Sandy Miguel

The pretrained time-series foundation model Chronos-2 was tested as a surrogate for power-system dynamic simulation. On 1,800 load-disturbance samples from an IEEE 9-bus system, retrieval-augmented in-context learning beat random reference selection, and fine-tuned Chronos-2 had lower errors than a traditional spatiotemporal graph neural network baseline on the 178 held-out samples, with power-balance regularization improving every reported metric except voltage-angle MAE. Its 35.5 ms inference was 563 times faster than the 20 s ANDES simulation.

The pretrained time-series foundation model Chronos-2 was tested as a surrogate for power-system dynamic simulation. On 1,800 load-disturbance samples from an IEEE 9-bus system, retrieval-augmented in-context learning beat random reference selection, and fine-tuned Chronos-2 had lower errors than a traditional spatiotemporal graph neural network baseline on the 178 held-out samples, with power-balance regularization improving every reported metric except voltage-angle MAE. Its 35.5 ms inference was 563 times faster than the 20 s ANDES simulation.