Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process

Blazakis, Konstantinos; Stavrakakis, G.

doi:10.5121/ijaia.2019.10106

Cited by 6 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The other operations can converge to the optimal solution 3.9088 × 10 6 every time. Similarly, the other two algorithms can reach the optimal solution every time [ 27 ].…”

Section: Results and Analysismentioning

confidence: 99%

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm

Sun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to make key decisions more conveniently according to the massive data information obtained, a spatial data mining technology based on a genetic algorithm is proposed, which is combined with the k-means algorithm. The immune principle and adaptive genetic algorithm are introduced to optimize the traditional genetic algorithm, and the K-means, GK, and IGK algorithms are compared and analyzed. The results show that, in two different datasets, the objective functions obtained by the K-means algorithm are 94.05822 and 4.10373 × 10 6 , respectively, while the objective functions obtained by the GK and IGK algorithms are 89.8619 and 3.9088 × 10 6 , respectively. The difference between the three algorithms can also be reflected in the data comparison of the number of iterations. The number of iterations required for k-means to reach the optimal solution is 8.21 and 8.4, respectively, which is the most among the three algorithms, while the number of iterations required for IGK to reach the optimal solution is 5.84 and 4.9, respectively, which is the least. Although the time required for K-means is short, by comparison, the IGK algorithm we use can get the optimal solution in relatively less time.

show abstract

“…The other operations can converge to the optimal solution 3.9088 × 10 6 every time. Similarly, the other two algorithms can reach the optimal solution every time [ 27 ].…”

Section: Results and Analysismentioning

confidence: 99%

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm

Sun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…− Meter tampering/breaking seal: This is quite similar to what happens with the HV metres. − Other methods of electricity theft include: Detect a paying consumer nearby, damage to metre boxes and slow the spinning discs in the metre box using magnets [12].…”

Section: Techniques Of Electricity Theftmentioning

confidence: 99%

An efficient and improved model for power theft detection in Pakistan

et al. 2021

View full text Add to dashboard Cite

This paper describes an improved model for the monitoring of power used by a party such as household users and different industries in Pakistan. The power theft detection was done using the intelligent internet of things (IoT) service system for calculating the user's power simultaneously. The power meter catches a theft detection device that is instantly transmitted to the central system which compares both the data by means of microcontroller and if there is any difference found, it informs the power utility about the hooking, meter relief or theft activities happen. Information of the theft detection through the global mobile communications system is transmitted and notified theft is displayed on the terminal monitor or won. As a result, although consumers continue to use excess fuel, the customer's power supply is cut in the electricity boards segment. The general radio package module system sends central circuit and meter data via an internet protocol address to a web server. GSM's IoT based perception is used to monitor the power supply and billing information calculated with a microcontroller continuously with the determination of the electricity table area. With this unit, the duplicate user can be located at the rear of the electricity office with the power meter status.

show abstract

“…In order to calculate the number of local outliers, the reconstruction error of each sample was compared with neighboring samples, which indicates the degree of abnormality of each sample. In [5], Blazakis and Stavrakakis presented a computational method of analysis and identification of customer electricity consumption patterns based on data mining techniques in order to identify unauthorized residential customers. In the mentioned method, principal component analysis (PCA) is combined with mean shift algorithm for different power theft scenarios.…”

Section: Introductionmentioning

confidence: 99%

Improving power theft detection using efficient clustering and ensemble classification

Ghaedi

Farizani

Ghaemi

2021

IJECE

View full text Add to dashboard Cite

One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks.

show abstract

Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process

Cited by 6 publications

References 34 publications

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm

An efficient and improved model for power theft detection in Pakistan

Improving power theft detection using efficient clustering and ensemble classification

Contact Info

Product

Resources

About