###### Abstract  
Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain. d 2021 July 22  
This would allow the implementation of planning strategies, optimization of the operations of these systems, and reduction in energy peaks and associated costs [8; 9]. However, before building a model for forecasting the energy consumption of HVAC systems, it is important to consider two fundamental factors: the past observations or time lags and the time horizon to be forecasted [4; 8]. On the one hand, the time lags have been of interest in recent years since they allow for an increase in the accuracy capacity of the predictions of the dynamic environment models [8] and, on the other hand, the forecast time horizon delimits from which point in time the model will carry out the forecast. The steps of this action plan can be regrouped into three phases: short-, medium- and long-term [10; 11]. Short-term forecasts are focused on the day-to-day operations of energy systems, peak energy demand, daily energy consumption, short-term electric market, fault detection, and diagnosis, etc. [12; 13]. Medium-term and long-term forecasts are directed to determine equipment modernization, create energy saving strategies, and modify electric market plans, among others [8]. Within this framework, numerous data-driven models (i.e., statisticians and machine learning (ML)) have been used for the short-term forecasting of the energy consumption of HVAC systems in buildings. Some of these are: autoregressive integrated moving average (ARIMA), multiple-linear regression (MLR), support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural networks (ANNs), among others [14; 15; 16; 17; 18; 19]. While these models offer a good approximation when forecasting/predicting the energy consumption of the HVAC system in buildings, they are very generalist, noise-sensitive models and have a lower level of abstraction [16; 20; 21; 22; 23; 24; 25] since the data of these systems have many disturbances and variations while they are in ordinary operation. In addition, the various processes and complex features inherent in buildings significantly affect the behavior of HVAC systems [5; 18; 26; 27]. To overcome the shortcomings of the aforementioned models, deep learning (DL)-based models have exposed promising alternatives since they allow automatic discovery of the intrinsic structure of the data without the need for expert knowledge in the domain [15; 20; 28; 29]. Therefore, DL models are applicable to the analysis and forecasting of complex and dynamic building energy systems such as HVAC systems. In fact, DL models based on the long short-term memory (LSTM) neural network are very useful in this context (as discussed in the following sections).