Análise do roubo de eletricidade e a propagação de sua influência usando redes multiplexadas e heterogêneas
Resumo
In developing countries, electricity theft is a common type of non- technical losses (NTL, i.e., losses associated with electricity that is consumed but not billed by some type of anomaly), financially affecting not only distribution system operators (DSO) but also customers. Similarly to frauds in other contexts, there is evidence that electricity theft is highly influenced by social interactions. Here we propose a multiplex and heterogeneous network model to evaluate how social and professional interactions influence on electricity theft. Particularly, by employing a variation of the random walk with restart algorithm we were able to derive a new exposure score for discriminating between fraudsters and regular customers.
Palavras-chave:
Análise de redes, perdas não técnicas, propagação de fraudes, detecção de roubos de eletricidade
Referências
Akoglu, L., Faloutsos, C., Chandy, R., and Faloutsos, C. (2013). Opinion Fraud Detection in Online Reviews by Network Effects. In Proceeding of the 7th International AAAI Conference on Weblogs and Social Media, pages 2–11.
Baesens, B., Van Vlasselaer, V., and Verbeke, W. (2015). Social network analysis for fraud detection. In Fraud Analytics: Using Descriptive, Predictive, and Social Network Techniques, pages 207–278. Wiley, Hoboken, NJ.
Coma-Puig, B., Carmona, J., Gavalda, R., Alcoverro, S., and Martin, V. (2016). Fraud Detection in Energy Consumption: A Supervised Approach. In 2016 IEEE Interna- tional Conference on Data Science and Advanced Analytics (DSAA), pages 120–129. IEEE.
Costa, B. C., Alberto, B. L. A., Portela, A. M., Maduro, W., and Eler, E. O. (2013). Fraud Detection in Electric Power Distribution Networks using an Ann-Based Knowledge- Discovery Process. International Journal of Artificial Intelligence & Applications (IJAIA), 4(6).
Faria, L. T., Melo, J. D., and Padilha-Feltrin, A. (2016). Spatial-Temporal Estimation for Nontechnical Losses. IEEE Transactions on Power Delivery, 31(1):362–369.
Glauner, P., Meira, J. A., Dolberg, L., State, R., Bettinger, F., and Rangoni, Y. (2016). Neighborhood features help detecting non-technical losses in big data sets. In IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT). IEEE.
Glauner, P., Meira, J. A., Valtchev, P., State, R., and Bettinger, F. (2017). The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey. International Journal of Computational Intelligence Systems, 10(1):760.
Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., and Mishra, S. (2016). Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid. IEEE Transactions on Industrial Informatics, 12(3):1005–1016.
León, C., Biscarri, F., Monedero, I., Guerrero, J. I., Biscarri, J., and Millán, R. (2011). In- tegrated expert system applied to the analysis of non-technical losses in power utilities. Expert Systems with Applications, 38(8):10274–10285.
Leon, C., Biscarri, F., Monedero, I., Guerrero, J. I., Biscarri, J., and Millan, R. (2011). Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies. IEEE Transactions on Power Systems, 26(4):1798–1807.
Messinis, G. M. and Hatziargyriou, N. D. (2018). Review of non-technical loss detection methods. Electric Power Systems Research, 158:250–266.
Monedero, I., Biscarri, F., León, C., Guerrero, J. I., Biscarri, J., and Millán, R. (2012). Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees. International Journal of Electrical Power & Energy Systems, 34(1):90–98.
Nagi, J., Keem Siah Yap, Sieh Kiong Tiong, Ahmed, S. K., and Nagi, F. (2011). Im- proving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System. IEEE Transactions on Power Delivery, 26(2):1284–1285.
Nagi, J., Mohammad, A. M., Yap, K. S., Tiong, S. K., and Ahmed, S. K. (2008a). Non- Technical Loss analysis for detection of electricity theft using support vector machines. In 2008 IEEE 2nd International Power and Energy Conference, pages 907–912. IEEE.
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., and Mohamad, M. (2010). Nontech- nical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines. IEEE Transactions on Power Delivery, 25(2):1162–1171.
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., and Mohammad, A. M. (2008b). Detec- tion of abnormalities and electricity theft using genetic Support Vector Machines. In TENCON 2008 - 2008 IEEE Region 10 Conference, pages 1–6. IEEE.
Omohundro, S. M. (1989). Five balltree construction algorithms. International Computer Science Institute Berkeley.
Ramos, C. C., Souza, A. N., Chiachia, G., Falcão, A. X., and Papa, J. P. (2011). A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection. Computers & Electrical Engineering, 37(6):886–894.
Ramos, C. C. O., Rodrigues, D., de Souza, A. N., and Papa, J. P. (2018). On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characteriza- tion. IEEE Transactions on Smart Grid, 9(2):676–683.
Triballeau, N., Acher, F., Brabet, I., Pin, J.-P., and Bertrand, H.-O. (2005). Virtual Screen- ing Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Re- ceptor Subtype 4. Journal of Medicinal Chemistry, 48(7):2534–2547.
Valdeolivas, A., Tichit, L., Navarro, C., Perrin, S., Odelin, G., Levy, N., Cau, P., Remy, E., and Baudot, A. (2019). Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics, 35(3):497–505.
