Citation: Fernandes, R.; Soares, I. Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market. Energies 2022, 15, 5020. https:// doi.org/10.3390/en15145020 Academic Editor: Tek Tjing Lie Received: 31 May 2022 Accepted: 28 June 2022 Published: 9 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Review Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market Renato Fernandes 1,2,† and Isabel Soares 2,3, * ,† 1 Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC), 4200-465 Porto, Portugal; renato.s.fernandes@inesctec.pt 2 Faculty of Economics, University of Porto, 4200-465 Porto, Portugal 3 Centro de Economia e Finanças da UP (CEF.UP), 4200-465 Porto, Portugal * Correspondence: isoares@fep.up.pt † These authors contributed equally to this work. 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 1. Introduction Since the EU Directives and, most particularly, over the last ten years, EU electricity markets have started shifting towards deregulation and increased competition. This had led to much uncertainty in all market agents but also to increased sophistication in the strategic behaviour of various utilities: improving efficiency either by self-improvement, often through outsourcing, or shutting down businesses with low returns while creating new and innovative businesses. Therefore, market modelling accuracy becomes crucial, namely concerning price volatility and agents’ behaviour, allowing for a timely adjustment of investment strategies and prevention/mitigation of market risks. On the older deregulated electricity markets, the offer and supply have already sta- bilised. This means that even though there are still some changes in these markets, they no longer cause any drastic and disconnected ruptures but mainly cause small continuous shifts in the regular operation, depending on the gradual evolution of each market factor. There are many contributions in the literature concerning electricity market operation and price determination, spanning through different methodologies and different datasets. It is very important to understand the current electricity market trends and to prepare for the new challenges arriving, which will potentially change the market drastically. However, it becomes difficult to forecast the impact of these new changes if, for each market, it is not possible to identify the methodology that should be used. Therefore, this paper is to consolidate these studies under a single data set to analyse the results and make transparent the advantages and disadvantages of each methodology. In this paper, for the same data set, 19 different models are considered from economet- rics, statistics, and artificial intelligence. These models were chosen for being the most cited Energies 2022, 15, 5020. https://doi.org/10.3390/en15145020 https://www.mdpi.com/journal/energies