Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest

•Proposed a decomposition-based quantile regression forest load forecasting method.•The proposed method reaches n-step prediction by training n models.•Temperature and humidity index is introduced as a relevant factor.•Tree-structured of Parzen Estimators based Bayesian optimization is introduced.•PICP, PINAW, and CWC are introduced to evaluate the interval prediction results.