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Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging

Author

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  • Katarzyna Maciejowska
  • Jakub Nowotarski
  • Rafal Weron

Abstract

We examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to automate the selection process from among a large set of individual forecasting models available for averaging. We show that the resulting Factor Quantile Regression Averaging (FQRA) approach performs very well for price (and load) data from the British power market. In terms of unconditional coverage, conditional coverage and the Winkler score, we find the FQRA-implied prediction intervals to be more accurate than those of the benchmark ARX model and the QRA approach.

Suggested Citation

  • Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    • Handle: RePEc:wuu:wpaper:hsc1409
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    References listed on IDEAS

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    More about this item

    Keywords

    Probabilistic forecasting; Prediction interval; Quantile regression; Factor model; Forecasts combination; Electricity spot price;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • 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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