**1. Introduction**

Buildings presently produce up to 40% of worldwide energy consumption and 30% of carbon dioxide emissions, numbers which are constantly increasing due to urbanization [1]. Additionally, considering the long life expectancy of buildings, it is assessed that 85–95% of buildings that exist today will still be utilized in 2050 [2]. Hence, changes in energy

**Citation:** Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. *Sustainability* **2022**, *14*, 5857. https://doi.org/ 10.3390/su14105857

Academic Editor: Antonio Caggiano

Received: 6 April 2022 Accepted: 10 May 2022 Published: 12 May 2022

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utilization on buildings are inclined to intensely affect current society, including major economic and environmental changes such as climate change and global warming [3,4]. Buildings are becoming substantially more complex and sophisticated. They integrate conventional energy services systems, on-site energy generation systems, and charging systems [5]. For this reason, energy management is becoming fundamental for buildings around the world, and energy forecasting is essential as an initial step to establish an energy management system [6]. The forecasting of building energy utilization supports smart building performance through low energy and control procedures [7].

In recent times, because of their important application in various fields including electric energy consumption in buildings, data-driven models such as machine- and deep learning-based approaches have become exceptionally well known [8] and are being utilized to improve forecast accuracy [9]. In real life, electrical consumption forecasting models should regularly be made online in real-time. An online setting brings extra challenges since there could be an anticipation of changes to the information distribution over the long haul [10]. However, traditional electric energy forecasting models are normally trained once and not re-trained again with new data, thus missing out on the new information that new data can provide [11]. When this situation happens, it can lead to incorrect forecasting [12].

Recognizing change points and incorporating these uncertain change points into electric energy forecasting models is one of the most difficult tasks [13]. The unexpected changes in the data distribution over time, are known as concept drift [14]. Concept drift has been perceived as the root cause of decreased effectiveness in data-driven decision support systems [15]. Based on how the data change, concept drift can be separated into different kinds: sudden, gradual, recurring, and incremental [16]. Sudden drift happens when the data change quickly and without variation. Whenever the data begin changing in class distribution, this is defined as gradual drift. Recurring drifts happen when the data change for a moment and then return sooner or later. Incremental drift occurs when the data continuously change over the long run [17].

To address those different situations in forecasting models, two main strategies have been used: active and passive methods. For active methods, a model is equipped with a change detection strategy and re-trained when a trigger has been flagged. Nonetheless, in passive methods, algorithms are re-trained at regular intervals regardless of whether a change has occurred or not [18]. There has been a very important effort investigating concept drift in regression tasks (see Table 1) that have focused on load forecasting in houses [19,20], energy consumption in smart grids [21], electricity supply and demand [22], total reactive power [23], energy production for a wind farm [24], power generation in a photovoltaic plant [25], and electricity price [26,27]. However, there have not been many works in real cases where concept drift techniques are used to maintain or improve the results of machine learning techniques in smart buildings. Therefore, this paper's objective is to provide a novel analysis of the integration of drift detection methods in decision trees and deep learning algorithms for whole building electricity consumption forecasting in smart buildings.

Given the above, the main contributions of this paper in this field of research could be summarized as follows:



**Table 1.** Summary of literature review, their contributions, and their limitations.
