5,669 research outputs found
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Non-technical losses (NTL) such as electricity theft cause significant harm
to our economies, as in some countries they may range up to 40% of the total
electricity distributed. Detecting NTLs requires costly on-site inspections.
Accurate prediction of NTLs for customers using machine learning is therefore
crucial. To date, related research largely ignore that the two classes of
regular and non-regular customers are highly imbalanced, that NTL proportions
may change and mostly consider small data sets, often not allowing to deploy
the results in production. In this paper, we present a comprehensive approach
to assess three NTL detection models for different NTL proportions in large
real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and
Support Vector Machine. This work has resulted in appreciable results that are
about to be deployed in a leading industry solution. We believe that the
considerations and observations made in this contribution are necessary for
future smart meter research in order to report their effectiveness on
imbalanced and large real world data sets.Comment: Proceedings of the Seventh IEEE Conference on Innovative Smart Grid
Technologies (ISGT 2016
Fraud detection in energy consumption: a supervised approach
Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.Peer ReviewedPostprint (author's final draft
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future
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 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
Review of Non-Technical Losses Identification Techniques
Illegally consumption of electric power, termed as non-technical losses for the distribution companies is one of the dominant factors all over the world for many years. Although there are some conventional methods to identify these irregularities, such as physical inspection of meters at the consumer premises etc, but it requires large number of manpower and time; then also it does not seem to be adequate. Now a days there are various methods and algorithms have been developed that are proposed in different research papers, to detect non-technical losses. In this paper these methods are reviewed, their important features are highlighted and also the limitations are identified. Finally, the qualitative comparison of various non-technical losses identification algorithms is presented based on their performance, costs, data handling, quality control and execution times. It can be concluded that the graph-based classifier, Optimum-Path Forest algorithm that have both supervised and unsupervised variants, yields the most accurate result to detect non-technical losses
Bridging the gap between energy consumption and distribution through non-technical loss detection
The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.Peer ReviewedPostprint (published version
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
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.Comment: Proceedings of the 19th International Conference on Intelligent
System Applications to Power Systems (ISAP 2017
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