  The coronavirus disease 2019 (COVID-19) pandemic has urged many governments
in the world to enforce a strict lockdown where all nonessential businesses are
closed and citizens are ordered to stay at home. One of the consequences of
this policy is a significant change in electricity consumption patterns. Since
load forecasting models rely on calendar or meteorological information and are
trained on historical data, they fail to capture the significant break caused
by the lockdown and have exhibited poor performances since the beginning of the
pandemic. This makes the scheduling of the electricity production challenging,
and has a high cost for both electricity producers and grid operators. In this
paper we introduce adaptive generalized additive models using Kalman filters
and fine-tuning to adjust to new electricity consumption patterns.
Additionally, knowledge from the lockdown in Italy is transferred to anticipate
the change of behavior in France. The proposed methods are applied to forecast
the electricity demand during the French lockdown period, where they
demonstrate their ability to significantly reduce prediction errors compared to
traditional models. Finally expert aggregation is used to leverage the
specificities of each predictions and enhance results even further.
