Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

Antonio Bello, Javier Reneses and Antonio Muñoz
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Antonio Bello: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, Madrid 28015, Spain
Javier Reneses: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, Madrid 28015, Spain
Antonio Muñoz: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, Madrid 28015, Spain

Energies, 2016, vol. 9, issue 3, 1-27

Abstract: One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

Keywords: electricity markets; medium-term electricity price forecasting; probabilistic forecasting; extremely low prices; spikes; hybrid approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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