*4.2. Deep Learning Models Evaluation*

After integrating the active and passive DDM with deep learning models, the results obtained for Building 1 (see Table 5) show that for the TCN, the model without DDM obtains better performance than the models with DDM. However, in the case of CNN, it is observed that the model without DDM obtains better performance than the active methods but not better than the passive method if we focus on the RMSE and R<sup>2</sup> metrics.


**Table 5.** Deep learning model results for Building 1.

Wo/DDM = without drift detection method, ND = numbers of detections, n/a = not applicable.

Table 6 shows the results in Building 2 where it is observed that, like Building 1, the TCN obtains better performance without DDM. However, for CNN, if we focus on the RMSE and R2 metrics, the KSWIN method obtained better performance than the model without DDM.

**Table 6.** Deep learning model results for Building 2.


Wo/DDM = without drift detection method, ND = numbers of detections, n/a = not applicable.

For the deep learning models, the findings show that the ADWIN method, which performs the smallest amount of retraining, presents the worst performance of the active methods, while the passive method presents the better performance. However, in general, the model without DDM obtains better performance except in the RMSE and R<sup>2</sup> metrics for CNN with DDM. Which would suggest that the type of change in the data distribution is not abrupt enough to require the retraining of the deep learning models.

This behavior in the performance of the deep learning models would make us question the need for retraining in this case, but if we compare the outcomes of the decision tree models versus the deep learning models, it can be seen that, in the case of Building 2 where the deep learning models without DDM have better performance than the decision tree models without DDM when DDM is applied, decision tree models perform better than deep learning models without DDM.

Figure 5 shows the average error of the forecast algorithms by hours of the electrical consumption of the entire building from the first hour that is forecast for each algorithm. As can be seen, when we analyze the average error per hour in each of the buildings, we realize that the decision tree models, when integrating the DDMs, improve their performance in each of the hours, however, this is not the case for deep learning models.

**Figure 5.** (**a**) Performance of forecasting algorithms without DDM by hours in Building 1. (**b**) Performance of forecasting algorithms without DDM by hours in Building 2. (**c**) Performance of forecasting algorithms with DDM by hours in Building 1. (**d**) Performance of forecasting algorithms with DDM by hours in Building 2.

The results show that the proposed method can be applied to maintain or even improve the performance of learning algorithms in situations where there are constant changes in the behavior of electrical consumption in buildings. A limitation is the drift detection methods that were integrated. In the case of ADWIN, only the confidence value parameter was allowed to be modified, while in the case of KSWIN an inappropriate modification of the values of the size of windows would cause the method to not detect sudden changes in the distribution data.
