A new way for comparing solutions to non-technical electricity losses in South America
Non-technical losses are a component of energy losses associated with energy theft and fraud by the final consumers, hindering revenues of distribution utilities. This paper aims to compare the implemented solutions in the countries of South America to reduce non-technical losses. In this comparison...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/5910
- Acceso en línea:
- http://hdl.handle.net/11407/5910
- Palabra clave:
- Energy theft
Non-technical losses
Power distribution system planning
Quality in the electricity supply
- Rights
- License
- http://purl.org/coar/access_right/c_16ec
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A new way for comparing solutions to non-technical electricity losses in South America |
| title |
A new way for comparing solutions to non-technical electricity losses in South America |
| spellingShingle |
A new way for comparing solutions to non-technical electricity losses in South America Energy theft Non-technical losses Power distribution system planning Quality in the electricity supply |
| title_short |
A new way for comparing solutions to non-technical electricity losses in South America |
| title_full |
A new way for comparing solutions to non-technical electricity losses in South America |
| title_fullStr |
A new way for comparing solutions to non-technical electricity losses in South America |
| title_full_unstemmed |
A new way for comparing solutions to non-technical electricity losses in South America |
| title_sort |
A new way for comparing solutions to non-technical electricity losses in South America |
| dc.subject.spa.fl_str_mv |
Energy theft Non-technical losses Power distribution system planning Quality in the electricity supply |
| topic |
Energy theft Non-technical losses Power distribution system planning Quality in the electricity supply |
| description |
Non-technical losses are a component of energy losses associated with energy theft and fraud by the final consumers, hindering revenues of distribution utilities. This paper aims to compare the implemented solutions in the countries of South America to reduce non-technical losses. In this comparison, we introduce a new indicator based on the World Bank's database as input information. Considering that some regulatory agencies take policy actions related to non-technical losses to improve the quality of the electricity supply, we also present a correlation analysis of the proposed indicator and the electricity supply quality index. This analysis shows that in most of South America's countries, there is a high correlation within the studied horizon. An adequate characterization of the temporal variation in the proposed indicator can characterize the evolution of the consumers' perception of the quality in the electricity supply. This indicator allows each country's regulatory agency to analyze how the performed action is reducing non-technical losses concerning neighboring countries. © 2020 Elsevier Ltd |
| publishDate |
2020 |
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2021-02-05T14:57:47Z |
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2021-02-05T14:57:47Z |
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2020 |
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Article |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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9571787 |
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http://hdl.handle.net/11407/5910 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.jup.2020.101113 |
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9571787 10.1016/j.jup.2020.101113 |
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http://hdl.handle.net/11407/5910 |
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eng |
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eng |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090285093&doi=10.1016%2fj.jup.2020.101113&partnerID=40&md5=42ff9a041d200e9c7947dd08b96091ab |
| dc.relation.citationvolume.none.fl_str_mv |
67 |
