Stacking for Probabilistic Short-term Load Forecasting
This work addresses forecasting accuracy for electricity grid operators, but it is incremental as it builds on existing meta-learning and quantile regression methods.
The study tackled probabilistic short-term electricity demand forecasting by combining point base forecasts using meta-learning, quantile regression, and residual simulation, finding that quantile regression forest outperformed competitors across 35 forecasting scenarios with 16 base models.
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