Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia
ilustraciones, graficas
- Autores:
-
Calentura Rojas, Yeison Ferney
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81555
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL)
Machine learning
Aprendizaje computacional
Inspecciones técnicas de instalaciones eléctricas
Pérdidas no técnicas de energía
Consumo de energía
Facturación mensual
Empresa comercializadora de energía
Detección de anomalías
Comercializador
Computational learning
Technical inspections
Non-technical energy losses
Monthly reading take
Energy consumption
Monthly billing
Energy trading company
Anomaly detection
Energy losses
Energía eléctrica
Electric power
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| dc.title.translated.eng.fl_str_mv |
Clustering and classification algorithm for detection of customers suspected of contributing to non-technical energy losses at energy trader company |
| title |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| spellingShingle |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia 000 - Ciencias de la computación, información y obras generales::003 - Sistemas APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) Machine learning Aprendizaje computacional Inspecciones técnicas de instalaciones eléctricas Pérdidas no técnicas de energía Consumo de energía Facturación mensual Empresa comercializadora de energía Detección de anomalías Comercializador Computational learning Technical inspections Non-technical energy losses Monthly reading take Energy consumption Monthly billing Energy trading company Anomaly detection Energy losses Energía eléctrica Electric power |
| title_short |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| title_full |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| title_fullStr |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| title_full_unstemmed |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| title_sort |
Algoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en Colombia |
| dc.creator.fl_str_mv |
Calentura Rojas, Yeison Ferney |
| dc.contributor.advisor.none.fl_str_mv |
Prias Caicedo, Omar Fredy Cruz Roa, Angel Alfonso |
| dc.contributor.author.none.fl_str_mv |
Calentura Rojas, Yeison Ferney |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grisec |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) Machine learning Aprendizaje computacional Inspecciones técnicas de instalaciones eléctricas Pérdidas no técnicas de energía Consumo de energía Facturación mensual Empresa comercializadora de energía Detección de anomalías Comercializador Computational learning Technical inspections Non-technical energy losses Monthly reading take Energy consumption Monthly billing Energy trading company Anomaly detection Energy losses Energía eléctrica Electric power |
| dc.subject.lemb.spa.fl_str_mv |
APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) |
| dc.subject.lemb.eng.fl_str_mv |
Machine learning |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje computacional Inspecciones técnicas de instalaciones eléctricas Pérdidas no técnicas de energía Consumo de energía Facturación mensual Empresa comercializadora de energía Detección de anomalías Comercializador |
| dc.subject.proposal.eng.fl_str_mv |
Computational learning Technical inspections Non-technical energy losses Monthly reading take Energy consumption Monthly billing Energy trading company Anomaly detection Energy losses |
| dc.subject.unesco.spa.fl_str_mv |
Energía eléctrica |
| dc.subject.unesco.eng.fl_str_mv |
Electric power |
| description |
ilustraciones, graficas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-10T14:06:02Z |
| dc.date.available.none.fl_str_mv |
