Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

By Anish Jindal, Amit Dua, Kuljeet Kaur, Mukesh Singh, Neeraj Kumar, Sukumar Mishra. IEEE Transactions on Industrial Informatics, 2016

469 citations recorded by the source corpus.

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/48352fdf0f1fc622e870d747e08b0e530465e5d3

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Citations in the corpus

Significant Permission Identification for Machine-Learning-Based Android Malware Detection (2018, methodology): “The results of our empirical evaluation show that SIGPID can drastically reduce the number of permissions that we need to analyze to just 22 out of 135 (84% reduction), while maintaining over 90% malware detection accuracy and Fmeasure when Support Vector Machine (SVM) is used as the classifier.”
Big data analytics in smart grids: a review (2018, methodology): “Reference (Jindal et al., 2016) takes the advantage of the social networking to minimize the peak power consumption of the electrical appliances by proposing a “ family plan ” approach which leverages the social network topology and statistical energy usage patterns of the users.”
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, methodology): “The daily electricity consumption patterns of users can be analyzed by using machine learning algorithms to establish classiﬁcation models, which include decision trees, random forest (RF), support vector machines (SVM) [20], neural networks (NN) [21], and so on.”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “By introducing external variables, a top-down scheme based on decision tree and SVM methods was proposed in [86].”
Artificial Intelligence Techniques in Smart Grid: A Survey (2021, methodology): “The decision tree learning model and logistic regression, which are very easy to interpret and implement, have also been widely adapted in smart gird systems [32,33].”
Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay (2018, methodology): “[14] A.”
A preliminary study on deep learning for predicting social insurance payment behavior (2017, methodology): “[13] proposed a comprehensive top-down scheme that based on decision tree and SVM.”
A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, methodology): “SVM’s simplicity is the main reason for its frequent utilization in detecting FDIA as illustrated in [92], [93], [94], [95], [96], [97], [98], [99], [100].”