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Resolução Normativa no 687 de 2015 (2015) Acende Brasil, I., (2017) Perdas comerciais e inadimplência no setor elétrico, 18. , White Paper São Paulo Adhikari, R., Agrawal, R.K., An Introductory Study on Time Series Modeling and Forecasting (2013), arXiv preprint arXiv:1302.6613 Nota Técnica nº 106/2015. Metodologia de tratamento regulatório para perdas não técnicas de energia elétrica. Superintendência de regulação econômica SER (2015), Brasília Angelos, E.W.S., Saavedra, O.R., Cortés, O.A.C., De Souza, A.N., Detection and identification of abnormalities in customer consumptions in power distribution systems (2011) IEEE Trans. Power Deliv., 26 (4), pp. 2436-2442 Bachra, S., Buckley-Pearson, M., Garcia, N.D.N., McQuillan, M., Market Information Report: Colombia. Advanced Energy Centre. MaRS Cleantech (2015), Ontario, Canada Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gomez-Exposito, A., Detection of non-technical losses using smart meter data and supervised learning (2019) IEEE Trans. Smart Grid, 10 (3), pp. 2661-2670 Cambini, C., Fumagalli, E., Rondi, L., Incentives to quality and investment: evidence from electricity distribution in Italy (2016) J. Regul. Econ., 49, pp. 1-32 Carvalho, P., Smart metering deployment in Brazil (2015) Energy Procedia, 83, pp. 360-369 Corton, M.L., Zimmermann, A., Phillips, M.A., The low cost of quality improvements in the electricity distribution sector of Brazil (2016) Energy Pol., 97, pp. 485-493 De Faria, R.A., Fonseca, K.V.O., Schneider, B., Nguang, S.N., Collusion and fraud detection on electronic energy meters - a use case of forensics investigation procedures (2014), pp. 65-68. , Published in 2014, IEEE Security and Privacy Workshops Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W., A review on time series forecasting techniques for building energy consumption (2017) Renew. Sustain. Energy Rev., 74, pp. 902-924 Debnath, K.B., Mourshed, M., Forecasting methods in energy planning models (2018) Renew. Sustain. Energy Rev., 88, pp. 297-325 Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Electricity theft: overview, issues, prevention and a smart meter based approach to control theft (2011) Energy Pol., 39 (2), pp. 1007-1015 Di Bella, G., Norton, L., Ntamatungiro, J., Ogawa, S., Samake, I., Santoro, M., Energy subsidies in Latin America and the Caribbean: stocktaking and policy challenges (2015) IMF Work. Pap, 15 (30) Dixon, P., Woolner, P., Quantitative data analysis: using SPSS (2012) Research Methods in Educational Leadership & Management, pp. 340-362. , R.J. Ann Briggs M. Coleman M. Morrison SAGE Publications Ltd London Enel, Seeding energies (2015) Sustainability Report 2015, , http://reports2015.enel.com/download/BdS_2015_ENG_PA_WEB.pdf, Roma. [Online]. Available (Accessed 19 July 2020) Energia, M.Y., Ministerio de Obras públicas y comunicaciones (2017) Electricidad-Generación [Online], , https://www.ssme.gov.py/vmme/index.php?option=com_content&view=article&id=1216&, Available (Accessed 19 July 2020) Faria, L.T., Melo, J.D., Padilha-Feltrin, A., Spatial-Temporal estimation for non-technical losses (2016) IEEE Trans. Power Deliv., 31 (1), pp. 362-369 Flavin, C., Gonzalez, M., Majano, A.M., Ochs, A., Rocha, M.D., Tagwerker, P., Study of renewable energy market in Latin American and the Caribbean (2014), 1-67. , Inter-American Dev. Bank Glauner, P., Meira, J.A., Valtchev, P., State, R., Bettinger, F., The challenge of non-technical loss detection using artificial intelligence: a survey (2017) Int. J. Comput. Intell. Syst., 10 (1), pp. 760-775 Growitsch, C., Jamasb, T., Pollitt, M., Quality of service, efficiency, and scale of network industries: an analysis of European electricity distribution (2009) Appl. Econ., 41 (20), pp. 2555-2570 Development and Expansion Programmes 2013–2017 (2013), https://gplinc.net/pl/plc/media/DE-Programme-2013-2017-F.pdf, [Online]. Available (Accessed 19 July 2020) Henriques, H.O., Barbero, A.P.L., Ribeiro, R.M., Fortes, M.Z., Zanco, W., Xavier, O.S., Amorim, R.M., Development of adapted ammeter for fraud detection in low-voltage installations (2014) Meas. J. Int. Meas. Confed., 56, pp. 1-7 Access to Energy in Low-Income Communities in the Latin America and Caribbean Region: Lessons Learned and Recommendations (2013), https://energy-base.org/app/uploads/2020/03/8.IFC-Access-to-Energy-in-Low-income-Communities-in-the-Latin-America-and-Caribbean-Region-2013.pdf, [Online]. Available (Accessed 19 July 2020) Jacobs, D., Marzolf, N., Paredes, J.R., Rickerson, W., Flynn, H., Becker-Birck, C., Solano-Peralta, M., Analysis of renewable energy incentives in the Latin America and Caribbean region: the feed-in tariff case (2013) Energy Pol., 60, pp. 601-610 Generation and Distribution of Electricity in Jamaica: A Regional Comparison of Performance Indicators (2010), p. 111. , http://www.mona.uwi.edu/msbm/news/generation-and-distribution-electricity-jamaica-regional-comparison-performance-indicators, 2010 Jamaica Kingston [Online]. Available (Accessed 19 July 2020) Jiménez, R., Serebrisky, T., Mercado, J., Power Lost: Sizing Electricity Losses in Transmission and Distribution Systems in Latin America and the Caribbean (2014), Published by Inter-American Development Bank New York Kayacan, E., Ulutas, B., Kaynak, O., Grey system theory-based models in time series prediction (2010) Expert Syst. Appl., 37 (2), pp. 1784-1789 Kessides, I.N., The impacts of electricity sector reforms in developing countries (2012) Electr. J., 25 (6), pp. 79-88 Kumar, U., Jain, V.K., Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India (2010) Energy, 35 (4), pp. 1709-1716 Kumar, V.S., Prasad, J., Samikannu, R., Overview, issues, and prevention of energy theft in smart grids and virtual power plants in Indian context (2017) Energy Pol., 110, pp. 365-374 Liu, S., Lin, Y., Introduction to grey systems theory (2010) Grey Systems. Understanding Complex Systems, 68. , Springer Berlin, Heidelberg Mimmi, L.M., Ecer, S., An econometric study of illegal electricity connections in the urban favelas of Belo Horizonte, Brazil (2010) Energy Pol., 38 (9), pp. 5081-5097 Mwaura, F.M., Adopting electricity prepayment billing system to reduce non-technical energy losses in Uganda: lesson from Rwanda (2012) Util. Pol., 23, pp. 72-79 Newswire, P.R., World Loses $89.3 Billion to Electricity Theft Annually, $58.7 Billion in Emerging Markets (2014), http://www.prnewswire.com/news-releases/world-loses-893-billion-to-electricity-theft-annually-587-billion-in-emerging-markets-300006515.html, [Online]. Available (Accessed 17 July 2020) Newswire, P.R., 96 Billion Is Lost Every Year to Electricity Theft: Utilities Increasingly Investing in Solutions to Combat Theft and Non-technical Losses (2017), https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html, [Online]. Available (Accessed 19 July 2020) Nicolas, P.V., Liberalization in the Venezuelan power sector: what is it stalling it? Venez. Energy policy, laws regul (2015) Handb. Vol. 1 Strateg. Inf. Basic Laws, pp. 73-86. , 2015 International Business Publications Washington Emcali trabaja para reducir pérdidas de energía por conexiones fraudulentas (2018), https://90minutos.co/emcali-trabaja-reducir-perdidas-energia-fraudulentas-31-07-2018/, (Accessed 19 July 2020) Ott, J., World Bank world development indicators (2014) Encyclopedia of Quality of Life and Well-Being Research, p. 7253. , Springer Netherlands Dordrecht 7253 Pérez, L.L., Estrategia inteligente eficiente para la planificación de las inspecciones de suministros de las empresas del servicio eléctrico (Doctoral dissertation) (2011), Universidad Nacional Experimental Politécnica "Antonio José de Sucre Barquisimeto, Venezuela Pollitt, M., Electricity reform in Argentina: lessons for developing countries (2008) Energy Econ., 30 (4), pp. 1536-1567 Porras, J.A., Rivera, H.O., Giraldo, F.D., Correa, B.S.A., Identification of non-technical electricity losses in power distribution systems by applying techniques of information analysis and visualization (2015) IEEE Lat. Am. Trans., 13 (3), pp. 659-664 R: A Language and Environment for Statistical Computing (2019), R Foundation for Statistical Computing Vienna, Austria Ramos, C.C.O., Sousa, A.N.D., Papa, J.P., Falcão, A.X., A new approach for non-technical losses detection based on optimum-path forest (2011) IEEE Trans. Power Syst., 26 (1), pp. 181-189 Sánchez-Zuleta, C.C., Fernandez-Gutiérrez, J.P., Piedrahita-Escobar, C.C., Identification of the characteristics incident to the detection of nontechnical losses for two Colombian energy companies (2017) Rev. Fac. Ing., 84, pp. 60-71 Schiffer, H.W., WEC energy policy scenarios to 2050 (2008) Energy Pol., 36 (7), pp. 2464-2470 