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A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method
2022
Energies
In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural
doi:10.3390/en15020663
fatcat:wmk3gzmibrarpgm3x6d456isme