Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market  
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Footnote 1: email: arcos-Vargas.deve.ve. regulatory change was that, due to technological progress, economies of scale had disappeared in some of the links of the electricity system, so that the introduction of competition would lead to a greater welfare for society. Thus, while the transmission and distribution businesses still had strong natural monopoly characteristics (and therefore made sense to regulate them), competition was introduced for the generation and retail businesses. The generators, for their part, comprise the market supply, while the retailers aggregate energy demands, which they then deliver to end users [4]. Electricity price forecasting (EPF) has become a very valuable mechanism for all market partners in deregulated and competitive electricity markets, when this whole process is over, the system operator is responsible for setting a final price established for each hour of the current day. In deregulated and competitive electricity markets, electricity price forecasting has become a high-priced tool for all market participants. Both producers and consumers use day-ahead price forecasts to determine their own bidding strategies to the electricity market. Consequently, the use of enhanced electricity price predictions strongly increases the revenues and decreases the risks of producers [5] and in the case of consumers, it maximizes their utilities [6] and may help with the identification of their needs [7]. Generally accurate forecasting results in lower scheduling costs because it can correctly predict the number of high-priced hours to avoid and the number of low-priced hours to be used [8]. Due to the proximity and the peculiarities of the Spanish electrical system, it was decided to choose this market as a case study, even though it is not one of the easiest to study due to its idiosyncrasy. Transparency measures taken by many governments have made possible free access to many of the data that were previously only accessible to those involved in this market. Nowadays any interested user can manage all the information available. The Spanish electricity market is managed by the Iberian Electricity Market (MIBEL) which was formed as a result of the integration in 2007 of the Spanish electricity system with the Portuguese system. The transmission network is operated by Red Electrica de Espana (REE) in Spain and Redes Energeticas Nacionais (REN) in Portugal, who manage and guarantee the operation. Regarding the management of the wholesale spot market, the authority responsible is the Iberian Energy Market Operator-Spanish Pole (OMIE). The Transmission System Operator (TSO) is Red Electrica de Espana (REE) and is responsible for ensuring the overall functioning and stability of the Spanish electricity system through the operation of the electricity system and the transmission of electricity. In order to achieve all these objectives and also to bring transparency to the system, REE has created an information system called the System Operator Information System (e-sios) which makes the results of the markets or system operation processes public [9]. Additionally, all the market information is provided by de OMIE too. ([[http://www.omie.es](http://www.omie.es)]([http://www.omie.es](http://www.omie.es)), accessed on 20 November 2020). Many methods and ideas have been tried for electricity price forecasting, with varying degrees of success in Spain. In [10], a short-medium-term forecasting methodology based on calendar and temperature correction was proposed to forecast demand values in order to reveal its importance on the electricity price. Furthermore, in [11], an Auto-Regressive Integrated Moving Average (ARIMA) in combination with Radial Basis Function Neural Networks (RBFN) was proposed for electricity price data for the Spanish region with very good results.