Schwab, K., Sala-i-Martín, X., The Global Competitiveness Report 2017–2018: Full Data Edition (2016), World Economic Forum Suárez-Alemán, A., Serebrisky, T., Perelman, S., Benchmarking economic infrastructure efficiency: how do the Latin America and Caribbean region compare? (2019) Util. Pol., 58, pp. 1-15 Svetunkov, I., Greybox: toolbox for model building and forecasting (2020) R package version 0.5.9, , https://CRAN.R-project.org/package=greybox Tasdoven, H., Fiedler, B.A., Garayev, V., Improving electricity efficiency in Turkey by addressing illegal electricity consumption: a governance approach (2012) Energy Pol., 43, pp. 226-234 Tejeda, J., Duran, J., Jimenez, R., Doyle, M., Incrementando la eficiencia del sector eletrico: Lecciones sobre la reduccion de perdidas eletricas en Ecuador (2017) Supported grey-box models (2019), https://www.mathworks.com/help/ident/ug/supported-grey-box-models.html, [Online]. Available (Accessed 18 July 2020) Indicadores de Energia (2014), https://www1.upme.gov.co/Paginas/Indicadores-de-Energia.aspx, [Online]. Avaiable: (Accessed 19 July 2020) Viegas, J.L., Esteves, P.R., Melício, R., Mendes, V.M.F., Vieira, S.M., Solutions for detection of non-technical losses in the electricity grid: a review (2017) Renew. Sustain. Energy Rev., 80, pp. 1256-1268 The World Bank DataBank [online]. The World Bank website (2016), http://databank.worldbank.org/data/home.aspx, Available (Accessed 18 July 2020) Financial Viability of the Electricity Sector in Developing Countries: Recent Trends and Effectiveness of World Bank Interventions (2016), http://documents.worldbank.org/curated/en/137421472118276977/Financial-viability-of-the-electricity-sector-in-developing-countries-recent-trends-and-effectiveness-of-World-Bank-interventions, Washington DC. [Online]. Available (Accessed 19 July 2020) Yeh, A., Lin, D., Zhou, H., Venkataramani, C., A multivariate exponentially weighted moving average control chart for monitoring process variability (2003) J. Appl. Stat., 30 (5), pp. 507-536 Zanardo, R.P., Siluk, J.C.M., Savian, F.S., Schneider, P.S., Energy audit model based on a performance evaluation system (2018) Energy, 154, pp. 544-552 Zhao, H., Guo, S., An optimized grey model for annual power load forecasting (2016) Energy, 107, pp. 272-286 |
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Elsevier Ltd |
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Utilities Policy |
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20202021-02-05T14:57:47Z2021-02-05T14:57:47Z9571787http://hdl.handle.net/11407/591010.1016/j.jup.2020.101113Non-technical losses are a component of energy losses associated with energy theft and fraud by the final consumers, hindering revenues of distribution utilities. This paper aims to compare the implemented solutions in the countries of South America to reduce non-technical losses. In this comparison, we introduce a new indicator based on the World Bank's database as input information. Considering that some regulatory agencies take policy actions related to non-technical losses to improve the quality of the electricity supply, we also present a correlation analysis of the proposed indicator and the electricity supply quality index. This analysis shows that in most of South America's countries, there is a high correlation within the studied horizon. An adequate characterization of the temporal variation in the proposed indicator can characterize the evolution of the consumers' perception of the quality in the electricity supply. This indicator allows each country's regulatory agency to analyze how the performed action is reducing non-technical losses concerning neighboring countries. © 2020 Elsevier LtdengElsevier LtdFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090285093&doi=10.1016%2fj.jup.2020.101113&partnerID=40&md5=42ff9a041d200e9c7947dd08b96091ab67Resolução Normativa no 687 de 2015 (2015)Acende Brasil, I., (2017) Perdas comerciais e inadimplência no setor elétrico, 18. , White Paper São PauloAdhikari, R., Agrawal, R.K., An Introductory Study on Time Series Modeling and Forecasting (2013), arXiv preprint arXiv:1302.6613Nota Técnica nº 106/2015. Metodologia de tratamento regulatório para perdas não técnicas de energia elétrica. Superintendência de regulação econômica