  We present a winning method of the IEEE DataPort Competition on Day-Ahead
Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load
forecasting approach is based on online forecast combination of multiple point
prediction models. It contains four steps: i) data cleaning and preprocessing,
ii) a holiday adjustment procedure, iii) training of individual forecasting
models, iv) forecast combination by smoothed Bernstein Online Aggregation
(BOA). The approach is flexible and can quickly adopt to new energy system
situations as they occurred during and after COVID-19 shutdowns. The pool of
individual prediction models ranges from rather simple time series models to
sophisticated models like generalized additive models (GAMs) and
high-dimensional linear models estimated by lasso. They incorporate
autoregressive, calendar and weather effects efficiently. All steps contain
novel concepts that contribute to the excellent forecasting performance of the
proposed method. This holds particularly for the holiday adjustment procedure
and the fully adaptive smoothed BOA approach.
