Energy Consumption
Energy consumption research focuses on understanding and optimizing energy use across various sectors, from individual households and smart grids to large-scale AI systems and data centers. Current research emphasizes developing energy-efficient machine learning models (e.g., using transformers, convolutional neural networks, and recurrent neural networks) and employing techniques like federated learning to improve privacy while training models on distributed data. This work is crucial for mitigating the environmental impact of increasing energy demands and for improving the efficiency and sustainability of numerous technological applications.
Papers
Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach
Rohit Dube, Natarajan Gautam, Amarnath Banerjee, Harsha Nagarajan
Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehicles
Jiacong Xu, Riley Kilfoyle, Zixiang Xiong, Ligang Lu