## 6 Results and Discussion
From the previous results, we have taken the best set of configurations, including the activation function and number of neurons obtained, defined as follows: C1-T50, C2-T60, C3-T50, and C4-T50. To understand and justify the difference in the number of neurons required for the LSTM layer, we proceeded to analyze the results of the loss function (LF) and the number of epochs used by these configurations with the training-set and validation-set data. Taking into account the above and analyzing the results observed in Figure 7, the C1-T50 configuration showed the best results with training and validation sets since the use of regularization techniques for dropout and early stopping prevented it from presenting any tendency of overfitting in LF-training and LF-validation. It is also appreciated that an average of less than 20 epochs was required to achieve good results, so this model has enough computational capacity to capture temporal variations and make a daily forecast of the energy consumption of the building's heat pump. On the other hand, the C2-T60 configuration, although it employs a greater number of neurons and implements dropout, generated the greatest errors in LF-training and LF-validation. In addition, it had a tendency to overfit since it does not implement early stopping to monitor LF-validation during training, which continued until reaching the established maximum. There are several possible explanations for these results: (i) the established dropout rate was not optimal for this number of neurons; (ii) the influence of other selected HPs such as learning rate, epochs, optimizer, etc. From this perspective, the use of optimization techniques focused on these HPs could improve the model accuracy [3, 47, 100]. Meanwhile, C3-T50 and C4-T50 configurations showed a clear trend to overfit, as LF-training decreased dramatically compared to LF-validation. On the one hand, the C3-T50 configuration, while making use of the early stopping technique, was not enough to prevent overfitting over the training-set. On the other hand, the C4-T50 configuration, by not using regularization techniques, was not able to correctly adapt to the data, causing the model to overfit. Thus, we have observed that using only the early stopping technique or any of the regularization techniques would not guarantee that the model is robust and offers good forecast accuracy in data with high temporal variation (chaotic series), as in the case of complex building HVAC systems. Therefore, the use of the early stopping technique will depend on the data, the size of the data, and the context to which it is directed. On the other hand, to test the robustness and applicability of all the resulting C1-T50, C2-T60, C3-T50, and C4-T50 configurations, we evaluated each configuration of the model with some samples from the testing-set and obtained the corresponding daily forecasts, to compare the accuracy when making the forecast of a day, as well as determine the relative errors obtained in the forecast of the average and maximum daily energy consumption of the heat pump. In this sense, the results observed in Figure 8 show good accuracy of the configurations in the forecast and in detection of the daily temporal variation of the energy consumption of the heat pump. However, in accordance with the aforementioned results and what is observed in Tables 8-11, the C2-T60, C3-T50, and C4-T50 configurations obtained values of the relative error of the average daily energy consumption of (7.78%, 7.80%, 7.66%) and the maximum daily energy consumption of (10.55%, 9.14%, 10.18%), respectively. The C1-T50 configuration obtained a relative error of daily average energy consumption of 7.73% and daily maximum energy consumption of 11.80%.