![[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Summary
This paper benchmarks various deep learning models for forecasting electricity demand across US power grids, revealing no single best model for all scenarios.
Why It Matters
As energy demands grow and fluctuate, accurate forecasting is crucial for grid operators. This study provides insights into model performance, helping operators select the best architecture based on specific data conditions, ultimately enhancing grid reliability and efficiency.
Key Takeaways
- No single model is superior for all forecasting tasks; model performance varies by data type.
- State space models and PatchTST excel with historical load data, while iTransformer performs better with weather data.
- Model rankings differ significantly based on the forecasting task, such as solar versus wind power.
Computer Science > Machine Learning arXiv:2602.21415 (cs) [Submitted on 24 Feb 2026] Title:Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting Authors:Sunki Hong, Jisoo Lee, Yuanyuan Shi View a PDF of the paper titled Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting, by Sunki Hong and 2 other authors View PDF HTML (experimental) Abstract: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. To ensure a fair comparison, we adapt each model with specialized temporal processing and a modular layer that cleanly integrates weather covariates. Our results reveal that there is no single best model for all situations. When forecasting using only historical load, PatchTST and the state space models provide the highest accuracy. However, when explicit weather data is added to the inputs, the rankings reverse: iTransformer improves its accuracy three times more efficiently than PatchTST. By controlling for model size, we confirm that this advantage stems from the architecture'...
