Short-term electricity price forecastingmodels comparative analysis: Machine Learning vs. Econometrics

Antoine Ferré, Guillaume de Certaines, Jérôme Cazelles, Tancrède Cohet, Arash Farnoosh and Frédéric Lantz
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Antoine Ferré: IFPEN - IFP Energies nouvelles, IFP School
Guillaume de Certaines: IFPEN - IFP Energies nouvelles, IFP School
Jérôme Cazelles: IFPEN - IFP Energies nouvelles, IFP School
Tancrède Cohet: IFPEN - IFP Energies nouvelles, IFP School
Arash Farnoosh: IFPEN - IFP Energies nouvelles, IFP School
Frédéric Lantz: IFPEN - IFP Energies nouvelles, IFP School

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Abstract: This paper gives an overview of several models applied to forecast the day-ahead prices of the German electricity market between 2014 and 2015 using hourly wind and solar productions as well as load. Four econometric models were built: SARIMA, SARIMAX, Holt-Winters and Monte Carlo Markov Chain Switching Regimes. Two machine learning approaches were also studied: a Gaussian mixture classification coupled with a random forest and finally, an LSTM algorithm. The best performances were obtained using the SARIMAX and LSTM models. The SARIMAX model makes good predictions and has the advantage through its explanatory variables to better capture the price volatility. The addition of other explanatory variables could improve the prediction of the models presented. The RF exhibits good results and allows to build a confidence interval. The LSTM model provides excellent results, but the precise understanding of the functioning of this model is much more complex.

Keywords: Energy Markets; Renewable Energy; Econometric modelling; Bootstrap Method; Merit-Order effect (search for similar items in EconPapers)
Date: 2021-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-ets and nep-for
Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-03262208
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Citations: View citations in EconPapers (2)

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