  This paper investigates the performance of a day-ahead explanatory model for
inertia forecasting based on field data in the Nordic system, which achieves a
43% reduction in mean absolute percentage error (MAPE) against a
state-of-the-art time-series forecast model. The generalizability of the
explanatory model is verified by its consistent performance on Nordic and Great
Britain datasets. Also, it appears that a long duration of training data is not
required to obtain accurate results with this model, but taking a more
spatially granular approach reduces the MAPE by 3.6%. Finally, two further
model enhancements are studied considering the specific features in Nordic
system: (i) a monthly interaction variable applied to the day-ahead national
demand forecast feature, reducing the MAPE by up to 18%; and (ii) a feature
based on the inertia from hydropower, although this has a negligible impact.
The field dataset used for benchmarking is also made publicly available.
