Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees

Monedero, Íñigo; Biscarri, Félix; León, Carlos; Guerrero, Juan Ignacio; Biscarri, Jesús; Millán, Rocío

doi:10.1016/j.ijepes.2011.09.009

Cited by 148 publications

(64 citation statements)

References 23 publications

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“…Such failures can either be accidental or the product of fraudulent manipulation. These deviations are commonly referred to as non-technical losses (NTLs) [55], and different techniques have been successfully applied to detect them. For instance, León et al [56] proposed a comprehensive framework to detect NTLs and recover electrical energy (lost by abnormalities or fraud).…”

Section: Energy Fraud Detectionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

222

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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Section: Energy Fraud Detectionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

222

108

View full text Add to dashboard Cite

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“…They are statistical measures of the degree of association between two vectors. Both have been applied successfully for calculating correlations in a variety of scenarios [21][22][23][24].…”

Section: Karl Pearson Coefficient and Cosine Similaritymentioning

confidence: 99%

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

2016

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This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005-2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

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“…This index shows that the overall of electrical distribution utility has an economical viewpoint in the proper utilization of components to supply the customer services (Dashti et al, 2013). Therefore, distribution network operators need to calculate network losses for various purposes, such as assessing the network efficiency; reducing the network losses; calculating the nontechnical losses and decision making about the operation with the optimal configuration (Ababei and Kavasseri, 2011;Yongping, 2011;Monedero et al, 2012). Furthermore, the most of traditional distribution networks are operated to minimum monitoring systems and systems operators are the lack any computational support for network conditions.…”

Section: Introductionmentioning

confidence: 99%

Management of Effective Factors in Components’ Supply Chain on Losses of Electric Distribution System

Honarmand¹,

Ghazizadeh²,

Kermanshah³

2018

American Journal of Applied Sciences

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Restructuring in the electrical distribution utilities, the loss management is one of the critical activities to enhance network efficiency. Accordingly, management of technical loss would be the main target of the asset managers. For loss management, the usual methods of loss calculation and estimation need numerous data which is not used to real networks. In this research, a practical method for loss management is introduced from the viewpoint of components' supply chain to the network. For this purpose, sub-indictors are extracted using focus group for each process of this chain and their probability distribution functions are calculated. Then, permissible values of subindicators are determined by the Delphi method and to calculate new probability functions, the deviation of each process is calculated. Finally, a real network with historical data is used here to study the proposed method. The results illustrate that the most deviation is in the sub-indicator design process about 91% in the viewpoint losses.

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Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees

Cited by 148 publications

References 23 publications

Data science for building energy management: A review

Data science for building energy management: A review

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

Management of Effective Factors in Components’ Supply Chain on Losses of Electric Distribution System

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