Wavelet-based decompositions in probabilistic load forecasting

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.