# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 5 Conclusions  
Machine learning and deep learning models are important techniques for building energy forecasting, so it is necessary to accompany them with strategies that can make the most of these techniques. In this paper, an energy consumption forecasting strategy to predict day-ahead hourly energy consumption in smart buildings is presented and tested on real data from two buildings located in Valladolid, Spain. The proposed strategy is different from other strategies because it can predict the energy consumption of the next 24 h at any hour of the day, which is of interest to smart buildings and microgrids.  
For the energy consumption forecast, a dataset composed of historical data, meteorological data, calendar data, and past series values was used. Different machine learning and deep learning models were used to test the strategy. Of those models, the best ones were considered for the build of an assembled model using the mean of the prediction values. In general, the assembled models obtained better results than the single models. The assembled model with the combination of five models was the best one for building 1 with a MAPE of 7.85% and the assembled model with the combination of four models was the best one for building 2 with a MAPE of 15.14%. Confirming what authors such as [40,41] have mentioned that the combination of several forecasting models can cancel out random errors, achieving in some cases more accurate forecasts.  
We are currently working to improve the accuracy of the prediction. In the same way, future lines of research would be to improve the composition of the dataset including occupancy data to achieve better adaptability of the forecast when there are radical changes such as the periods when the university closes and when it starts, which is where it has been seen that the model generates the greatest error. As well as being able to reduce the learning patterns of the strategy to make it possible to use it with fewer patterns and obtain better results.  
Conceptualization, D.M.-H., L.H.-C., and M.S.; methodology, D.M.-H. and L.H.-C.; software, M.S. and D.M.-H.; validation, L.G.-M., A.Z.-L., O.D.-P., and F.S.-G.; formal analysis, D.M.-H., M.S., and L.H.-C.; investigation, D.M.-H. and M.S.; data curation, M.S. and D.M.-H.; writing--original draft preparation, D.M.-H.; writing--review and editing, L.H.-C., O.D.-P., and A.Z.-L.; visualization, D.M.-H.; supervision, L.H.-C. All authors have read and agreed to the published version of the manuscript.  
Figure 8: (a) Real versus predicted energy consumption for building 1; (b) real vs. predicted energy consumption for building 2.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  
The authors acknowledge the University of Valladolid and the Instituto Tecnologico de Santo Domingo for their support in this research, which is the result of a co-supervised doctoral thesis. The authors also acknowledge the CITIES thematic network, a member of the CYTED program. The authors declare no conflict of interest.