Two-Stage Bootstrap Sampling for Probabilistic Load Forecasting

Abstract

The integration of distributed renewable energy and the implementation of the demand response complicate the change patterns of load profiles and present great uncertainties. Probabilistic load forecasting, an effective method for capturing the future load uncertainty, has been a hot issue. This article proposes a two-stage bootstrap sampling method for probabilistic load forecasting. In the first stage, alpha-bootstrap, which is a modification of traditional bootstrap methods, is applied to characterize the uncertainties from multiple forecasting models; in the second stage, the residual bootstrap method is used to formulate the regression errors. Finally, the probabilistic load forecasts can be obtained by integrating the uncertainties in both stages. Various off-the-shelf point load forecasting methods such as random forest (RF) and gradient boosting regression tree (GBRT) can be integrated into the proposed framework. We illustrate the effectiveness of our proposed method and superiority over direct quantile regression methods such as quantile RF and quantile GBRT using the case studies on open load datasets of eight zones in ISO New-England.