VAAD: A VAE Based Anomaly Detection Approach for Smart Grids

In the realm of smart grids and energy efficiency, this research presents an approach entitled VAAD to enhance resiliency using Variational Autoencoders (VAEs). Owing to the substantial increase in smart meter deployment, real-time data becomes crucial for analyzing electricity consumption patterns and identifying anomalies. Existing anomaly detection approaches often lack adaptability to dynamic environments and suffer from false positives, particularly when consumer behaviors undergo significant changes. Our approach addresses this challenge by employing VAEs, demonstrating superior accuracy in detecting anomalies through Noise Tolerant Concept Drift Detection. The adaptability and effectiveness of our approach position it as a valuable asset for fortifying smart grid resiliency, contributing to the broader goals of energy efficiency and sustainable urban ecosystems. Experimental results validate the proposed framework’s ability to detect anomalies in near real-time, showcasing its practical applicability and potential impact in smart grid operations.