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

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  • Grothe, Oliver
  • Kächele, Fabian
  • Krüger, Fabian

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.

Suggested Citation

  • Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:eneeco:v:120:y:2023:i:c:s0140988323001007
    DOI: 10.1016/j.eneco.2023.106602
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    Cited by:

    1. Loizidis, Stylianos & Kyprianou, Andreas & Georghiou, George E., 2024. "Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets," Applied Energy, Elsevier, vol. 363(C).
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    3. Nie, Ying & Li, Ping & Wang, Jianzhou & Zhang, Lifang, 2024. "A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism," Applied Energy, Elsevier, vol. 366(C).
    4. Yang, Yifan & Guo, Ju’e & Li, Yi & Zhou, Jiandong, 2024. "Forecasting day-ahead electricity prices with spatial dependence," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1255-1270.
    5. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).
    6. Maria da Graça Ruano & Antonio Ruano, 2024. "A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting," Energies, MDPI, vol. 17(3), pages 1-49, January.

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    More about this item

    Keywords

    Electricity price forecasting; Probabilistic forecast; Multivariate forecasting; Copulas; Day-ahead market; Forecast evaluation;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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