  We present the winning strategy of an electricity demand forecasting
competition. This competition was organized to design new forecasting methods
for unstable periods such as the one starting in Spring 2020. We rely on
state-space models to adapt standard statistical and machine learning models.
We claim that it achieves the right compromise between two extremes. On the one
hand, purely time-series models such as autoregressives are adaptive in essence
but fail to capture dependence to exogenous variables. On the other hand,
machine learning methods allow to learn complex dependence to explanatory
variables on a historical data set but fail to forecast non-stationary data
accurately. The evaluation period of the competition was the occasion of trial
and error and we put the focus on the final forecasting procedure. In
particular, it was at the same time that a recent algorithm was designed to
adapt the variances of a state-space model and we present the results of the
final version only. We discuss day-today predictions nonetheless.
