Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction
Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack X. Xin · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 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, GLasso-former shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.