Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market

Renato Fernandes and Isabel Soares
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Renato Fernandes: Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC), 4200-465 Porto, Portugal
Isabel Soares: Faculty of Economics, University of Porto, 4200-465 Porto, Portugal

Energies, 2022, vol. 15, issue 14, 1-17

Abstract: In this paper, for the data set of the Iberian Electricity Market for the period 1 January 2015 to 30 June 2019, 19 different models are considered from econometrics, statistics, and artificial intelligence to explain how electricity markets work. This survey allows us to obtain a more complete, critical view of the most cited models. The machine learning models appear to be very good at selecting the best explanatory variables for the price. They provide an interesting insight into how much the price depends on each variable under a nonlinear perspective. Notwithstanding, it might be necessary to make the results understandable. Both the autoregressive models and the linear regression models can provide clear explanations for each explanatory variable, with special attention given to GARCHX and LASSO regression, which provide a cleaner linear result by removing variables that have a minimal linear impact.

Keywords: electricity market; machine learning; autoregressive; linear regression; GARCHX; LASSO (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: 2022
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