Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT

Machine learning algorithms applied towards detection of non-technical losses are increasingly becoming a go-to solution and tools that help detection losses in such a way are being developed by power utilities of all sizes across the globe. Non-technical losses represent the biggest fraction of dis...

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Bibliographic Details
Author: Osypova, Sofia
Format: master thesis
Publication Date:2020
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/332789
Online Access:https://hdl.handle.net/2117/332789
Access Level:Open access
Keyword:Energy consumption
Machine learning
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Gestió de l'energia::Demanda i consum energètics
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repository_id_str
spelling Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORTOsypova, SofiaEnergy consumptionMachine learningEnergia -- ConsumAprenentatge automàticÀrees temàtiques de la UPC::Energies::Gestió de l'energia::Demanda i consum energèticsMachine learning algorithms applied towards detection of non-technical losses are increasingly becoming a go-to solution and tools that help detection losses in such a way are being developed by power utilities of all sizes across the globe. Non-technical losses represent the biggest fraction of distribution losses in electric power systems of all countries making utilities experience significant revenue fatalities. These losses are largely represented by fraudulent activities performed by the customers such, which lead to additional losses, damaging the infrastructure of networks and deteriorating the safety of the grid. This thesis proposes an extensive review of different methods and case studies of non-technical losses in grids in different parts of the world, their effect on the economies of scale and other aspects of social well-being such as links between the rate of fraud being committed in the national grid to human development index (HDI) and other metrics. Modern technologies of power consumption estimation and regulation such as smart meters are given a close look as well as their infrastructure, role and possible applications are also reviewed in this thesis. As for the machine learning part overview, over 80 research papers were carefully studied in order to quantify the progression of interest towards applied artificial intelligence algorithms from the industry, key performance indicators and state-of-the-art fraud detection models were analysed and findings were extracted. The findings suggest several optimal application thresholds of learning techniques under examination with respect to specifics of the problem, available resources and budgets as well as volume and quality of the data. In the experimental section of the thesis, a specific threat model was developed and effectively employed through the generation of synthetic fraudulent profiles according to a methodology presented in the literature. The underlying data that was used to satisfy the question and the scope of this thesis was extrapolated from a larger source of consumption record that was in open access for all in such a way stimulating the further development of the field by a wide range of researchers and scientists around the world. The algorithm of choice, namely k-means clustering, was profoundly reviewed, studied and applied and at a later instance executed as three parametristic approaches to tackle the same problem from different perspectives and get a deep look-out of the unsupervised clustering and the patterns in the data. Results were insightful and superiority of performance was shifting between an approach that used artificial feature extraction and the approach with kWh reading samples being examined over a dynamic time warping temporal sequencing algorithm. Finally, the conclusions were drawn suggesting the scope of future work followed by a brief cost-benefit analysis designed for utilities considering investing in the fraud detection tool development as well as an environmental sustainability outlook of the thesis concept’s implementation is proposed.Universitat Politècnica de CatalunyaAragüés Peñalba, MònicaJené Vinuesa, Marc20202020-11-1120202020-11-23master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/332789reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3327892026-05-27T15:37:01Z
dc.title.none.fl_str_mv Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
title Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
spellingShingle Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
Osypova, Sofia
Energy consumption
Machine learning
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Gestió de l'energia::Demanda i consum energètics
title_short Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
title_full Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
title_fullStr Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
title_full_unstemmed Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
title_sort Consumption Pattern Detection Through the Use of Machine Learning : Clustering Techniques for Non-Technical Losses Detection RERORT
dc.creator.none.fl_str_mv Osypova, Sofia
author Osypova, Sofia
author_facet Osypova, Sofia
author_role author
dc.contributor.none.fl_str_mv Aragüés Peñalba, Mònica
Jené Vinuesa, Marc
dc.subject.none.fl_str_mv Energy consumption
Machine learning
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Gestió de l'energia::Demanda i consum energètics
topic Energy consumption
Machine learning
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Gestió de l'energia::Demanda i consum energètics
description Machine learning algorithms applied towards detection of non-technical losses are increasingly becoming a go-to solution and tools that help detection losses in such a way are being developed by power utilities of all sizes across the globe. Non-technical losses represent the biggest fraction of distribution losses in electric power systems of all countries making utilities experience significant revenue fatalities. These losses are largely represented by fraudulent activities performed by the customers such, which lead to additional losses, damaging the infrastructure of networks and deteriorating the safety of the grid. This thesis proposes an extensive review of different methods and case studies of non-technical losses in grids in different parts of the world, their effect on the economies of scale and other aspects of social well-being such as links between the rate of fraud being committed in the national grid to human development index (HDI) and other metrics. Modern technologies of power consumption estimation and regulation such as smart meters are given a close look as well as their infrastructure, role and possible applications are also reviewed in this thesis. As for the machine learning part overview, over 80 research papers were carefully studied in order to quantify the progression of interest towards applied artificial intelligence algorithms from the industry, key performance indicators and state-of-the-art fraud detection models were analysed and findings were extracted. The findings suggest several optimal application thresholds of learning techniques under examination with respect to specifics of the problem, available resources and budgets as well as volume and quality of the data. In the experimental section of the thesis, a specific threat model was developed and effectively employed through the generation of synthetic fraudulent profiles according to a methodology presented in the literature. The underlying data that was used to satisfy the question and the scope of this thesis was extrapolated from a larger source of consumption record that was in open access for all in such a way stimulating the further development of the field by a wide range of researchers and scientists around the world. The algorithm of choice, namely k-means clustering, was profoundly reviewed, studied and applied and at a later instance executed as three parametristic approaches to tackle the same problem from different perspectives and get a deep look-out of the unsupervised clustering and the patterns in the data. Results were insightful and superiority of performance was shifting between an approach that used artificial feature extraction and the approach with kWh reading samples being examined over a dynamic time warping temporal sequencing algorithm. Finally, the conclusions were drawn suggesting the scope of future work followed by a brief cost-benefit analysis designed for utilities considering investing in the fraud detection tool development as well as an environmental sustainability outlook of the thesis concept’s implementation is proposed.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-11-11
2020
2020-11-23
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/332789
url https://hdl.handle.net/2117/332789
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
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