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

Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France