On Normality Assumption in Residual Simulation for Probabilistic Load Forecasting

Abstract

Grid modernization has brought in various types of active demand, and intermittent and distributed generation resources to challenge the traditional power system planning and operation practices. As a result, more and more decision making processes rely on probabilistic forecasts as an input. While residual simulation has been recognized as one way to generate probabilistic load forecasts, the research on the application side of probabilistic load forecasting has been heavily relying on unverified distributions of load forecasting residuals, such as normal distribution. In this paper, we study the normality assumption from a different angle. Instead of trying to prove or disprove its validity via hypothesis tests, we attempt to understand whether applying the normality assumption helps improve the quality of probabilistic load forecasts. We apply a proper scoring rule, the pinball loss function, to evaluate a set of probabilistic load forecasts developed from different underlying linear and nonlinear models. To ensure the solidity of our conclusion, we conduct two case studies, one based on data from a large generation and transmission cooperative in the U.S., and the other based on data from the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014.