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.
Waiting for anything is undesirable by most of the human beings. Especially in the case of digital money transactions, most of the people may have doubtful thoughts on their mind about the success rate of their transactions while taking a longer processing time. The Unified Payment Interface (UPI) system was developed in India for minimizing the typographic works during the digital money transaction process. The UPI system has a separate UPI identification number of each individual consisting of their name, bank name, branch name, and account number. Therefore, sharing of account information has become easier and it reduces the chances of typographic errors in digital transaction applications. Sharing of UPI details are also made easy and secure with Quick Response (QR) code scanning methods. However, a digital transaction like UPI requires a lot of servers to be operated for a single transaction same as in National Electronic Fund Transfer (NEFT) and Immediate Payment Services (IMPS) in India. This increases the waiting time of digital transactions due to poor server communication and higher volume of payment requests on a particular server. The motive of the proposed work is to minimize the server communications by employing a distributed blockchain system. The performance is verified with a simulation experiment on BlockSim simulator in terms of transaction success rate and processing time over the traditional systems.
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