AI RESEARCH
Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
arXiv CS.LG
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ArXi:2602.21415v2 Announce Type: replace Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available to the operator. This paper presents a comprehensive benchmark of five modern neural architectures: two state space models (PowerMamba, S-Mamba), two Transformers (iTransformer, PatchTST), and a traditional LSTM. We evaluate these models on hourly electricity demand across six diverse US power grids for forecast windows between 24 and 168 hours.