Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Abstract

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

“…Cluster-analysis-based data-mining techniques, such as [11][12][13] High requirements on the quantity of the data; High computational cost Machine learning, such as [14][15][16][17][18][19][20][21][22][23] Strong non-linear mapping ability; Influence of super parameters on prediction stability Artificial intelligence algorithms, such as [24][25][26] Strong convergence; Easy to fall into local extreme value Data envelopment analysis, such as [27][28][29] Strong applicability; Wide application range This study presents a power load forecasting-based abnormal data detection method to improve the economy of electricity inspection and promote the sustainable development of electric power firms. First, an intelligent algorithm is used to optimize the parameters of ELM to improve the forecasting accuracy for the power load.…”

“…The model was found to perform better than a convolutional neural network and long-short-term memory network in electricity inspection. Akram et al [21] introduced a RUSBoost technique and proposed the manta ray foraging model and the bird flocking algorithm model, which improved the accuracy of electricity theft detection. Taking account of imbalance in electricity consumption data, Banga et al [22] designed a machine learning model which included six data-balancing techniques, and compared the performance of 12 classification algorithms, using the superposition ensemble algorithm to optimize the machine learning model to improve accuracy.…”

Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

“…In order to improve operational efficiency, enhance grid security, improve customer service quality, and expand new business, State Grid has put forward higher requirements for the operational development of data [11][12]. According to the information system construction platform of State Grid, a business unified data platform with standardized and unified models, clean and transparent data, and flexible and intelligent analysis will be built in the future [13][14][15]. The State Grid Customer Service Center, as the execution unit of the power supply service business and the support organization of marketing decision-making, has accumulated the customer data and massive power supply service information of the whole network.…”

Machine learning-based electricity theft detection using support vector machines

“…The study confirmed reasonable accuracy in detecting electricity theft using SVM. Another study by et al [18] proposed a novel convolutional neural network based (CNN) method with RUSBoost manta-ray foraging optimization and RUSBoost bird swarm algorithm for detecting electricity theft by analyzing customers' electricity consumption patterns. The proposed method included data preprocessing, feature extraction, model training, and standard or abnormal classification of electricity expenditure patterns.…”