From biased point forecasts of electricity demand to accurate predictive distributions: Using LASSO and GAMLSS

Electricity demand forecasts are crucial for power system operations. Market participants frequently rely on day-ahead predictions provided by Transmission System Operators (TSOs), but these can be systematically biased and - as recent studies report - may be improved using parsimonious autoregressive models. Despite the fact that many operational and economic decisions require well-calibrated uncertainty estimates, previous work has focused on point forecasts. The key question is how to derive accurate quantile and density predictions. Here we show that processing TSO forecasts with the Least Absolute Shrinkage and Selection Operator (LASSO) brings further accuracy gains and provides strong inputs for probabilistic forecasts. Drawing on ten years of data (2016-2025) from three European and North American power markets, we find that Generalized Additive Models for Location, Scale, and Shape (GAMLSS) deliver consistently better probabilistic performance than commonly used econometric and machine learning approaches. Together, these findings highlight how regularization and flexible distributional modeling can improve uncertainty quantification of electricity demand.