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Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals

Author

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  • Katarzyna Maciejowska
  • Rafal Weron

Abstract

In this paper we investigate whether considering the fine structure of half-hourly electricity prices, the market closing prices of fundamentals (natural gas, coal and CO2) and the system-wide demand can lead to significantly more accurate short- and mid-term forecasts of APX UK baseload prices. We evaluate the predictive accuracy of a number of univariate and multivariate time series models over a three-year out-of-sample forecasting period and compare it against that of a benchmark autoregressive model. We find that in the short-term, up to a few business days ahead, a disaggregated model which independently predicts the intra-day prices and then takes their average to yield baseload price forecasts is the best performer. However, in the mid-term, factor models which explore the correlation structure of intra-day prices lead to significantly (as measured by the Diebold-Mariano test) better baseload price forecasts. At the same time, we observe that the inclusion of fundamental variables - especially natural gas prices (in the short-term) and coal prices (in the mid-term) - provides significant gains. The CO2 prices, on the other hand, generally do not improve the price forecasts at all, at least in the time period considered in this study (Apr. 2009 - Dec. 2013).

Suggested Citation

  • Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc1504
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    References listed on IDEAS

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    Cited by:

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    2. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    3. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    4. Rick Steinert & Florian Ziel, 2018. "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures," Papers 1801.10583, arXiv.org.

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    More about this item

    Keywords

    Electricity price; Forecasting; Vector autoregression; Factor model; Principal components;
    All these keywords.

    JEL classification:

    • 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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