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2015a). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75:38–48.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2015b). AFRAID. In Proceedings of the 2015 IEEE/ACM International Conference on Ad- vances in Social Networks Analysis and Mining 2015 - ASONAM ’15, pages 659–666, New York, New York, USA. ACM Press.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2017). GOTCHA! Network-Based Fraud Detection for Social Security Fraud. Management Science, 63(9):3090–3110.
Baesens, B., Van Vlasselaer, V., and Verbeke, W. (2015). Social network analysis for fraud detection. In Fraud Analytics: Using Descriptive, Predictive, and Social Network Techniques, pages 207–278. Wiley, Hoboken, NJ.
Coma-Puig, B., Carmona, J., Gavalda, R., Alcoverro, S., and Martin, V. (2016). Fraud Detection in Energy Consumption: A Supervised Approach. In 2016 IEEE Interna- tional Conference on Data Science and Advanced Analytics (DSAA), pages 120–129. IEEE.
Costa, B. C., Alberto, B. L. A., Portela, A. M., Maduro, W., and Eler, E. O. (2013). Fraud Detection in Electric Power Distribution Networks using an Ann-Based Knowledge- Discovery Process. International Journal of Artificial Intelligence & Applications (IJAIA), 4(6).
Faria, L. T., Melo, J. D., and Padilha-Feltrin, A. (2016). Spatial-Temporal Estimation for Nontechnical Losses. IEEE Transactions on Power Delivery, 31(1):362–369.
Glauner, P., Meira, J. A., Dolberg, L., State, R., Bettinger, F., and Rangoni, Y. (2016). Neighborhood features help detecting non-technical losses in big data sets. In IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT). IEEE.
Glauner, P., Meira, J. A., Valtchev, P., State, R., and Bettinger, F. (2017). The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey. International Journal of Computational Intelligence Systems, 10(1):760.
Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., and Mishra, S. (2016). Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid. IEEE Transactions on Industrial Informatics, 12(3):1005–1016.
León, C., Biscarri, F., Monedero, I., Guerrero, J. I., Biscarri, J., and Millán, R. (2011). In- tegrated expert system applied to the analysis of non-technical losses in power utilities. Expert Systems with Applications, 38(8):10274–10285.
Leon, C., Biscarri, F., Monedero, I., Guerrero, J. I., Biscarri, J., and Millan, R. (2011). Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies. IEEE Transactions on Power Systems, 26(4):1798–1807.
Messinis, G. M. and Hatziargyriou, N. D. (2018). Review of non-technical loss detection methods. Electric Power Systems Research, 158:250–266.
Monedero, I., Biscarri, F., León, C., Guerrero, J. I., Biscarri, J., and Millán, R. (2012). Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees. International Journal of Electrical Power & Energy Systems, 34(1):90–98.
Nagi, J., Keem Siah Yap, Sieh Kiong Tiong, Ahmed, S. K., and Nagi, F. (2011). Im- proving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System. IEEE Transactions on Power Delivery, 26(2):1284–1285.
Nagi, J., Mohammad, A. M., Yap, K. S., Tiong, S. K., and Ahmed, S. K. (2008a). Non- Technical Loss analysis for detection of electricity theft using support vector machines. In 2008 IEEE 2nd International Power and Energy Conference, pages 907–912. IEEE.
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., and Mohamad, M. (2010). Nontech- nical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines. IEEE Transactions on Power Delivery, 25(2):1162–1171.
Nagi, J., Yap, K. S., Tiong, S. K., Ahmed, S. K., and Mohammad, A. M. (2008b). Detec- tion of abnormalities and electricity theft using genetic Support Vector Machines. In TENCON 2008 - 2008 IEEE Region 10 Conference, pages 1–6. IEEE.
Omohundro, S. M. (1989). Five balltree construction algorithms. International Computer Science Institute Berkeley.
Ramos, C. C., Souza, A. N., Chiachia, G., Falcão, A. X., and Papa, J. P. (2011). A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection. Computers & Electrical Engineering, 37(6):886–894.
Ramos, C. C. O., Rodrigues, D., de Souza, A. N., and Papa, J. P. (2018). On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characteriza- tion. IEEE Transactions on Smart Grid, 9(2):676–683.
Triballeau, N., Acher, F., Brabet, I., Pin, J.-P., and Bertrand, H.-O. (2005). Virtual Screen- ing Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Re- ceptor Subtype 4. Journal of Medicinal Chemistry, 48(7):2534–2547.
Valdeolivas, A., Tichit, L., Navarro, C., Perrin, S., Odelin, G., Levy, N., Cau, P., Remy, E., and Baudot, A. (2019). Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics, 35(3):497–505.
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2015a). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75:38–48.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2015b). AFRAID. In Proceedings of the 2015 IEEE/ACM International Conference on Ad- vances in Social Networks Analysis and Mining 2015 - ASONAM ’15, pages 659–666, New York, New York, USA. ACM Press.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., and Baesens, B. (2017). GOTCHA! Network-Based Fraud Detection for Social Security Fraud. Management Science, 63(9):3090–3110.
Publicado
09/07/2019
Como Citar
BORRO, Luiz C.; MAIOLI, Mayara C.; SOUZA, Tales F. B.; PINTO, Daniel C..
Análise do roubo de eletricidade e a propagação de sua influência usando redes multiplexadas e heterogêneas. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 8. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1-11.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2019.6543.
