Power Load Interval Prediction Based on Selective Ensemble of EEMD-ROCKET

In this paper, we propose a selective ensemble-based EEMD-ROCKET interval forecasting model and apply it to the task of power load interval prediction. Initially, we decompose the power load sequence using modified ensemble empirical mode decomposition (EEMD), obtaining a set number of Intrinsic Mode Function (IMF) components and a trend term. Subsequently, we employ a bootstrap-based approach and selective ensemble technique to construct an interval forecasting model based on ROCKET for predicting each IMF. Additionally, a point forecasting ROCKET model is utilized to predict the residual trend. The final prediction interval results are obtained by directly summing the predicted results of each IMF component and the trend term. We conduct experiments on a publicly available electricity load dataset, validating the performance of the proposed model.