  This paper presents a data-driven methodology for estimating Under Frequency
Load Shedding (UFLS) in small power systems. UFLS plays a vital role in
maintaining system stability by shedding load when the frequency drops below a
specified threshold following loss of generation. Using a dynamic System
Frequency Response (SFR) model we generate different values of UFLS (i.e.,
labels) predicated on a set of carefully selected operating conditions (i.e.,
features). Machine Learning (ML) algorithms are then applied to learn the
relationship between chosen features and the UFLS labels. A novel regression
tree and the Tobit model are suggested for this purpose and we show how the
resulting non-linear model can be directly incorporated into a Mixed Integer
Linear Programming (MILP) problem. The trained model can be used to estimate
UFLS in security-constrained operational planning problems, improving frequency
response, optimizing reserve allocation, and reducing costs. The methodology is
applied to the La Palma island power system, demonstrating its accuracy and
effectiveness. The results confirm that the amount of UFLS can be estimated
with the Mean Absolute Error (MAE) as small as 0.213 MW for the whole process,
with a model that is representable as a MILP for use in scheduling problems
such as unit commitment among others.
