Optimizing Electricity Theft Detection using Neural Turing Machines and Machine Learning

Electricity theft detection (ETD) is crucial for smart grids to maintain their cost-effectiveness. Current approaches for identifying theft struggle with large datasets of electricity use owing to problems with missing values, data variance, and nonlinear patterns in the data; furthermore, an integrated infrastructure to coordinate the study of electricity load data is not currently in place.The suggested method trains models after feature extraction and stage preprocessing. Data normalization, missing value imputation, and data cleansing are all components of data preprocessing. Utility data sets often suffer from inconsistent and missing data as a result of manual and unsynchronized meter reading and registering processes; however, this can be mitigated through the feature extraction process. All the way through training, the proposed approach stuck with the NTM paradigm. The average accuracy of this state-of-the-art approach is $\mathbf{94.22\%}$, which is higher than RNN and ELM.