Explainable AI (XAI) for Intelligent Energy Management in IoT-Enabled Smart Grids
TL;DR
This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids, using a hybrid machine learning method that combines classification and clustering.
Abstract
Non-technical losses (NTLs) are a major problem in modern smart grids, damaging revenue and operational functionality. This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids. The approach extracts multiple features from smart meter data and uses a hybrid machine learning method that combines classification and clustering. To test the system’s robustness, the study included simulated fraud scenarios in difficult circumstances. Results show that the system achieved high detection accuracy (96.4%) and an AUC of 0.98. The framework also reduced false alarms by 86% compared to traditional rule-based methods, improving consistency and productivity. It supports near-real-time operation with response times around 125 ms and is scalable for larger smart grid environments. Behavioral segmentation further improved reliability by identifying differences in electricity consumption and reducing incorrect classifications. Overall, the study shows that combining data quality management, behavioral analysis, and distributed processing yields a more reliable and resilient solution for operational smart grid systems.