Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and thes...

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Authors: Guerrero Alonso, Juan Ignacio, León de Mora, Carlos, Monedero Goicoechea, Iñigo Luis, Biscarri Triviño, Félix, Biscarri Triviño, Jesús
Format: article
Status:Versión enviada para evaluación y publicación
Publication Date:2014
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/76678
Online Access:https://hdl.handle.net/11441/76678
https://doi.org/10.1016/j.knosys.2014.08.014
Access Level:Open access
Keyword:Expert systems
Power distribution
Non-technical losses
Neural networks
Text mining
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spelling Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detectionGuerrero Alonso, Juan IgnacioLeón de Mora, CarlosMonedero Goicoechea, Iñigo LuisBiscarri Triviño, FélixBiscarri Triviño, JesúsExpert systemsPower distributionNon-technical lossesNeural networksText miningCurrently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers.ElsevierTecnología Electrónica2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/76678https://doi.org/10.1016/j.knosys.2014.08.014reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésKnowledge-Based Systems, 71 (november 2014), 376-388.https://www.sciencedirect.com/science/article/pii/S0950705114003025info:eu-repo/semantics/openAccessoai:idus.us.es:11441/766782026-06-17T12:51:07Z
dc.title.none.fl_str_mv Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
title Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
spellingShingle Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
Guerrero Alonso, Juan Ignacio
Expert systems
Power distribution
Non-technical losses
Neural networks
Text mining
title_short Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
title_full Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
title_fullStr Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
title_full_unstemmed Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
title_sort Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
dc.creator.none.fl_str_mv Guerrero Alonso, Juan Ignacio
León de Mora, Carlos
Monedero Goicoechea, Iñigo Luis
Biscarri Triviño, Félix
Biscarri Triviño, Jesús
author Guerrero Alonso, Juan Ignacio
author_facet Guerrero Alonso, Juan Ignacio
León de Mora, Carlos
Monedero Goicoechea, Iñigo Luis
Biscarri Triviño, Félix
Biscarri Triviño, Jesús
author_role author
author2 León de Mora, Carlos
Monedero Goicoechea, Iñigo Luis
Biscarri Triviño, Félix
Biscarri Triviño, Jesús
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Tecnología Electrónica
dc.subject.none.fl_str_mv Expert systems
Power distribution
Non-technical losses
Neural networks
Text mining
topic Expert systems
Power distribution
Non-technical losses
Neural networks
Text mining
description Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/76678
https://doi.org/10.1016/j.knosys.2014.08.014
url https://hdl.handle.net/11441/76678
https://doi.org/10.1016/j.knosys.2014.08.014
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Knowledge-Based Systems, 71 (november 2014), 376-388.
https://www.sciencedirect.com/science/article/pii/S0950705114003025
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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