Electricity theft detecting based on density-clustering method

Zheng, Kedi; Wang, Yi; Chen, Qixin; Li, Yuanpeng

doi:10.1109/isgt-asia.2017.8378347

Cited by 30 publications

(13 citation statements)

References 8 publications

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“…They have used Silhouette coefficient and Davies Bouldin index to evaluate their technique but did not compare them with other clustering algorithms. A similar approach is used in [17]. The authors have proposed a distance matrix to observe the unusual profiles of consumers.…”

Section: A: Unsupervised Learningmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

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Section: A: Unsupervised Learningmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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“…To date, anomaly detection strategies have played a key role in identifying energy fraud in smart meters by analysing historical data [17]. Energy providers identify anomalous consumption patterns and impede energy fraud using consumer's load profiles, where anomalies are typically classed into three main categories: (1) point anomalies, (2) contextual anomalies and (3) collective anomalies.…”

Section: Related Workmentioning

confidence: 99%

“…Density-based clustering methods, such as Gaussian Mixture Model (GMM) and density-based spatial clustering of applications with noise (DBSCAN), have been compared in [17] to detect abnormal electricity patterns to challenge electricity theft. These types of unsupervised algorithms group elements into categories, also known as clusters, based on their similarities.…”

Section: Related Workmentioning

confidence: 99%

Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Hurst

Montañez

Shone

2020

IoT

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Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.

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“…Supervised methods include various classifiers and neural network models, such as support vector machines [17], [22], extreme learning machines [23], random forests [24], and deep learning algorithms. Unsupervised methods primarily include a variety of clustering algorithms, e.g., k-means clustering [25], fuzzy clustering [26], and other improved clustering methods [2], [27], [28]. However, as mentioned above, the labels of abnormal electricity consumption data in most datasets are difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

Zhang¹,

Dong²,

Li³

et al. 2020

IEEE Access

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The detection of abnormal electricity consumption behavior has been of great importance in recent years. However, existing research often focuses on algorithm improvement and ignores the process of obtaining features. The optimal feature set, which reflects customers' electricity consumption behavior, has a significant influence on the final detection results. Moreover, it is not straightforward to obtain datasets with label information. In this paper, a method based on feature engineering for unsupervised detection of abnormal electricity consumption behavior is proposed. First, the original feature set is constructed by brainstorming in the feature engineering step. Then, the optimal feature set, which reflects the customers' electricity consumption behavior, is obtained by features selected based on the variance and similarity between them. After that, in the abnormal detection step, a density-based clustering algorithm, in which the best clustering parameters are selected through iteration and evaluation, combined with unsupervised clustering evaluation indexes, is used to detect abnormal electricity consumption behaviors. Finally, using the load dataset of an industrial park, several typical feature strategies are applied for comparison with the feature engineering proposed in this paper. To perform the evaluation, the label information of abnormal behaviors is obtained by combining the original electricity consumption behavior detection results with abnormal data injections. The abnormal detection method proposed has given good results and outperformed typical feature strategies in an effective and generalizable way.

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Electricity theft detecting based on density-clustering method

Cited by 30 publications

References 8 publications

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

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