Detection of Electricity Theft using a Deep Learning-based Bayesian optimization algorithm and LSTM

Theft of electricity is one of the complex issues facing advanced technologies. Due to ET, the world's electric utilities would suffer significant financial losses. The standard electricity theft detection (ETD) models face a number of difficulties, including the dimensionality curse, the unavoidable effects of benign factors, and the very unbalanced distribution of electricity consumption data. In order to identify electricity theft, we propose an LSTM (long short-term model) based on deep learning. We suggest a deep learning-based electricity theft detector in this work to identify theft attempts. Deep learning techniques are suggested by combining two separate phases, such as data pre-processing and classification, in order to detect electricity theft from user power usage patterns. The initial phase in the process of refining data entails standardizing and addressing classes that are unbalanced. The second step is feature extraction, where we employ the Long Short-Term Memory (LSTM) block to more effectively extract and memorise features from vast time series data. This effectively extracts important information to accurately portray occurrences of power theft. The third phase involves classifying the data using the deep learning-based Bayesian optimization algorithm (DL-BOA), which also saves and extracts the long-term dependency in the data on power use. The suggested framework for deep learning greatly surpasses earlier machine learning techniques like logistic regression and SVM in terms of accuracy, according to simulation findings. The simulations are run to demonstrate how well our suggested model works. Information to track down electricity thieves and stop the big money losses in the power industry.