GLASSOFORMER: A QUERY-SPARSE TRANSFORMER FOR POST-FAULT POWER GRID VOLTAGE PREDICTION
Yunling Zheng, Carson Hu, Jack Xin, Guang Lin, Meng Yue, Bao Wang
-
Members: FreeSPS
IEEE Members: $11.00
Non-members: $15.00Length: 00:07:39
11 May 2022
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, GLassoformer shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.
