Probabilistic load forecasting is gaining growing interest by researchers and practitioners. Multi-stage forecasting systems have recently demonstrated their effectiveness in increasing the overall performances. In this paper, we address the effect of pre-processing load time series using wavelet-based decompositions, before using quantile regression forests and random forests to build probabilistic forecasts. Four wavelet-based decompositions are specifically used for this task. Forecasts for the load components resulting from these transformations are obtained through distinct models, in order to increase the accuracy and to reduce the computational effort. Numerical applications based on the actual data published during the 2014 Global Energy Forecasting Competition are presented to evaluate the performance in a comparison with several benchmarks.

中文翻译：

---

概率负荷预测中基于小波的分解

概率负荷预测越来越受到研究人员和从业人员的关注。多阶段预测系统最近证明了其在提高整体绩效方面的有效性。在本文中，我们在使用分位数回归森林和随机森林建立概率预测之前，使用基于小波的分解处理了预处理负荷时间序列的影响。四个基于小波的分解专门用于此任务。通过不同的模型可以对这些转换产生的载荷分量进行预测，以提高准确性并减少计算量。