*4.1. Decision Trees Models Evaluation*

After integrating the active and passive DDM with decision tree models, the results obtained for Building 1 (see Table 3) show that the models with DDM obtained better performance for both algorithms than the model without DDM. Likewise, it is highlighted that the passive method used for training presents better results than the active methods.


**Table 3.** Decision tree model results for Building 1.

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

Table 4 shows the results in Building 2 where it is observed that, like Building 1, the models with DDM present better performance for both algorithms than the model without DDM. However, if we focus on the RMSE and R<sup>2</sup> metrics, the passive method does not clearly show that it obtains better performance than the KSWIN method in the case of XGBoost.


**Table 4.** Decision tree model results for Building 2.

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

The findings show that the decision tree algorithms certainly benefited from the integration of the DDM, showing improvement in the results. When analyzing the detection number, which corresponds to the number of sudden changes detected by the DDM, it could be concluded that for active methods a higher number of detections, which in our case would be the same as the retraining number, could lead to better results. However, when we compare the passive method with the KSWIN method, it can be seen that the results are very approximate but in the case of the KSWIN method, the number of retraining is less than 50% of the retraining performed by the passive method.

Even though the passive method has shown better performance, it cannot be affirmed with certainty that it would be better to use it since it assumes that the data distribution undergoes daily changes, which would not necessarily be true since it could be the case that the behavior of the occupants or energy savings measures causes changes in electricity consumption in periods greater than 24 h and the model is being retrained at a time when it is not necessary.
