  In this study, we delve into the realm of meta-learning to combine point base
forecasts for probabilistic short-term electricity demand forecasting. Our
approach encompasses the utilization of quantile linear regression, quantile
regression forest, and post-processing techniques involving residual simulation
to generate quantile forecasts. Furthermore, we introduce both global and local
variants of meta-learning. In the local-learning mode, the meta-model is
trained using patterns most similar to the query pattern.Through extensive
experimental studies across 35 forecasting scenarios and employing 16 base
forecasting models, our findings underscored the superiority of quantile
regression forest over its competitors
