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Forecasting Electricity Prices with Expert, Linear and Non-Linear Models

Author

Listed:
  • Anna Gloria Billé

    (Department of Statistical Sciences, University of Padua, Italy)

  • Angelica Gianfreda

    (Department of Economics, University of Modena and Reggio Emilia, Italy; Energy Markets Group, London Business School, UK)

  • Filippo Del Grosso

    (Faculty of Economics and Management, Free University of Bozen, Italy)

  • Francesco Ravazzolo

    (Faculty of Economics and Management, Free University of Bozen, Italy; BI Norwegian Business School; Rimini Centre for Economic Analysis)

Abstract

This paper provides an iterative model selection for forecasting day–ahead hourly electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources (RES), fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA–GARCH models hence including parsimonious autoregressive specifications (known as expert-type models). Results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass and waste, weighted imports and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting. Considering the northern Italian prices and using the Model Confidence Set, predictions indicate a strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and RES improves the forecast accuracy of electricity prices more than predictions freely available to researchers.

Suggested Citation

  • Anna Gloria Billé & Angelica Gianfreda & Filippo Del Grosso & Francesco Ravazzolo, 2021. "Forecasting Electricity Prices with Expert, Linear and Non-Linear Models," Working Paper series 21-20, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:21-20
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    References listed on IDEAS

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    More about this item

    Keywords

    Demand; Wind; Solar; Biomass; Waste; Fossil Fuels (coal; natural gas; CO2); Weighted Inflows; Commercial and Public Forecasts;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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