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
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method
Xinyu Liang, Ziheng Wang, Hao Wang
TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model
Shirong Zheng, Shaobo Liu, Zhenhong Zhang, Dian Gu, Chunqiu Xia, Huadong Pang, Enock Mintah Ampaw