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

State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting