Novel FDIs-based data manipulation and its detection in smart meters’ electricity theft scenarios - Munawar, Shoaib; Khan, Zeshan Aslam; Chaudhary, Naveed Ishtiaq; Javaid, Nadeem; Raja, Muhammad Asif Zahoor; Milyani, Ahmad H.; Azhari, Abdullah Ahmed (Frontiers in Energy Research, 2022)
Non technical loss (NTL) is a serious issue around the globe. Consumers manipulate their smart meters (SMs) data in order to under report their readings and gain financial benefits. Various manipulation techniques are used for gaining such unfair manipulation. This paper highlights novel false data injection (FDIs) techniques, which are used to manipulate the SMs data. These techniques are introduced in comparison to six theft cases. Furthermore, various features are engineered to analyze the variance, complexity and distribution of the manipulated data. The variance and complexity is created in data distribution when FDIs and theft cases are used to poison the SMs data, which is investigated through skewness and kurtosis analysis. Furthermore, to tackle the data imbalance issue, proximity weighted synthetic oversampling (ProWsyn) technique is used. Moreover, a hybrid attentionLSTMInception is introduced, which is an integration of attention layers, LSTM and inception blocks to tackle data dimensionality, misclassification and high false positive rate (FPR) issues. The proposed hybrid model outperforms the traditional theft detectors and achieves an accuracy of 0.95%, precision 0.97%, recall 0.94%, F1 score 0.96% and area under the curve (AUC) score 0.98%.