Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology - Chen, Joy Iong-Zong; Yeh, Lu-Tsou (Journal of Soft Computing Paradigm, 2021)
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.
Keywords: Parameter Tuning; Imbalanced data; Random under sampling; Smart meter; Electricity theft; non-technical losses; Long Short-Term Memory; RUSBoost