Considerations for Multi-task Data Generation in Grid Foundation Models

This talk discusses several practical considerations in the development of datasets for the multi-task training of power grid foundation models. Tasks considered in this talk encompass a range of regression, classification, and optimization tasks including optimal power flow, reliability assessment, state estimation, and unit commitment. The talk focuses on dataset construction, data ingestion and normalization for multi-task training, and issues pertaining to time-scale selection.