AI-Enhanced Predictive Analytics for Fraud Detection in Smart Grid Systems

Electricity theft is a serious threat to the reliability and effectiveness of smart grids. As smart meters become increasingly common, the traditional methods of detection are no longer sufficient to counteract ever more sophisticated fraud schemes. The goal of this research is to examine the use of prospective data analytics for the detection of electricity fraud through machine learning models optimized for maximum accuracy. The smart meter real-time data is proposed to enhance algorithms for techniques what can be undertaken in a model using in the use of hyperparameter optimization. The proposed method aims to detect unusual patterns of consumption, minimize false positive generation, and enhance the quality of fraud detection; thereby improving the security and efficiency of smart grids.