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

Oliver Grothe, Fabian Kächele and Fabian Krüger

Energy Economics, 2023, vol. 120, issue C

Abstract: Modeling price risks is crucial for economic decision making in energy markets. Besides the risk associated with a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for generating 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. Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices as needed for pricing individual load profiles.

Keywords: Electricity price forecasting; Probabilistic forecast; Multivariate forecasting; Copulas; Day-ahead market; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C52 C53 L94 Q47 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:120:y:2023:i:c:s0140988323001007

DOI: 10.1016/j.eneco.2023.106602

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