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Reducing complexity in multivariate electricity price forecasting

Hendrik Kohrs (Department of Risk Management and Quantitative Analysis, VNG Handel and Vertrieb GmbH, Leipzig, Germany)
Benjamin Rainer Auer (Chair of Finance, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany)
Frank Schuhmacher (Department of Finance, University of Leipzig, Leipzig, Germany)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 19 August 2021

Issue publication date: 3 January 2022

61

Abstract

Purpose

In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue.

Design/methodology/approach

In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems.

Findings

This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study.

Originality/value

With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

Keywords

Acknowledgements

The authors thank an anonymous reviewer for valuable comments and suggestions. The authors are also indebted to the market data team of the European Energy Exchange (EEX) for kindly supplying the data set used in the study.

Citation

Kohrs, H., Auer, B.R. and Schuhmacher, F. (2021), "Reducing complexity in multivariate electricity price forecasting", International Journal of Energy Sector Management, Vol. 16 No. 1, pp. 21-49. https://doi.org/10.1108/IJESM-12-2020-0017

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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