SER (2015), BrasíliaAngelos, E.W.S., Saavedra, O.R., Cortés, O.A.C., De Souza, A.N., Detection and identification of abnormalities in customer consumptions in power distribution systems (2011) IEEE Trans. Power Deliv., 26 (4), pp. 2436-2442Bachra, S., Buckley-Pearson, M., Garcia, N.D.N., McQuillan, M., Market Information Report: Colombia. Advanced Energy Centre. MaRS Cleantech (2015), Ontario, CanadaBuzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gomez-Exposito, A., Detection of non-technical losses using smart meter data and supervised learning (2019) IEEE Trans. Smart Grid, 10 (3), pp. 2661-2670Cambini, C., Fumagalli, E., Rondi, L., Incentives to quality and investment: evidence from electricity distribution in Italy (2016) J. Regul. Econ., 49, pp. 1-32Carvalho, P., Smart metering deployment in Brazil (2015) Energy Procedia, 83, pp. 360-369Corton, M.L., Zimmermann, A., Phillips, M.A., The low cost of quality improvements in the electricity distribution sector of Brazil (2016) Energy Pol., 97, pp. 485-493De Faria, R.A., Fonseca, K.V.O., Schneider, B., Nguang, S.N., Collusion and fraud detection on electronic energy meters - a use case of forensics investigation procedures (2014), pp. 65-68. , Published in 2014, IEEE Security and Privacy WorkshopsDeb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W., A review on time series forecasting techniques for building energy consumption (2017) Renew. Sustain. Energy Rev., 74, pp. 902-924Debnath, K.B., Mourshed, M., Forecasting methods in energy planning models (2018) Renew. Sustain. Energy Rev., 88, pp. 297-325Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Electricity theft: overview, issues, prevention and a smart meter based approach to control theft (2011) Energy Pol., 39 (2), pp. 1007-1015Di Bella, G., Norton, L., Ntamatungiro, J., Ogawa, S., Samake, I., Santoro, M., Energy subsidies in Latin America and the Caribbean: stocktaking and policy challenges (2015) IMF Work. Pap, 15 (30)Dixon, P., Woolner, P., Quantitative data analysis: using SPSS (2012) Research Methods in Educational Leadership & Management, pp. 340-362. , R.J. Ann Briggs M. Coleman M. Morrison SAGE Publications Ltd LondonEnel, Seeding energies (2015) Sustainability Report 2015, , http://reports2015.enel.com/download/BdS_2015_ENG_PA_WEB.pdf, Roma. [Online]. Available (Accessed 19 July 2020)Energia, M.Y., Ministerio de Obras públicas y comunicaciones (2017) Electricidad-Generación [Online], , https://www.ssme.gov.py/vmme/index.php?option=com_content&view=article&id=1216&, Available (Accessed 19 July 2020)Faria, L.T., Melo, J.D., Padilha-Feltrin, A., Spatial-Temporal estimation for non-technical losses (2016) IEEE Trans. Power Deliv., 31 (1), pp. 362-369Flavin, C., Gonzalez, M., Majano, A.M., Ochs, A., Rocha, M.D., Tagwerker, P., Study of renewable energy market in Latin American and the Caribbean (2014), 1-67. , Inter-American Dev. BankGlauner, P., Meira, J.A., Valtchev, P., State, R., Bettinger, F., The challenge of non-technical loss detection using artificial intelligence: a survey (2017) Int. J. Comput. Intell. Syst., 10 (1), pp. 760-775Growitsch, C., Jamasb, T., Pollitt, M., Quality of service, efficiency, and scale of network industries: an analysis of European electricity distribution (2009) Appl. Econ., 41 (20), pp. 2555-2570Development and Expansion Programmes 2013–2017 (2013), https://gplinc.net/pl/plc/media/DE-Programme-2013-2017-F.pdf, [Online]. Available (Accessed 19 July 2020)Henriques, H.O., Barbero, A.P.L., Ribeiro, R.M., Fortes, M.Z., Zanco, W., Xavier, O.S., Amorim, R.M., Development of adapted ammeter for fraud detection in low-voltage installations (2014) Meas. J. Int. Meas. Confed., 56, pp. 1-7Access to Energy in Low-Income Communities in the Latin America and Caribbean Region: Lessons Learned and Recommendations (2013), https://energy-base.org/app/uploads/2020/03/8.IFC-Access-to-Energy-in-Low-income-Communities-in-the-Latin-America-and-Caribbean-Region-2013.pdf, [Online]. Available (Accessed 19 July 2020)Jacobs, D., Marzolf, N., Paredes, J.R., Rickerson, W., Flynn, H., Becker-Birck, C., Solano-Peralta, M., Analysis of renewable energy incentives in the Latin America and Caribbean region: the feed-in tariff case (2013) Energy Pol., 60, pp. 601-610Generation and Distribution