## 5 Conclusions and Future Works  
In this paper, we proposed a strategy based on neuroevolution in order to predict the short-term electric energy demand. In particular, we used a genetic algorithm in order to obtain the architecture of a deep feed-forward neural network provided by the H2O big data analysis framework. The resulting networks have been applied to a dataset registering the electric energy consumption in Spain over almost 10 years. The results were compared with other standard and machine learning strategies for time-series forecasting. For the experimentation performed we can conclude that the methodology we proposed in this paper is efficient for short-term electric energy forecasting, and on the particular dataset used in this paper the proposed strategy obtained the best performances. It is interesting to notice that our proposal outperforms the other ten strategies in all the cases, and that even when a historical window of 96 reads was used, our proposal achieved more precise predictions than any other methods with any other historical window size. As for future work, we intend to apply the framework proposed in this paper to other datasets, and also to other kinds of time-series, in order to check the validity of our proposal also in other fields. Moreover, we intend to overcome a present limitation of the current proposal. In fact, the R GA package we have used does not allow to optimize parameter of different types, e.g., real and integer parameters. In order to overcome this, in this proposal we had to treat all the parameters as real. However, this causes the search space dimension to increase drastically. In the future we intend to solve this problem as well, and by reducing the size of the search space, we are confident that better \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & \(w\) & & & \\ \hline  & **24** & **48** & **72** & **96** & **120** & **144** & **168** \\ \hline NDL & 3.01 (0.90) & 2.38 (0.69) & 2.08 (0.57) & 1.85 (0.55) & 1.60 (0.46) & 1.51 (0.46) & 1.44 (0.42) \\ CNN & 4.08 (0.04) & 3.16 (0.03) & 2.69 (0.02) & 2.51 (0.02) & 2.30 (0.02) & 1.71 (0.02) & 1.79 (0.02) \\ LSTM & 2.43 (0.03) & 2.05 (0.02) & 1.82 (0.02) & 2.08 (0.02) & 1.74 (0.02) & 1.78 (0.02) & 1.97 (0.02) \\ FFNN & 4.51 (0.52) & 3.46 (0.33) & 3.39 (0.30) & 3.12 (0.42) & 2.98 (0.28) & 2.32 (0.29) & 2.46 (0.29) \\ ARIMA & 8.82 (5.31) & 8.26 (4.73) & 11.37 (10.43) & 14.03 (13.00) & 6.79 (2.53) & 7.63 (2.54) & 6.92 (2.97) \\ DT & 9.52 (1.55) & 9.45 (1.48) & 9.33 (1.39) & 9.40 (1.45) & 9.08 (1.12) & 8.86 (1.01) & 8.79 (0.96) \\ GBM & 8.07 (3.82) & 6.59 (2.71) & 5.73 (2.23) & 5.33 (2.08) & 5.02 (1.81) & 4.49 (1.54) & 4.45 (1.56) \\ RF & 4.39 (2.13) & 3.69 (1.71) & 2.93 (1.16) & 2.78 (1.04) & 2.45 (0.79) & 2.22 (0.71) & 2.15 (0.69) \\ EV & 4.49 (1.91) & 3.98 (1.52) & 3.48 (1.18) & 3.42 (1.15) & 3.19 (0.95) & 3.15 (0.90) & 3.09 (0.84) \\ NN & 4.39 (2.23) & 4.27 (2.16) & 4.13 (2.05) & 3.55 (1.56) & 3.15 (1.41) & 2.16 (0.78) & 2.08 (0.74) \\ ENSEMBLE & 3.58 (1.65) & 2.95 (1.19) & 2.64 (0.99) & 2.57 (0.97) & 2.38 (0.81) & 1.94 (0.69) & 1.88 (0.67) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average results obtained by different methods for different historical window values. Standard deviation between brackets. configurations of the deep learning can be found. The use of on-line learning will also be explored in future works in order to speed up the prediction process and reduce the volume of stored data. [PERSON] conceived and partially wrote the paper. [PERSON] launched the experimentation. [PERSON] and [PERSON] addressed the reviewers comments. [PERSON] validated the experiments. All authors have read and agree to the published version of the manuscript. This research received no external funding. The authors would like to thank the Spanish Ministry of Science, Innovation and Universities for the support under project TIN2017-88209-C2-1-R. This work has also been partially supported by CONACYT-Paraguay through Research Grant PINV18-661. The authors declare no conflict of interest.