## 3 Data and Methodology  
### Data  
In order to assess the quality of our proposal, we used a dataset containing information regarding the global electricity consumption registered in Spain (in MW), available at [48].  
In particular, the data were recorder over a period going from 1 January 2007 at midnight until 21 June 2016 at 11:40 pm, which amounts to nine years and six months. Specifically, the data is relative to the consumption measured at 10 minutes intervals, meaning that the dataset consists of a total of 497,832 measurements. No missing values or outliers were found, since data are provided by the Spanish Nominated Electricity Market Operator (NEMO) and all data are already preprocessed and cleaned.  
Time-series regarding the electric energy demand are typically non-stationary. This fact renders the problem of forecasting the electric energy demand challenging, since such time-series present statistical properties, such as the mean, variance and autocorrelation, that are not all constant over time. It follows that they can present changes in variance, trends or seasonal effects. For this reason, we performed a preliminary study of the dataset in order to assess whether or not the time-series used in this paper is stationary. To this aim, we analyzed the AutoCorrelation Function (ACF) and the Partial AutoCorrelation Function (PACF) of the time-series, which are reported in Figure 1.  
From Figure 1a, we can notice that the time-series has a high correlation with a number significant of lags, while from Figure 1b we can see that there are four spikes in the first lags, from which we can determine the order of autoregression of the time-series. From these observations, we can conclude that the time-series is not stationary, and that the order of autoregression to be used should be 4.  
A preprocessing of the dataset had to be applied before it could be used. In particular, we used the preprocessing strategy proposed in [36], which is graphically depicted in Figure 2. In a first step, we extract the attribute corresponding to the energy consumption, obtaining in this way a consumption vector \(V_{c}\).  
From \(V_{c}\) matrix \(M_{c}\) is built. The size of \(M_{c}\) depends on the values of the historical window (\(w\)) and of the prediction horizon (\(h\)) used. Notice that \(w\) determines the number of previous entries that will be used in order to induce a forecasting model that will be used to estimate the subsequent \(h\) values.  
In this work, as in [36], \(h\) was set to 4 hours, which corresponds to a value of 24 reads. Various values of \(w\) were tested.  
In particular, \(w\) was set to values 24, 48, 72, 96, 120, 144 and 168. Such values correspond to 4, 8, 12, 16, 20, 24 and 28 hours, respectively.  
One the matrix \(M_{c}\) has been obtained, we divided the resulting dataset into a 70% used as a training set, while the remaining 30% was used as a testing set. This means that the prediction model was obtained using only the training set. The forecasting performances of the so induced model are  
Figure 1: Correlation plots for the original time-series. (**a**) AutoCorrelation Function (ACF); (**b**) Partial AutoCorrelation Function (PACF).  
Figure 2: Dataset pre-processing. \(w\) determines the amount of historical data used, while \(h\) represents the prediction horizon.  
assessed on the test set, which basically represents unseen data. Within the training set, a 30% is used as a validation set for determining the deep learning hyperparameters.  
These preprocessing steps yield the generation of seven different matrices, whose information is reported in Table 1. Note that for all the obtained datasets, the last 24 columns represent the prediction horizon.