From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting

TL;DR

This paper introduces a simple copula-based method to transform univariate point forecasts into multivariate probabilistic forecasts for day-ahead electricity prices, capturing dependencies across hours.

Contribution

It presents a generic, easy-to-implement approach for modeling multivariate dependencies in electricity prices using copulas and optional time series components.

Findings

Effective modeling of dependencies across hours in electricity prices.

Application to five benchmark datasets demonstrating practical utility.

Construction of realistic prediction intervals for aggregated prices.

Abstract

Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day's 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets recently provided by Lago et al. (2020). Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices, as, e.g., needed for pricing individual load profiles.