Accelerated kriging interpolation for real-time grid frequency forecasting
arXiv:2604.02932 · doi:10.1016/j.segan.2026.102403
The paper introduces a fast, data‑driven kriging interpolation method for real‑time grid frequency forecasting, achieving sub‑second computation using measurements from renewable‑rich distribution networks.
Abstract
The integration of renewable energy sources and distributed generation in the power system calls for fast and reliable predictions of grid dynamics to achieve efficient control and ensure stability. In this work, we present a novel nonparametric data-driven prediction algorithm based on kriging interpolation, which exploits the problem's numerical structure to achieve the required computational efficiency for fast real-time forecasting. Our results enable accurate frequency predictions directly from measurements, achieving sub-second computation times. We validate our findings on a simulated distribution grid case study.
14 pages, 8 figures, 2 tables. Revised version incorporating peer-review changes and minor editorial corrections. Journal reference and DOI added