## 7 Conclusions and Future Works
Therefore, it would be interesting to investigate whether there is an improvement in the forecasting accuracy of the model when tuning some of these HPs [4; 62]. Finally, in future work, we will compare the performance of this model with respect to other Deep Learning models, under the implementation of an intelligent microservices environment within a cloud-connected ecosystem, in order to improve the BMS architecture and extend its functionalities [32] since microservices allow the decoupling of services that require higher computational power (GPU or parallel computing) [101]. **Author Contributions:** Conceptualization, J.M.G.-P. and M.V.-L.; Methodology, L.M.-P., H.C.-G., J.M.G.-P. and M.V.-L.; Software, L.M.-P. and H.C.-G.; Validation, L.M.-P., J.M.G.-P., [PERSON], C.S.d.B. and [PERSON]-[PERSON]; Formal analysis, [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]-[PERSON]; Investigation, [PERSON]-[PERSON], [PERSON] and [PERSON]; Data curation, [PERSON]-P., [PERSON] and [PERSON]; Writing--original draft preparation, [PERSON]-P., [PERSON], [PERSON] and [PERSON]-[PERSON]; Writing--review and editing, [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; Visualization, [PERSON], [PERSON] and [PERSON]; Supervision, [PERSON] and [PERSON]; Project administration, [PERSON] and C.S.d.B. ; Funding acquisition, [PERSON] and C.S.d.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This study forms part of the \"Smart Energy\" Campus of International Excellence (CIE), promoted by the Universidad de Alcala and the Universidad Rey Juan Carlos, for the financial support received by granting the authors the Second prize of the 2019 Awards Call for the research project: \"Intelligent Management System for Optimizing Energy Consumption in Building Air Conditioning\". In addition, this work forms part of the project \"Smart Tool for the Design of Residential Projects and Energetically Sustainable Buildings project\" funding from the FONDO I + D + 1 SENACYT, 2018. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing is not applicable to this article. **Acknowledgments:** We would like to extend special thanks to the management committee of Teatro Real (Madrid, Spain) who contributed with the dataset of the building HVAC System. In addition, we would like to thank the company \"ACIX ENERGIA Y MEDIO AMBIENTE SL.\" for his technical-scientific advice on the concepts related to HVAC systems in singular buildings. Finally, we thank the National Secretariat for Science, Technology and Innovation (SENACYT, Panama) and the Institute for the Training and Use of Human Resources (IFARHU), for the doctoral study scholarship awarded within the IFARHU-SENACYT doctoral scholarship program, 2018. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
**Acronyms** **Description**  
HVAC  
Hearing, ventilating, and air conditioning  
BMS  
Building management system  
ML  
Machine learning  
DL  
Deep learning  
DNN  
Deep neural network  
LSTM  
Long short-term memory  
RNN  
Recurrent neural network  
MLR  
Multiple-linear regression  
ARIMA  
Autoregressive integrated moving average  
DT  
Decision Tree  
RF  
Random Forest  
SVM  
Support vector machine  
ANN  
Artificial neural network  
MLP  
Multilayer perceptron  
HPs  
Hyperparameters  
SGD  
Stochastic gradient descent  
AdaGrad  
Adaptive gradient algorithm  
RMSprop  
Root mean square propagation  
Adam  
Adaptive moment estimation  
ReLu  
Leaky rectified linear unit  
ELU  
Exponential linear unit  
SeLu  
Scaled exponential linear unit  
Tanh  
Hyperbolic tangent  
LF  
Loss function  
MSE  
Mean squared error