## 2 Related Works  
In recent years, a large number of investigations have focused on the area of artificial intelligence (AI) work with models based on deep learning (DL) techniques, in which they have obtained surprising results in both academic and industrial fields [3; 34; 35; 36]. Furthermore, these DL techniques have been used to model complex and sophisticated patterns for the prediction/forecasting of time series in the energy sector [37]. DL techniques use different neural network structures that allow learning from significant data representations, and in this sense, neural networks, such as the multilayer perceptron, recurrent neural network, have provided useful information for energy decision making for building managers/administators, such as the prediction/forecast of the energy consumption variables of residential and nonresidential buildings [38; 39; 40; 41; 42; 43], building temperature [37; 44], energy load of the building [45; 46; 47], and heating or cooling load of the building [48; 49; 50]. Within the aforementioned perspective, there are different proposals for DL-based model architectures aimed at forecasting the energy consumption of HVAC systems in buildings. Next, we describe some relevant works in this context. [PERSON], [PERSON] et al. [3] proposed DL models for predicting the performance and energy consumption of a heating and cooling (HC) system. They used a feed-forward ANN algorithm based on back-propagation in which they selected several hyperparameters (HPs), such as the activation function, number of hidden layers and nodes, and regularization techniques, to adjust their model. Moreover, they also tested the proposed models using different time intervals and selected the most appropriate input variables for the models. Finally, they emphasized that HC system operating variables are more important than the internal and external temperatures of the building and noted that the DL approach could establish strategies to predict the energy performance of the HC system. Likewise, [PERSON] et al. [51] benchmarked DL-based algorithms (MLP and LSTM) for air conditioning load forecasting to reduce the energy consumption of the building. Their tests found that the LSTM had fewer errors than MLP. They verified that the DL algorithms are capable of solving load forecasting problems. Moreover, [PERSON], [PERSON] et al. [52] proposed an estimation method for energy consumption for building air conditioning systems. They used an RNN with five neurons in the middle layer and time lags of three states. In addition, they used data from a simulated building in the summer and winter seasons, incorporating variables such as preset temperature, on-off state, and the number of people in the zone/room, among others. In this study, the authors demonstrated that, with the proposed method, it is possible to estimate energy consumption. On the other hand, [PERSON] and [PERSON] [4], developed different LSTM-based models for short-term prediction of the HVAC system of a real self-sufficient solar house based on the previous day's behavior. They only experimented with the number of neurons and learning rate. In their work, they highlighted that these results drive the prediction of energy consumption in real-time in buildings. Similarly, [PERSON], [PERSON] et al. [25] compared predictive models, such as the ARIMA model, Multilayer neural network model, and LSTM model for predicting the daily and time energy consumption of air conditioning systems. The authors noted that some HPs tuning had to be made with respect to the LSTM model. They validated their models by comparing the mean average percentage error (MAPE) of each. According to the results obtained, the LSTM model had high adaptability and accuracy in predicting only the energy consumption data of the air conditioning systems. In the work of [PERSON] and [PERSON] [53], an encoder-decoder LSTM-based economic model predictive control (EMPC) framework was developed. They only tuned one, which was the number of nodes in the LSTM layers.