## 4 Results
### Datasets Description  
In Spain, the electricity system underwent a process of liberalisation between 1997 and 1998 [37], in which the tasks of transmission and distribution (regulated) and generation and retail (competitive) were separated as it has been presented above. The Spanish electricity market is in charge of the OMIE, and it is organised in sequential sessions: the day-ahead, intraday, and balancing sessions. The price of electricity is different for each of the hours of each day; this price is established by means of an auction. First, power generation companies and wholesale retailers buyers are convened in the day-ahead spot market to send their quotes that include energy price pairs within 24 h of the next day. The market operator (MO) is responsible for clearing the market and providing a temporary energy plan for each bidder within 24 h of the next day. By matching all the purchase and sale bids, the electricity market establishes the price that the energy will have for each of the hours of the following day, i.e., the hourly marginal price is obtained at the intersection of the supply and demand curves [38]. Every day, between 12:30 and 13:00 of the current day \(D\), the price that the energy will have in each of the hours of the following day \(D+1\), within the daily market, is made public [39]. As it can be intuited, it is very important for the system operators to have an idea of what the prices may be in the short term, as this will significantly influence the offers they make, so a system capable of making predictions for the next day is an indispensable tool for them [40]. The main objective of this study is to see how the choice of data can influence the effectiveness of the prediction results. For each of the chosen data sets, three months have been selected, with a total of 2208 time points (hours) for each dataset, with two of them being used to set the parameters of the models (train/test step), and the remaining month to validate them (validation step). For each of the three data sets, a normalization of the data has been carried out, the type of normalization is also a parameter to be chosen in the first stage, the possible values are a _minimax_ or _z-score_ normalization. Attempts have been made to choose time periods with some particularity. These are detailed below:  
* **Fraud Period:** In 2019, the National Commission for Markets and Competition (CNMC) fined Endesa and Naturgy for altering electricity prices between October 2016 and January 2017 [41], the months chosen for this study. This period will help to test how sensitive the models are to periods that may present tax irregularities. To make the results more comparable, the following periods used will have the same length, i.e., three months. Hourly electricity price in this period can be observed in Figure 3 where the yellow line represents the mean price of all the period. * **Normal period:** In order to have a reference period, a stage has been chosen in which no anomalies were detected, unlike the two next data sets. This stage covers the months from September to December 2019. Figure 4 represents the hourly electricity price in this period, with the mean represented by a horizontal yellow line. * **Quarantine Period:** The year 2020 marked a change in many daily habits due to the global pandemic. One of the most notable changes was the increase in electricity consumption, as people spent more time at their homes due to quarantine requirements. The three months from 15 March 2020 to 15 June 2020 have been chosen to represent this stage. Hourly electricity price in this period can be observed in Figure 5 where the yellow line represents the mean price of all the period. Figure 4: Time series of normal period. On the x-axis we can see that this period covers electricity prices from September to December 2019. The horizontal line which can be seen indicates the average for that period with a value of 48.91. Figure 3: Time series of fraud period.