2022-06-10T14:06:02Z |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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Abdullateef, A. I., Salami, M. J. E., Musse, M. A., Onasanya, M. A., & Alebiosu, M. I. (2013). New consumer load prototype for electricity theft monitoring. IOP Conference Series: Materials Science and Engineering, 53(1). https://doi.org/10.1088/1757-899X/53/1/012061 Ahmad, T., & Ul Hasan, Q. (2016). Detection of Frauds and Other Non-technical Losses in Power Utilities using Smart Meters: A Review. International Journal of Emerging Electric Power Systems, 17(3), 217–234. https://doi.org/10.1515/ijeeps-2015-0206 Alharbi, M., Alghumayjan, S., Alsaleh, M., Shah, D., & Alabdulkareem, A. (2020). Electricity Non-Technical Loss Detection: Enhanced Cost-Driven Approach Utilizing Synthetic Control. 2–6. Arcos-vargas, A., & Cruz, P. (2016). Detection of Non-Technical Losses in Smart Distribution Networks: a Review. 473(February). https://doi.org/10.1007/978-3-319-40159-1 ASOCODIS. (2014). Evolución Sectorial de la Distribución y Comercialización de Energía Eléctrica en Colombia 2010-2013. 1–100. ASOCODIS. (2018). Evolución Sectorial de la Distribución y Comercialización de Energía Eléctrica en Colombia 2010-2018. Athira, P. M., & Jeniba, D. J. (2015). Electricity Theft Control Using Smart Prepaid Energy Meter. 1(3), 16–20. Babu, T. V., Murthy, T. S., & Sivaiah, B. (2013). Detecting unusual customer consumption profiles in power distribution systems - APSPDCL. 2013 IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2013, 3. https://doi.org/10.1109/ICCIC.2013.6724264 Biscarri, F., Monedero, I., García, A., Guerrero, J. I., & León, C. (2017). Electricity clustering framework for automatic classification of customer loads. Expert Systems with Applications, 86, 54–63. https://doi.org/10.1016/j.eswa.2017.05.049 Boucetta, C., Flauzac, O., Nassour, A. N. M., & Nolot, F. (2020). Multi-level Hierarchical Clustering Algorithm for Energy-theft Detection in Smart Grid Networks. 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, June, 1–6. https://doi.org/10.1109/ICECCE49384.2020.9179334 Buzau, M. M., Tejedor-Aguilera, J., Cruz-Romero, P., & Gomez-Exposito, A. (2018). Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning. IEEE Transactions on Smart Grid, 3053(c), 1–10. https://doi.org/10.1109/TSG.2018.2807925 Buzau, M. M., Tejedor-Aguilera, J., Cruz-Romero, P., & Gómez-Expósito, A. (2020). Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters. IEEE Transactions on Power Systems, 35(2), 1254–1263. https://doi.org/10.1109/TPWRS.2019.2943115 Cespedes, R., Leon, R. A., Salazar, H., Ruiz, M. E., Hidalgo, R., & Mejia, D. (2012). An appraisal of the challenges and opportunities for the Colombia Inteligente Program implementation. IEEE Power and Energy Society General Meeting, 1–6. https://doi.org/10.1109/PESGM.2012.6345383 Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2020). SMOTE: Synthetic Minority Over-sampling Technique Nitesh. Ecological Applications, 30(2), 321–357. https://doi.org/10.1002/eap.2043 Dangar, B., & Joshi, S. K. (2015). Electricity theft detection techniques for metered power consumer in GUVNL, GUJARAT, INDIA. 2015 Clemson University Power Systems Conference, PSC 2015. https://doi.org/10.1109/PSC.2015.7101683 Ghori, K. M., Abbasi, R. A., Awais, M., Imran, M., Ullah, A., & Szathmary, L. (2019). Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection. IEEE Access, 8, 16033–16048. https://doi.org/10.1109/ACCESS.2019.2962510 Glauner, P., Meira, J. A., Valtchev, P., State, R., & Bettinger, F. (2016). The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey. 10, 760–775. https://doi.org/10.2991/ijcis.2017.10.1.51 Gómez, J., Carvajal, S., & A. Arango. (2015). Programas de gestión de demanda de electricidad para el sector residencial en Colombia: Enfoque Sistémico. Energética, 46, 73–83. Gomez, V. M., & Rengifo, C. F. (2016). A software tool for generating patterns of energy consumption in residential customers. Proceedings of the 2015 IEEE 35th Central American and Panama Convention, CONCAPAN 2015, Concapan Xxxv. https://doi.org/10.1109/CONCAPAN.2015.7428495 Guerrero, J. I., Garc, A., & Personal, E. (2017). Heterogeneous data source integration for smart grid ecosystems based on metadata mining. https://doi.org/10.1016/j.eswa.2017.03.007 Guerrero, J. I., Monedero, Í., Biscarri, F., Biscarri, J., Millán, R., & León, C. (2013). Detection of non-technical losses: The project MIDAS. Advances in Information Security, Privacy, and Ethics (AISPE), December, 140–164. https://doi.org/10.4018/978-1-4666-4940-8.ch008 Guerrrero, J., Parejo, A., Personal, E., & Biscarri, F. (2017). Intelligent Information System as a Tool to Reach Unaproachable Goals for Inspectors. November 2016. Heling, L., & Acosta, M. (2020). Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12123 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-030-49461-2_10 Ho, T. K. (1995). Random decision forests. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1, 278–282. https://doi.org/10.1109/ICDAR.1995.598994 José, J., Flórez, M., Andres, R., Martinez, E., & Ferreira, R. (2016). Parte I Smart Grids Colomnia VISIÓN 2030. Luxburg, U. Von. (2015). A turtorial on Spectral Clustering. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2(3), 1–8. http://dx.doi.org/10.1016/j.physrep.2013.08.002%0Ahttp://dx.doi.org/10.1016/j.physrep.2016.09.002%0Ahttp://digital-library.theiet.org/content/conferences/10.1049/cp.2012.2481%0Ahttp://portal.acm.org/citation.cfm?doid=1059981.1059984%0Ahttps://arxiv.org/pd Meffe, A., & de Oliveira, C. C. B. (2009). Technical loss calculation by distribution system segment with corrections from measurements. IET Conference Publications, 0752, 752–752. https://doi.org/10.1049/cp.2009.0962 Messinis, G. M., & Hatziargyriou, N. D. (2018). Review of non-technical loss detection methods. Electric Power Systems Research, 158, 250–266. https://doi.org/10.1016/j.epsr.2018.01.005 Messinis, G. M., Rigas, A. E., & Hatziargyriou, N. D. (2019). A Hybrid Method for Non-Technical Loss Detection in Smart Distribution Grids. IEEE Transactions on Smart Grid, 10(6), 6080–6091. https://doi.org/10.1109/TSG.2019.2896381 Ministerio de minas y energía. (2013). RETIE resolución 9 0708 de agosto 30 de 2013 con sus ajustes. Resolucion 90708, 127. Ministerio de minas y energía, R. de C. (2017). Resolucion No. Creg 019-2017 Comision de Regulacion de Energía y Gas.pdf. Ministerio de Minas y Energia, Upme, U. D. P. M. E., & Asocodis. (2011). Informe sectorial sobre la evolución de la distribución y comercialización de energía eléctrica en Colombia. http://www.asocodis.org.co/cms/docs/asocodis-correcciones-marzo-6.pdf Monedero, I., Biscarri, F., León, C., Guerrero, J. I., Biscarri, J., & Millán, R. (2009). New methods to detect non-technical losses on power utilities. Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2009, December 2014, 7–13. https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954009188&partnerID=40&md5=cf4a90c142938cea48098922ee0bc111 Monteiro, M. D., & Maciel, R. S. (2018). Detection of commercial losses in electric power distribution systems using data mining techniques. SBSE 2018 - 7th Brazilian Electrical Systems Symposium, 1–6. https://doi.org/10.1109/SBSE.2018.8395889 Murthy, T. S., Gopalan, N. P., & Ramachandran, V. (2019). A Naive Bayes Classifier for Detecting Unusual Customer Consumption Profiles in Power Distribution Systems - APSPDCL. Proceedings of the 3rd International Conference on Inventive Systems and Control, ICISC 2019, Icisc, 673–678. https://doi.org/10.1109/ICISC44355.2019.9036460 Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7(DEC). https://doi.org/10.3389/fnbot.2013.00021 Papadimitriou, C., Messinis, G., Vranis, D., Politopoulou, S., & Hatziargyriou, N. (2017). Non-technical losses: detection methods and regulatory aspects overview. CIRED - Open Access Proceedings Journal, 2017(1), 2830–2832. https://doi.org/10.1049/oap-cired.2017.0825 Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/10.1109/TPAMI.2005.159 Puri, A. (2018). Application and Uses of Big Data Predictive Analysis in Public Sectors: A Systematic Review. Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018, 539–543. https://doi.org/10.1109/CTEMS.2018.8769196 Rajabi, A., Li, L., Zhang, J., Zhu, J., Ghavidel, S., & Ghadi, M. J. (2017). A review on clustering of residential electricity customers and its applications. 