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Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models

Author

Listed:
  • Florian Ziel
  • Rafal Weron

Abstract

Conducting an extensive empirical study on short-term electricity price forecasting (EPF), involving state-of-the-art parsimonious expert models as benchmarks, datasets from 12 power markets and 32 multi-parameter regression models estimated via the lasso, we show that using the latter shrinkage approach can bring statistically significant accuracy gains compared to commonly-used EPF models. We also address the long-standing question on the optimal model structure for EPF. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling approach does not uniformly outperform univariate models across all datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This may be an indication that combining advanced structures or the corresponding forecasts from both modeling classes may bring a further improvement in forecasting accuracy. Finally, we also analyze variable selection for the best performing multivariate and univariate high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.

Suggested Citation

  • Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc1608
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    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_16_08.pdf
    File Function: Revised version (2017-12-31)
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Bartosz Uniejewski & Rafal Weron & Florian Ziel, 2017. "Variance stabilizing transformations for electricity spot price forecasting," HSC Research Reports HSC/17/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Papers 1801.01093, arXiv.org.
    4. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2017. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting," HSC Research Reports HSC/17/02, Hugo Steinhaus Center, Wroclaw University of Technology.

    More about this item

    Keywords

    Electricity price forecasting; Day-ahead market; Univariate model; Multivariate model; Regression; Variable selection; Lasso;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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