Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning

Abstract

Theft of electricity is a major problem that causes financial losses and inconsistent service for paying consumers for power distribution companies all over the world. The safety of the power grid depends on the ability to identify and stop electricity theft. The use of deep learning techniques has shown great promise in recent years, particularly in the areas of computer vision and natural language processing. This study recommends a random forest-based ensemble deep learning method for identifying cases of electricity theft. The proposed ensemble deep learning model leverages the best features of many kinds of deep learning architectures, including stacked Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM). Each architecture has its own strengths when it comes to monitoring normal and abnormal electrical use for signs of theft. The final forecast is derived by adding together the predictions of the different models in the random forest ensemble. The ensemble model is trained using a massive dataset of energy usage records and theft information. Information about consumption patterns is extracted using feature engineering methods once the dataset has been preprocessed to get rid of noise and outliers. This preprocessed dataset is used to train the ensemble model, which then optimizes its parameters to reduce prediction errors. We use many measures, including accuracy, precision, recall, and F1-score, to assess the proposed ensemble deep learning model’s performance. Experiments are run against both conventional machine learning methods and standalone deep learning models to prove that the ensemble method is superior. The findings demonstrate that the ensemble model is more accurate and has a greater detection rate, making it suitable for spotting energy theft.