## 2 Related Works  
As previously mentioned, a lot of attention has been paid to short-term electricity consumption forecasting during the last decades. This section provides a brief overview of up-to-date related works. We can distinguish two main strategies to predict energy consumption. A first strategy is based on conventional methods, e.g., [15; 16], whilst an alternative, and more recent strategy, is based on ML techniques. Conventional methods include, among others, statistical analysis, smoothing techniques such as the autoregressive integrated moving average (ARIMA), exponential smoothing and regression-based approaches. Such techniques can obtain satisfactory results when applied to linear problems. In contrast, ML strategies are also suitable for non-linear cases. We refer the reader to [17] for an expanded survey on data mining techniques applied to electricity-related time-series forecasting. In this work, several markets and prediction horizons are considered and discussed. Popular ML techniques successfully applied to the forecasting of power consumption data include Artificial Neural Networks (ANN) [18; 19; 20] or Support Vector Machines (SVM), see, for instance, [21; 22]. Other strategies are based on pattern similarity [23; 24]. Since 2011, when the Pattern Sequence based Forecasting (PSF) algorithm was published [24], a number of variants has been proposed for forecasting this kind of time-series [25; 26; 27; 28], including an R package [29] and a big data version [30]. Grey forecast models have also been used for predicting time-series. In particular such an approach has been applied to forecast the demand of natural gas in China. For instance, in [31] a self-adapting intelligent grey prediction model was proposed, where a linear function was used in order to automatically optimize the parameters used by the proposed grey model. This strategy was substituted with a genetic algorithm in [32], which resolved various limitations of the previous mechanism. A novel time-delayed polynomial grey model was introduced in [33], while in [34] authors proposed a least squares support vector machine model based on grey analysis. Recently, Deep Learning (DL) has also been applied to this problem, see, e.g., [9; 35]. However, to the best of our knowledge, a part from the early version [36] and few other works, such as [37], in which Brazilian data were analyzed, or [38] for Irish data, or [39] for Chinese data, no other works based on DL can be found in the literature. Although ML techniques provide effective solutions for time-series forecasting, these methods tend to get stuck in a local optimum. For instance, ANN and SVM may get trapped in a local optimum if their configuration parameters are not properly set. Recently, methods developed for big data environments have also been applied to electricity consumption forecasting. In [40] an approach based on the \(k\)-weighted nearest neighbours algorithm was introduced and implemented using the Apache Spark framework. The performances of the resulting algorithm were tested using a Spanish energy consumption Big Data time-series. As mentioned above, in 2018, [PERSON] et al. [9] proposed a DL model to deal with big data time-series forecasting. In particular, the H2O Big Data analysis framework was used. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 were reported. As can be seen, although much attention has been paid to the electricity consumption forecasting problem, few works based on DL have been proposed. Moreover, such existing works did not applied any metaheuristic strategy to set the parameters. These facts highlight the existing gap in the literature and justify, from the authors' point of view, the development of this work. As previously stated, in this paper we aim at using DL, in order to perform time-series forecasting. In DL, many parameters have to be set. The setting of such parameters have a great influence on the final results obtained by such a strategy.