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arXiv · 2201.09145

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction

Yunling Zheng·Carson Hu·Guang Lin·Meng Yue·Bao Wang·Jack Xin
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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, GLassoformer shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.

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Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin. 2022-01-22. glassoformer: a query-sparse transformer for post-fault power grid voltage prediction. https://arxiv.org/abs/2201.09145

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