Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines

By Jawad Nagi, Keem Siah Yap, Sieh Kiong Tiong, Syed Khaleel Ahmed, M. Mohamad. IEEE Transactions on Power Delivery, 2010

400 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/db0c1d03b24396f0916e56ac78b663f2ca132c64

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. J. Urgen Schmidhuber
2. Dan Claudiu Ciresan
3. Ueli Meier
4. Karol J. Piczak
5. Sangho Choe
6. Rajiv Punmiya
7. Kyunghyun Cho

In Electrical and Electronic Engineering

1. J. Urgen Schmidhuber
2. Rongxing Lu
3. Gianni Di Caro
4. Frederick Ducatelle
5. Luca Maria Gambardella
6. Xuemin Shen
7. Victor C. M. Leung
8. Zibin Zheng

In Mechanical Engineering

1. J. Urgen Schmidhuber

Citations in the corpus

• Max-pooling convolutional neural networks for vision-based hand gesture recognition (2011, methodology): “Since SVMs [29] have gained much attention in recent times due to their powerful generalization capabilities as gesture classifiers [16], [18] we evaluate different feature learning schemes using SVMs.”
• Electricity Theft Detection in AMI Using Customers’ Consumption Patterns (2016, methodology): “Furthermore, successful utilization of SVM for problems that deal with the similar data type [21] motivates us to use this classifier.”
• Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, methodology): “With the help of the Markle Hass concept, Wei et al. [35] proposed a security framework.”
• Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, methodology): “proposed an SVM-based approach[28] which uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities.”
• Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids (2018, background): “Many recent studies [17]–[19] are based on SVM.”