Multi-Objective Evolutionary Hybrid Deep Learning for energy theft detection

Jamshid Tursunboev, Vikas Palakonda, Jae-Mo Kang

Abstract: Highlights•In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids.•Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window.•A dynamic detection window is obtained through optimization to transform electricity consumption data.•The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization.

External IDs:doi:10.1016/j.apenergy.2024.122847