## 1 Introduction
To this aim, evolutionary algorithms (EAs) can be used in order to find good configurations of the deep neural networks. Individuals can be set of parameter values, and their fitnesses are determined based on how well they can be trained to perform in the task. This field is known as neuroevolution, which, in a nutshell, can be defined as a strategy for evolving neural networks with the use of EAs [11]. Usually, deep artificial neural networks (DNNs) are trained via gradient-based learning algorithms, namely backpropagation, see for example [12]. EAs can be used in order to seek the optimal values of hyper parameters, for the example the learning rates, or the number of layers and the amount of neurons per layer, among others. It has been proven that EAs can be combined with backpropagation-based techniques, such as Q-learning and policy gradients, on difficult problems, see, e.g., [13]. In fact, the problem of setting parameters for such methods is not trivial, and, if the parameters are not correctly set, the forecasting can be poor. The above observations motivate us to use a neuroevolution approach in order to tackle the short-term energy load forecasting problem. In order to validate our proposal, we applied it to a dataset regarding the electric energy consumption registered over almost 10 years in Spain. We have also compared our proposal with other standard and machine learning (ML) strategies, and results obtained confirm that our proposal achieves the best predictions. In the following, we summarise the main contributions of this paper:  
1. We propose a new general-purpose approach based on deep learning for big data time-series forecasting. Due to the high computational cost of the deep learning, we adopted a distributed computing solution in order to be able to process large time series. 2. The hyper-parameter tuning and optimization of the deep neural networks is a key factor for obtaining competitive results. Usually, the hyper-parameters of a deep neural network are pre-fixed previously or computed by a grid search, which performs an exhaustive search through the whole set of established hyper-parameters. However, the grid search presents an important limitation: it works with discrete values, which greatly limits the fine-tuning of the vast majority of hyper-parameters. Thus, an evolutionary search is proposed to find the hyper-parameters. 3. We conduct a wide experimentation using Spanish electricity consumption registered over 10 years, with measurements recorded every 10 min. Results show a mean relative error of 1.44%, demonstrating the high potential of the proposed approach, also compared to other forecasting strategies. 4. We evaluate our proposal predictive accuracy and compare it with a strategy based on deep learning using a grid search for setting the hyper parameters. The evolutionary search showed to be effective in order to achieve higher accuracy. 5. In addition, we compare the approach with seven state-of-the-art forecasting algorithms such as ARIMA, decision tree, an algorithm based on gradient boosting, random forest, evolutionary decision trees, a standard neural network and an ensemble proposed in [14], outperforming all of them. 6. We analyze how the size of the historical window affects the accuracy of the model. We found that when using the past 168 values as input features to predict the next 24 values the best results were obtained. The rest of the paper is organized as follows. In Section 2 we provide a brief overview of the state of the art of electric energy time-series forecasting. The dataset used in this work is described and analyzed in Section 3.1, while the methodology used is discussed in Section 3.2. In Section 4 we describe the results obtained by our approach and compare them to those achieved by other strategies. Finally, we draw the main conclusions and identify futures works in Section 5.