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Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading

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  • Katarzyna Maciejowska
  • Tomasz Serafin
  • Bartosz Uniejewski

Abstract

This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averaging scheme. The performance of the approach is evaluated on data sets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results show that the newly proposed method outperforms literature benchmarks in terms of statistical measures: the empirical coverage and the Christoffersen test for conditional coverage. Moreover, in line with recent literature trends, the economic value of forecasts is evaluated based on the trading strategy using probabilistic price predictions to optimize the operation of an energy storage system. The results suggest that apart from the use of statistical measures, there is a need for the economic evaluation of forecasts.

Suggested Citation

  • Katarzyna Maciejowska & Tomasz Serafin & Bartosz Uniejewski, 2023. "Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading," Papers 2303.08565, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2303.08565
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    File URL: http://arxiv.org/pdf/2303.08565
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    Cited by:

    1. Tomasz Serafin & Bartosz Uniejewski, 2024. "Ranking probabilistic forecasting models with different loss functions," Papers 2411.17743, arXiv.org.

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