Smart Meter
Smart meters, devices measuring household energy consumption, are revolutionizing energy management and grid optimization. Current research focuses on leveraging smart meter data for tasks like load disaggregation (identifying individual appliance usage), anomaly detection (pinpointing unusual energy patterns), and load forecasting (predicting future energy demand), often employing deep learning models such as transformers, recurrent neural networks, and autoencoders, along with federated learning techniques to address privacy concerns. These advancements are crucial for improving grid stability, enabling efficient demand-side management, and facilitating the transition to renewable energy sources while safeguarding user privacy.
Papers
Faraday: Synthetic Smart Meter Generator for the smart grid
Sheng Chai, Gus Chadney
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
Sarit Maitra, Sukanya Kundu, Aishwarya Shankar
Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks
Ana-Maria Cretu, Miruna Rusu, Yves-Alexandre de Montjoye