Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

Ramos, Caio C. O.; Souza, Ana Cristina de; Gastaldello, Danilo Sinkiti; Papa, João Paulo

doi:10.1109/induscon.2012.6451485

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…Paper [6], also discuss the related setting and achieved the test recall of 0.77 and accuracy of 0.86 on the different data set. According to Ramos et al [17], 5K Brazilian Industrial Customer data set is used where each consumer profile has ten features like maximum demand, demand billed, installed power, etc. The test accuracy of SVM, neural network, and K-nearest neighbors (KNN) are 0.9628, 0.9448, and 0.9620, respectively.…”

Section: Literature Related To the Detection Of Ntlmentioning

confidence: 99%

Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Yadav¹,

Kumar²

2021

JESA

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Non-technical losses (NTL), which occur up to 40% of the total electric transmission and distribution power, create many challenges worldwide. These losses have a severe impact on distribution utilities and adversely affect the performance of electrical distribution networks. Furthermore, the depreciation of these NTL reduces the requirement of new power plants to fulfill the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper proposed a model for the detection of non-technical losses based on machine learning and feature engineering. Experimental results check the performance of the proposed model. These results clearly show that this proposed detection model has better accuracy, precision, recall, F1 score, and AUC score than other existing approaches.

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Section: Literature Related To the Detection Of Ntlmentioning

confidence: 99%

Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Yadav¹,

Kumar²

2021

JESA

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“…The consumption profiles of 5K Brazilian industrial customer profiles are analyzed in [12]. Each customer profile contains 10 features including the demand billed, maximum demand, installed power, etc.…”

Section: A Backgroundmentioning

confidence: 99%

Distilling provider-independent data for general detection of non-technical losses

Meira

Glauner

State

et al. 2017

2017 IEEE Power and Energy Conference at Illinois (PECI)

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Abstract-Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses. We used Area Under the Receiver-operating Curve (AUC) to assess the results.

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“…On the test set, an accuracy of 0.8717, a precision of 0.6503 and a recall of 0.2947 are reported. Consumption profiles of 5K Brazilian industrial customer profiles are analyzed in [8]. Each customer profile contains 10 features including the demand billed, maximum demand, installed power, etc.…”

Section: Related Workmentioning

confidence: 99%

Is big data sufficient for a reliable detection of non-technical losses?

Glauner

Migliosi

Meira

et al. 2017

2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)

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Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection.

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Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

Cited by 11 publications

References 12 publications

Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Distilling provider-independent data for general detection of non-technical losses

Is big data sufficient for a reliable detection of non-technical losses?

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