Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

Villar-Rodríguez, Esther; Ser, Javier Del; Oregi, Izaskun; Bilbao, Miren Nekane; Gil-López, Sergio

doi:10.1016/j.energy.2017.07.008

Cited by 35 publications

(16 citation statements)

References 36 publications

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“…On one hand, abnormal user detection can help companies identify outliers with unusual behaviors, analyze their behavior changes and then improve service strategies. As a practice, an algorithm, based on probabilistic data mining and time series analysis, was introduced for detecting consumption outliers in smart grids [14]. Capozzoli et al [15] proposed an automatic load pattern learning and anomaly detection method to improve energy management in intelligent buildings.…”

Section: Motivationmentioning

confidence: 99%

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

et al. 2019

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With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior. This paper proposes an AUD-MTS (Abnormal User Detection approach based on power load multi-step clustering with Multiple Time Scales). Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior. Finally, a two-step clustering in long-term is adopted to divide users from both coarse-grained and fine-grained dimensions so as to detect abnormal users referring to customized OutlierIndex. Experiments are conducted using annual 24-point power load data of American users in all states. The accuracy of clustering methods in AUD-MTS reaches 87.5% referring to the 16 commercial building types defined by the U.S. Department of Energy, which outperforms other common clustering algorithms on AMI (Advanced Metering Infrastructure). After that, the OutlierIndex score of AUD-MTS can be increased by 0.16 compared with other outlier detection algorithms, which shows that the proposed method can detect abnormal users precisely and efficiently. Furthermore, we summarized possible causes including federal holidays, climate zones and summertime that may lead to abnormal behavior changes and discussed countermeasures respectively, which accounts for 82.3% of anomalies. The rest may be potential electricity stealing users, which requires further investigation.

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Section: Motivationmentioning

confidence: 99%

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

et al. 2019

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“…Revenue that is lost due to the difference between electricity supplied and electricity purchased is partitioned into two classes. The first class resulting from transmission and other infrastructural limitations is labelled as technical losses, whilst the second class, the majority of which are a result of meter tampering or bypassing, is labelled as non-technical losses [1][2][3] (NTL). Estimates of losses worldwide are in the billions 4,5 of US dollars and suppliers of electricity have expressed concern over these losses and the sustainability of the supply 6,7 .…”

Section: Introductionmentioning

confidence: 99%

“…Notably these include support vector machines (SVM), naïve Bayesian (NB) methods and k-nearest neighbour (k-NN) classifiers -the algorithms used in this study. The field has not stagnated; recent computational methods include convolutional neural networks 3 and ensemble-based classifiers 7 whilst time series methods have been explored 2 . In many of these studies, the common methodological approach is to assess the classifier by considering results from a test data set captured in a confusion matrix summary and reported as a performance measure.…”

Section: Introductionmentioning

confidence: 99%

A framework to select a classification algorithm in electricity fraud detection

2020

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In the electrical domain, a non-technical loss often refers to energy used but not paid for by a consumer. The identification and detection of this loss is important as the financial loss by the electricity supplier has a negative impact on revenue. Several statistical and machine learning classification algorithms have been developed to identify customers who use energy without paying. These algorithms are generally assessed and compared using results from a confusion matrix. We propose that the data for the performance metrics from the confusion matrix be resampled to improve the comparison methods of the algorithms. We use the results from three classification algorithms, namely a support vector machine, k-nearest neighbour and naïve Bayes procedure, to demonstrate how the methodology identifies the best classifier. The case study is of electrical consumption data for a large municipality in South Africa.

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“…In recent years, time series have extended to many scientific and social domains such as medicine, manufacturing industry, energy consumption and geophysics, among others (Fard et al, 2017;Villar-Rodriguez et al, 2017;Yu and Chen, 2007). In order to extract valuable information or respond to the specific needs and challenges of these areas of application, the scientific e-mail: izaskun.oregui@tecnalia.com (Izaskun Oregi), aperez@bcamath.org (Aritz Pérez), javier.delser@tecnalia.com (Javier Del Ser), ja.lozano@ehu.eus (Jose A. Lozano) community has made a great effort to develop different time series mining and machine learning models (Aghabozorgi et al, 2015;Esling and Agon, 2012;Lotte et al, 2007;Mondal et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

On-line Elastic Similarity Measures for time series

Oregi

Pérez

Ser

et al. 2019

Pattern Recognition

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The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are similar to each other. Indeed, in off-line time series mining, these measures have been shown to be very effective due to their ability to handle time distortions and mitigate their effect on the resulting distance. In the on-line setting, where available data increase continuously over time and not necessary in a stationary manner, stream mining approaches are required to be fast with limited memory consumption and capable of adapting to different stationary intervals. In this sense, the computational complexity of Elastic Similarity Measures and their lack of flexibility to accommodate different stationary intervals, make these similarity measures incompatible with the requirements mentioned. To overcome these issues, this paper adapts the family of Elastic Similarity Measures-which includes Dynamic Time Warping, Edit Distance, Edit Distance for Real Sequences and Edit Distance with Real Penalty-to the on-line setting. The proposed adaptation is based on two main ideas: a forgetting mechanism and the incremental computation. The former makes the similarity consistent with streaming time series characteristics by giving more importance to recent observations, whereas the latter reduces the computational complexity by avoiding unnecessary computations. In order to assess the behavior of the proposed similarity measure in on-line settings, two different experiments have been carried out. The first aims at showing the efficiency of the proposed adaptation, to do so we calculate and compare the computation time for the elastic measures and their on-line adaptation. By analyzing the results drawn from a distance-based streaming machine learning model, the second experiment intends to show the effect of the forgetting mechanism on the resulting similarity value. The experimentation shows, for the aforementioned Elastic Similarity Measures, that the proposed adaptation meets the memory, computational complexity and flexibility constraints imposed by streaming data.

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Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

Cited by 35 publications

References 36 publications

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

A framework to select a classification algorithm in electricity fraud detection

On-line Elastic Similarity Measures for time series

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