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017. https://doi.org/10.1109/ICEMS.2017.8056062 Ramos, S., Duarte, J. M., Duarte, F. J., & Vale, Z. (2015). A data-mining-based methodology to support MV electricity customers’ characterization. Energy and Buildings, 91, 16–25. https://doi.org/10.1016/j.enbuild.2015.01.035 Rosenberg, A., & Hirschberg, J. (2007). V-Measure : A conditional entropy-based external cluster evaluation measure. June, 410–420. Sethi, A. R., Amin, S., & Schwartz, G. (2017). Value of intrusion detection systems for countering energy fraud. Proceedings of the American Control Conference, 2739–2746. https://doi.org/10.23919/ACC.2017.7963366 Sharma, S., & Majumdar, A. (2020). Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning. IEEE Transactions on Power Delivery, 36(2), 1241–1244. https://doi.org/10.1109/TPWRD.2020.3029439 Shraddha K.Popat, E. M. (2014). Review and Comparative Study of Clustering Techniques. International Journal of Computer Science and Information Technologies, 5(1), 7. www.ijcsit.com Shukla, S. (2014). A Review ON K-means DATA Clustering APPROACH. International Journal of Information & Computation Technology, 4(17), 1847–1860. http://www.irphouse.com Spirić, J. V., Stanković, S. S., & Dočić, M. B. (2016). Determining a set of suspicious electricity customers using statistical ACL Tukey’s control charts method. International Journal of Electrical Power and Energy Systems, 83, 402–410. https://doi.org/10.1016/j.ijepes.2016.04.035 Spirić, J. V., Stanković, S. S., & Dočić, M. B. (2018). Identification of suspicious electricity customers. International Journal of Electrical Power and Energy Systems, 95(June 2017), 635–643. https://doi.org/10.1016/j.ijepes.2017.09.019 Teeraratkul, T., Daniel, O., & Lallz, S. (2016). Condensed Representation and Individual Prediction of Consumer Demand. 2016 IEEE Smart Energy Grid Engineering (SEGE), 11–16. https://doi.org/10.1109/SEGE.2016.7589192 Trevizan, R. D., Bretas, A. S., & Rossoni, A. (2015). Nontechnical Losses detection: A Discrete Cosine Transform and Optimum-Path Forest based approach. 2015 North American Power Symposium, NAPS 2015. https://doi.org/10.1109/NAPS.2015.7335160 Viegas, J. L., Esteves, P. R., Melício, R., Mendes, V. M. F., & Vieira, S. M. (2017). Solutions for detection of non-technical losses in the electricity grid: A review. Renewable and Sustainable Energy Reviews, 80(August 2016), 1256–1268. https://doi.org/10.1016/j.rser.2017.05.193 Villar-rodriguez, E., Del, J., & Oregi, I. (2017). Detection of Non-Technical Losses in Smart Meter Data based on Load Curve Profiling and Time Series Analysis. Volk, F., & Max, M. (2015). Efficient, Verifiable, Secure, and Privacy-Friendly Computations for the Smart Grid. 0–4. https://doi.org/10.1109/ISGT.2015.7131862 Wang, X., Tao, Y., & Zheng, K. (2018). Feature selection methods in the framework of mrmr. Proceedings - 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018, 2017, 1490–1495. https://doi.org/10.1109/IMCCC.2018.00307 Wang, Y., Li, L., & Yang, Q. (2015). Application of clustering technique to electricity customer classification for load forecasting. 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In Conjunction with 2015 IEEE International Conference on Automation and Logistics, August, 1425–1430. https://doi.org/10.1109/ICInfA.2015.7279510 Wang, Z., Li, G., Wang, X., Chen, C., & Long, H. (2019). Analysis of 10kV Non-technical Loss Detection with Data-driven Approaches. 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, 4154–4158. https://doi.org/10.1109/ISGT-Asia.2019.8881733 World Bank. (2009). Reducing technical and non-technical losses in the power sector. World Bank Group Energy Sector Strategy, July 2009, 1–35. http://siteresources.worldbank.org/INTESC/Resources/ReducingTechnicalAndNonTechnicalLossesBackgroundPaper.pdf Yadav, S., & Shukla, S. (2016). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proceedings - 6th International Advanced Computing Conference, IACC 2016, Cv, 78–83. https://doi.org/10.1109/IACC.2016.25 Yip, S. C., Tan, W. N., Tan, C. K., Gan, M. T., & Wong, K. S. (2018). An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power and Energy Systems, 101(February), 189–203. https://doi.org/10.1016/j.ijepes.2018.03.025 |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Prias Caicedo, Omar Fredy265d20246a1b791c7440bade08606854Cruz Roa, Angel Alfonsofc07088f5c43fb7bffba9a880fb78a24Calentura Rojas, Yeison