## 7 Conclusions and Future Works  
In this study, we developed an LSTM-based model aimed at forecasting the daily energy consumption of the HVAC system in buildings, specifically a heat pump at the Teatro Real in Spain. In particular, we focused on determining the time lags that best suits the need. In addition to identifying the best tuning of hyperparameters (HPs) for the LSTM layer, we analyzed the implementation of dropout and early stopping regularization techniques during learning the proposed model. From this point of view, we have compared different model configurations with respect to the actual data provided by the building's BMS, in which each model configuration implemented one, two, or none of the above regularization techniques. Subsequently, we evaluated, for all these model configurations, multiple executions of several time lags (one to seven days), with HPs as the activation function (_SeLu_ or _Tanh_) and a number of 50, 60, and 70 neurons. As a result, we analyzed and evaluated the forecast accuracy of each model configuration with respect to the various results obtained in the evaluation metrics (R2, RMSE, CVRMSE). From the experiments conducted, we identified that the CVRMSE measurement results obtained by the model configurations were within the acceptable accuracy range (<0.30) for the global uncertainty in the prediction of energy use according to the criteria indicated by the ASHRAE, IPMVP, and FEMP guidelines to validate an HVAC calibrated model [93; 94; 95; 96]. Additionally, for all configurations, we determined that the best time lag to make a daily forecast is the previous seven days. This suggests that models aimed at forecasting the energy consumption of the HVAC system should consider a wide time lag to capture all the patterns or temporal variations of these systems and the building [5; 18; 26; 27], so it could be a baseline for researchers studying similar complex cases. In addition, we determined the HPs of the C1 to C4 configurations with the best performance in the metrics. The _Tanh_ activation function was more accurate in these configurations compared with the _SeLu_ activation function, while the best number of neurons was 50 for the C1-T50, C3-T50, and C4-T50 configurations and 60 neurons for the C2-T60 configuration. Based on this evidence, we consider that the activation functions and the number of neurons will depend on many factors, such as data size and variations, model complexity, HPs, use of regularization techniques, or the context in which it is studied. Therefore, the application of optimization techniques for some of these factors would improve the accuracy for model prediction [3], although this would imply a high computational cost [36; 62]. We also observed that dropout and early stopping regularization techniques can work together in analogous scenarios, as they easily capture volatility, nonlinearity, and hidden patterns during DNN training. In fact, we have demonstrated that the configuration of model C1-T50, which implemented these regularization techniques, was able to determine the relative error of the forecast of average and maximum daily energy consumption by 7.73% and 11.80%, respectively. In addition, this model has a lower performance in the CVRMSE metric of 0.095 than that proposed by the ASHRAE, IPMVP, and FEMP guidelines. Therefore, it generates generalizable forecasts and is capable of capturing the temporal variations of a complex building such as the one studied. Taking into account the above, this HVAC system energy consumption forecast model could be part of a broader framework, in which, through the analysis of the forecast generated, intelligent decisions can be made, such as performing preventive maintenance, fault detection and diagnosis, optimization of operation modes, etc. On the other hand, it should be noted that the proposed model can be improved by adjusting other HPs, such as the learning rate, optimizer type, number of epochs, dropout rate, and number of delay or \"patience\" for early stopping.