Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor

Peng, Yanlin; Yang, Yining; Xu, Yuejie; Yang, Xue; Song, Runan; Kang, Jiannan; Zhao, Haisen

doi:10.1109/access.2021.3100980

Cited by 63 publications

(19 citation statements)

References 24 publications

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“…Outlier candidates are customers with load characteristics that are far from the cluster centers. The LOF was then used to calculate the anomalous degrees of outlier candidates [9]. However, there are certain drawbacks to the proposed strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Khot¹,

Dhore²,

Thorat³

et al. 2023

IJSREM

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Electricity theft is the act of stealing electricity by deceptive techniques. Electricity theft represents a large chunk of NTL (Non- Technical Loss). These are the losses caused by misidentified, mis-allocated, or incorrect energy flows. Electricity theft is a major issue in India, as it is in most of the developing countries. Although the theft can be detected using machine learning techniques. In this study, the system is proposed that reads smart meter data (input) using OCR (Optimized Character Recognition). OCR is a technique for recognizing text characters in digital images that have been printed or handwritten. The smart meter data image which consists of electricity usage units is converted into machine- readable text by OCR. Furthermore, the SARIMAX (Seasonal Auto Regressive Integrated Moving Average with exogenous factors) algorithm is utilized to monitor customers electricity consumption and detect electricity theft. If theft is identified, an alert message and details of the theft are sent to an electrical board worker. The worker then manually verifies/checks and updates the status. If no theft is discovered, a bill is generated. Key Words: Electricity Theft, Machine Learning, Optical Character Recognition, SARIMAX

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Section: Literature Reviewmentioning

confidence: 99%

“…The proposed method solely examines electricity use data, which may be incomplete. Other information, such as meteorological elements (temperature), geographical factors, and some electric factors (current and voltage), should be researched in addition to meter reading data [8], [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Khot¹,

Dhore²,

Thorat³

et al. 2023

IJSREM

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show abstract

“…Clustering divides objects into several subsets through some specific algorithms, so that objects in the same subset have some similar attributes. In many researches, clustering algorithm is used to identify power theft based on the data features of different users [4][5][6][7][8][9][10] . Different clusters represent different types of power users, and the same type of power users shares similar power consumption patterns.…”

Section: Clustering Analysis To Recover Power Loss Due To Power Stealingmentioning

confidence: 99%

Identify power stealing time period based on user power consumption data

Xie

Wang

Liu

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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In order to recover the loss of the stolen electric power, a comprehensive approach by analyzing the user power consumption data has been developed. According to the characteristics of the power stealing methods of each category of users, a comprehensive algorithm is employed to identify the method and the length of the power stealing behaviors. Abnormal data series that meet the power stealing characteristics for voltage, current, power and power factor are classified. Clustering algorithm is employed to identify abnormal power consumption, such that time period of power stealing can be calculated. Classification and clustering algorithms are synthesized such that results of different algorithms with different principles can be cross-verified, and stolen power can be recovered accurately and fairly.

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“…On model validation, the maximum accuracy achieved was 89%. The authors in [26] used a combination of Local Outlier Factor (LOF) and k-means clustering to detect electricity theft. They used kmeans clustering to analyze the load profiles of customers, and LOF to calculate the anomaly degrees of customers whose load profiles were from their cluster centres.…”

Section: Related Workmentioning

confidence: 99%

Electricity Theft Detection in Smart Grids Based on Deep Neural Network

2022

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Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyperparameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.

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Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor

Cited by 63 publications

References 24 publications

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Identify power stealing time period based on user power consumption data

Electricity Theft Detection in Smart Grids Based on Deep Neural Network

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