Ferney27829158b2e5f24d4de8edbb9ef44c3aGrisec2022-06-10T14:06:02Z2022-06-10T14:06:02Z2022https://repositorio.unal.edu.co/handle/unal/81555Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasLas empresas comercializadoras de energía eléctrica contemplan dentro de su proceso de planeación estratégica el propósito de maximizar sus rendimientos y brindar servicios con altos estándares de calidad. Por lo tanto, continuamente están en búsqueda de una operación más eficiente y rentable. El reto fundamental para este objetivo es minimizar las pérdidas de energía que corresponden a la diferencia entre la energía eléctrica generada y la que se factura finalmente a los usuarios. Estas pérdidas son de dos tipos: i) técnicas, que se manifiestan como parte de los fenómenos físicos asociados a la transmisión, transformación y distribución de la energía; y ii) no técnicas, que están asociadas a las intervenciones del ser humano que afectan el funcionamiento normal del equipo de medida, o demás acciones que no permiten la correcta facturación del consumo de energía. La naturaleza de las pérdidas no técnicas hace que su rastreo sea un proceso difícil e ineficiente, las soluciones propuestas por diversos autores se han agrupado en tres categorías: la primera enfocada a implementación de redes inteligentes y sistemas de monitoreo constante; la segunda basada en analítica de datos y la aplicación de técnicas de aprendizaje computacional sobre información de los usuarios, redes y consumos de energía; y la tercera, un enfoque mixto que toma elementos de ambos para la construcción de una solución completa analizando datos recopilados por redes de distribución inteligente. Este trabajo se abordó desde la perspectiva de la segunda categoría, el comercializador en estudio dispuso de fuentes de información que contenía datos del cliente, registros de toma de lectura mensual, e inspecciones técnicas. Posterior a la construcción del conjunto de datos, se analizaron los diferentes atributos numéricos y categóricos principalmente y se crearon características adicionales denominadas meta-características. Se emplearon dos algoritmos para la selección de las características: Random Forest y mRMR (Máxima relevancia, mínima redundancia), finalmente se realizó la implementación de técnicas de aprendizaje computacional supervisadas (Random Forest y Gradient Boosting) y no supervisadas (Kmeans, Agglomerative y Spectral clustering). En este trabajo puede evidenciarse como la selección de características y la creación de las meta-características propuestas permitieron un mejor desempeño de los modelos aplicados contrarrestando el efecto del desbalance entre clases propio de la naturaleza del problema, la implementación de la búsqueda de parámetros óptimos usando el método de Grid Search y la aplicación de validación cruzada por K-Folds contribuye de manera significativa a encontrar la mejor configuración de desempeño de los clasificadores y minimizar los errores de entrenamiento pasando de precisiones iniciales del 0,6 al 0,8 de precisión promedio macro (Macro-average Precision). Para las técnicas no supervisadas la naturaleza de los datos no permite una diferenciación clara entre los grupos obtenidos, por lo que ese enfoque no se considera apropiado para la solución del problema, en este caso se obtuvieron grupos bastante heterogéneos cuyos resultados se mantuvieron inferiores a 0,06 de puntuación de homogeneidad. (Texto tomado de la fuente)Electric energy trading companies consider within their strategic planning process the purpose of maximizing their yields and providing services with high quality standards. Therefore, they are continually searching for a more efficient and profitable operation. The fundamental challenge for this objective is to minimize energy losses, which correspond to the difference between the electrical energy generated and the one finally billed to users. These losses are of two types: i) technical, which are manifested as part of the physical phenomena associated with the transmission, transformation and distribution of energy; and ii) not technical, that are associated with human interventions that affect the normal operation of the media equipment or other actions that