of Electricity in Jamaica: A Regional Comparison of Performance Indicators (2010), p. 111. , http://www.mona.uwi.edu/msbm/news/generation-and-distribution-electricity-jamaica-regional-comparison-performance-indicators, 2010 Jamaica Kingston [Online]. Available (Accessed 19 July 2020)Jiménez, R., Serebrisky, T., Mercado, J., Power Lost: Sizing Electricity Losses in Transmission and Distribution Systems in Latin America and the Caribbean (2014), Published by Inter-American Development Bank New YorkKayacan, E., Ulutas, B., Kaynak, O., Grey system theory-based models in time series prediction (2010) Expert Syst. Appl., 37 (2), pp. 1784-1789Kessides, I.N., The impacts of electricity sector reforms in developing countries (2012) Electr. J., 25 (6), pp. 79-88Kumar, U., Jain, V.K., Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India (2010) Energy, 35 (4), pp. 1709-1716Kumar, V.S., Prasad, J., Samikannu, R., Overview, issues, and prevention of energy theft in smart grids and virtual power plants in Indian context (2017) Energy Pol., 110, pp. 365-374Liu, S., Lin, Y., Introduction to grey systems theory (2010) Grey Systems. Understanding Complex Systems, 68. , Springer Berlin, HeidelbergMimmi, L.M., Ecer, S., An econometric study of illegal electricity connections in the urban favelas of Belo Horizonte, Brazil (2010) Energy Pol., 38 (9), pp. 5081-5097Mwaura, F.M., Adopting electricity prepayment billing system to reduce non-technical energy losses in Uganda: lesson from Rwanda (2012) Util. Pol., 23, pp. 72-79Newswire, P.R., World Loses $89.3 Billion to Electricity Theft Annually, $58.7 Billion in Emerging Markets (2014), http://www.prnewswire.com/news-releases/world-loses-893-billion-to-electricity-theft-annually-587-billion-in-emerging-markets-300006515.html, [Online]. Available (Accessed 17 July 2020)Newswire, P.R., 96 Billion Is Lost Every Year to Electricity Theft: Utilities Increasingly Investing in Solutions to Combat Theft and Non-technical Losses (2017), https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html, [Online]. Available (Accessed 19 July 2020)Nicolas, P.V., Liberalization in the Venezuelan power sector: what is it stalling it? Venez. Energy policy, laws regul (2015) Handb. Vol. 1 Strateg. Inf. Basic Laws, pp. 73-86. , 2015 International Business Publications WashingtonEmcali trabaja para reducir pérdidas de energía por conexiones fraudulentas (2018), https://90minutos.co/emcali-trabaja-reducir-perdidas-energia-fraudulentas-31-07-2018/, (Accessed 19 July 2020)Ott, J., World Bank world development indicators (2014) Encyclopedia of Quality of Life and Well-Being Research, p. 7253. , Springer Netherlands Dordrecht 7253Pérez, L.L., Estrategia inteligente eficiente para la planificación de las inspecciones de suministros de las empresas del servicio eléctrico (Doctoral dissertation) (2011), Universidad Nacional Experimental Politécnica "Antonio José de Sucre Barquisimeto, VenezuelaPollitt, M., Electricity reform in Argentina: lessons for developing countries (2008) Energy Econ., 30 (4), pp. 1536-1567Porras, J.A., Rivera, H.O., Giraldo, F.D., Correa, B.S.A., Identification of non-technical electricity losses in power distribution systems by applying techniques of information analysis and visualization (2015) IEEE Lat. Am. Trans., 13 (3), pp. 659-664R: A Language and Environment for Statistical Computing (2019), R Foundation for Statistical Computing Vienna, AustriaRamos, C.C.O., Sousa, A.N.D., Papa, J.P., Falcão, A.X., A new approach for non-technical losses detection based on optimum-path forest (2011) IEEE Trans. Power Syst., 26 (1), pp. 181-189Sánchez-Zuleta, C.C., Fernandez-Gutiérrez, J.P., Piedrahita-Escobar, C.C., Identification of the characteristics incident to the detection of nontechnical losses for two Colombian energy companies (2017) Rev. Fac. Ing., 84, pp. 60-71Schiffer, H.W., WEC energy policy scenarios to 2050 (2008) Energy Pol., 36 (7), pp. 2464-2470Schwab, K., Sala-i-Martín, X., The Global Competitiveness Report 2017–2018: Full Data Edition (2016), World Economic ForumSuárez-Alemán, A., Serebrisky, T., Perelman, S., Benchmarking economic infrastructure efficiency: how do the Latin America and Caribbean region compare? (2019) Util. 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