**2. Methodology and Approach**

The use of drift detection methods is well known, however, the integration of these methods into a multi-step forecasting strategy to predict continuous hourly electricity energy consumption in the entire building turns out to be a novel topic.

Therefore, this section describes data preprocessing, forecasting algorithms, drift detection methods, and performance metrics used in this article. Section 2.1 provides information on how the datasets from the two buildings used to train the learning algorithms were made. Section 2.2 presents the approach and the learning algorithms used to forecast the electrical consumption in buildings. Section 2.3 describes the drift detection methods and their incorporation into the learning algorithms. Section 2.4 explains the metrics used for evaluating the performance of learning algorithms. A summary of the methodology used is shown in Figure 1.

**Figure 1.** Methodology used for the analysis of the integration of drift detection methods.
