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

本研究探索元学习领域，结合点预测与概率短期电力需求预测，在量位线性回归、量位回归森林以及涉及残差模拟的后处理技术的基础上生成量位预测。另外，我们引入了全局和局部元学习的变种。在局部学习模式中，元模型使用最接近查询模式的模式进行训练。通过在 35 个预测场景上进行广泛的实验研究，并使用 16 个基本预测模型，我们的研究结果突显了量位回归森林在其竞争对手中的优势。