Determining the causes of electricity losses and the role of management in curbing them: A case study of City of Tshwane Metropolitan Municipality, South Africa

The problem of energy losses, both nationally and internationally, is a leading cause for the financial collapse of most utilities. A quantitative research approach was adopted for this study where a questionnaire was used to collect information from the participants. A total of 113 City of Tshwane (CoT) employees within the electricity division participated in the study. Descriptive statistics and inferential statistical methods were used to analyse the outcome of the survey. The study found that technical and non-technical losses are the major cause of revenue loss. Non-technical losses are caused either by inefficiencies in managing losses or by end-users being unwilling to pay for electricity. The study found that power theft through meter tampering, incorrect billing by employees, and cable theft were also major causes of energy losses. Illegal connections were found to be the major cause of energy losses, along with power theft and lack of resources. Deficiencies in infrastructure maintenance were found to be the main cause of technical losses. The study found that management of CoT is committed to managing energy losses, being aware of their impact on the financial well-being of the municipality. There is an established policy of managing energy losses and there is a plan to replace ageing infrastructure. Employees are continuously trained in accurate billing and metering as part of efforts to curb energy losses. The municipality is also engaged in efforts to put educational programmes in place to inform communities about electricity theft.

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

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.

Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology

In power systems, electrical losses can be categorized into two types, namely, Technical Losses (TLs) and Non-Technical Losses (NTLs). It has been identified that NTL is more hazardous when compared to TL, primarily due to the factors such as billing errors, faulty meters, electricity theft etc. This proves to be crucial in the power system and will result in heavy financial loss for the utility companies. To identify theft, both academia and industry, use a mechanism known as Electricity Theft Detection (ETD). However, ETD is not used efficiently because of handling high-dimensional data, overfitting issues and imbalanced data. Hence, in this paper, a means of addressing this issue using Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) technique is proposed. Here, parameter optimization is performed using RUSBoost and abnormal electricity patterns are detected by LSTM technique. Electricity data are pre-processed in the proposed methodology, using interpolation and normalization methods. The data thus obtained are then sent to the LSTM module where feature extraction takes place. These features are then classified using RUSBoost algorithm. Based on the output simulated, it is identified that this methodology addresses several issues such as handling and overfitting of massive time series data and data imbalancing. Moreover, this technique also proves to be more efficient than several other methodologies such as Logistic Regression (LR), Convolutional Neural Network (CNN) and Support Vector Machine (SVM). A comparison is also drawn, taking into consideration the parameters such as Receiver operating characteristics, recall, precision and F1-score.