Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction

Yunling Zheng; Carson Hu; Guang Lin; Meng Yue; Bao Wang; Jack Xin

ICASSP 2022accepted0 citations

Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction

Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin

Abstract

We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLasso-former shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.

BibTeX

@inproceedings{icassp2022_glassoformeraque,
  title = {Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction},
  author = {Yunling Zheng and Carson Hu and Guang Lin and Meng Yue and Bao Wang and Jack Xin},
  booktitle = {ICASSP 2022},
  year = {2022}
}