do not allow the correct billing of energy consumption. The nature of non-technical losses makes their tracking a difficult and inefficient process, the solutions proposed by several authors have been grouped into three categories: the first focused on the implementation of smart grids and constant monitoring systems, second based on data analytics and the application of computational learning techniques on information from users, networks and energy consumption, third, a mixed approach that takes elements of both to build a complete solution by analyzing data collected by intelligent distribution networks. This work was approached from the perspective of the second group, the marketer under study had information sources that contained customer data, monthly reading records, and technical inspections. After the construction of the data set, the different numerical and categorical attributes were analyzed and additional characteristics called meta-characteristics were created. Two algorithms are applied to select the most relevant characteristics: Random Forest and mRMR (maximum relevance, minimum redundancy), finally the implementation of supervised (random forest y gradient boosting) and unsupervised agglomerative y spectral clustering) computational learning techniques were carried out. In this work it can be evidenced how the selection of characteristics and the creation of the proposed meta-characteristics allowed a better performance of the applied models, counteracting the effect of the imbalance between classes typical of the nature of the problem, the implementation of the search for optimal parameters using the Grid Search method and the application of cross-validation by K-Folds contribute significantly to finding the best performance configuration of the classifiers and minimizing training errors, going from initial precision of 0.6 to 0.8 Macro-average Precision. For the unsupervised techniques, the nature of the data does not allow a clear differentiation between the groups obtained, so this approach is not considered appropriate for solving the problem. In this case, quite heterogeneous groups were obtained whose results remained below 0.06 homogeneity score.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónComputación aplicada144 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::003 - SistemasAPRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL)Machine learningAprendizaje computacionalInspecciones técnicas de instalaciones eléctricasPérdidas no técnicas de energíaConsumo de energíaFacturación mensualEmpresa comercializadora de energíaDetección de anomalíasComercializadorComputational learningTechnical inspectionsNon-technical energy lossesMonthly reading takeEnergy consumptionMonthly billingEnergy trading companyAnomaly detectionEnergy lossesEnergía eléctricaElectric powerAlgoritmo de agrupación y clasificación para la detección de clientes sospechosos en contribuir a pérdidas no técnicas de energía en una empresa comercializadora eléctrica en ColombiaClustering and classification algorithm for detection of customers suspected of contributing to non-technical energy losses at energy trader companyTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbdullateef, A. 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International Journal of Electrical Power and Energy Systems, 101(February), 189–203. https://doi.org/10.1016/j.ijepes.2018.03.025EstudiantesInvestigadoresPúblico generalORIGINAL1121860755.2022.pdf1121860755.2022.pdfTesis de Maestría en Ingeniería Sistemas y Computaciónapplication/pdf7739075https://repositorio.unal.edu.co/bitstream/unal/81555/1/1121860755.2022.pdf76a01e40515a702c92eacbd6fba42c63MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81555/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1121860755.2022.pdf.jpg1121860755.2022.pdf.jpgGenerated Thumbnailimage/jpeg5280https://repositorio.unal.edu.co/bitstream/unal/81555/3/1121860755.2022.pdf.jpg7259891bfeece2bab70c0fa97d9f4e9cMD53unal/81555oai:repositorio.unal.edu.co:unal/815552024-08-06 23:10:12.897Repositorio Institucional Universidad Nacional de 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