SM5
SM6
SM7
Edge Computing
Fog Computing
Implementing a SMS Architecture

15. SM
measurements Consumption/
Production
Patterns
DC
A, SAs,
DER Fraud
Events
Report
Machine Learning
Machine Learning
Consumption/
Production
Data
Consumption/
Production
Patterns
Consumption/
Production
Data11
2
3
3
Check model
accurancy
4
5
6
SMS Edge-Fog Architecture

16. Structure of SM Database
Timestamp Consumption (kWh) Production (kWh)
01/01/2018 00:15 0.02652 0
01/01/2018 00:30 0 0. 00048
01/01/2018 00:45 0.04563 0.000458
… … …
01/31/2019 23:45 0.23181 0.000475

17. Comparative of ML Models
Decision Tree Regression (DTR),
Linear Regression (LR),
Sequential Neural Network (SNN) and
MultiLayer Perceptron Regression (MLPR).
Scikit-learn and Keras libraries of Python were used.

18. Method MAPE
SM
MAPE
DC
MAPE
DC-SM
DTR 16.47% 16.84% 16.51%
LR 18.27% 17.97% 17.66%
SNN 20.76% 21.12% 20.93%
MLPR 13.72% 12.36% 12.29%
Test with the complete database

19. Method MAPE
SM
MAPE
DC
MAPE
DC-SM
DTR 17.39% 16.23% 16.21%
LR 22.17% 21.49% 21.17%
SNN 25.33% 26.12% 26.43%
MLPR 23.47% 22.13% 20.57%
Test with database per year

20. Method MAPE
SM
MAPE
DC
MAPE
DC-SM
DTR 16.27% 16.64% 16.19%
LR 17.88% 17.71% 17.53%
SNN 19.23% 19.12% 19.23%
MLPR 12.66% 12.16% 11.99%
Test with database per month

21. Method MAPE SM MAPE DC MAPE
DC-SM
DTR 16.99% 17.14% 17.07%
LR 18.15% 17.97% 17.93%
SNN 20.53% 20.97% 20.99%
MLPR 13.28% 12.98% 12.74%
Test with database per week

22. Method MAPE SM MAPE DC MAPE DC-
SM
DTR 16.69% 16.73% 16.45%
LR 18.05% 17.83% 17.80%
SNN 19.53% 20.74% 20.63%
MLPR 13.57% 12.99% 12.91%
Test with database per day

23. This work shows that it is possible to detect energy fraud using
data and smart meter processing. The results indicate that the
detection of anomalies of consumption/production of electrical
energy is adequate. Still, like any forecast model, human
intervention plays an essential role in decision making. It is
important to emphasize that the proposed model adjusts its
parameters and amount of training/fit values so that they are
executed properly in embedded devices with limited computing
capabilities. Finally, the use of Fog Computing though DC in
general terms, improves the results of forecasting.
Conclusions

24. This work is partially supported by the TecnológicoNacional de
México under the grant 8000.20-P and 9002.20-P.
Questions?
Thanks you so much!
juan.or@morelia.tecnm.mx, enrique.ra@morelia.tecnm.mx,
noel.rm@zitacuaro.tecnm.mx, jose.gg3@morelia.tecnm.mx,
ismael.mm@Morelia.tecnm.mx, jaime.cerda@umich.mx