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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Article
Analysis of the Integration of Drift Detection Methods in
Learning Algorithms for Electrical Consumption Forecasting in
Smart Buildings
Deyslen Mariano-Hernández
1,2,
* , Luis Hernández-Callejo
2,
* , Martín Solís
3
, Angel Zorita-Lamadrid
4
,
Oscar Duque-Pérez
4
, Luis Gonzalez-Morales
5
, Felix Santos García
6
, Alvaro Jaramillo-Duque
7
,
Adalberto Ospino-Castro
8
, Victor Alonso-Gómez
9
and Hugo J. Bello
10
1
Área de Ingeniería, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic
2
ADIRE-ITAP, Departamento Ingeniería Agrícola y Forestal, Universidad de Valladolid, 42004 Soria, Spain
3
Tecnológico de Costa Rica, Cartago 30101, Costa Rica; marsolis@itcr.ac.cr
4
ADIRE-ITAP, Departamento de Ingeniería Eléctrica, Universidad de Valladolid, 47002 Valladolid, Spain;
zorita@eii.uva.es (A.Z.-L.); oscar.duque@eii.uva.es (O.D.-P.)
5
Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones–DEET, Facultad de Ingeniería,
Universidad de Cuenca, Cuenca 010107, Ecuador; luis.gonzalez@ucuenca.edu.ec
6
Área de Ciencias Básicas y Ambientales, Instituto Tecnológico de Santo Domingo,
Santo Domingo 10602, Dominican Republic; felix.santos@intec.edu.do
7
GIMEL, Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Medellín 050010, Colombia;
alvaro.jaramillod@udea.edu.co
8
Facultad de Ingeniería, Universidad de la Costa, Barranquilla 080002, Colombia; aospino8@cuc.edu.co
9
Departamento de Física, Universidad de Valladolid, 47011 Valladolid, Spain; victor.alonso.gomez@uva.es
10
Departamento de Matemática Aplicada, Universidad de Valladolid, 47002 Valladolid, Spain;
hugojose.bello@uva.es
* Correspondence: deyslen.mariano@intec.edu.do (D.M.-H.); luis.hernandez.callejo@uva.es (L.H.-C.);
Tel.: +1-809-949-1227 (D.M.-H.); +34-975-129-418 (L.H.-C.)
Abstract:
Buildings are currently among the largest consumers of electrical energy with considerable
increases in CO
2
emissions in recent years. Although there have been notable advances in energy
efficiency, buildings still have great untapped savings potential. Within demand-side management,
some tools have helped improve electricity consumption, such as energy forecast models. However,
because most forecasting models are not focused on updating based on the changing nature of
buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned,
the objective of this article is to analyze the integration of methods that can help forecasting models
to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be
used as tools to enhance savings in buildings. For this study, active and passive change detection
methods were considered to be integrators in the decision tree and deep learning models. The results
show that constant retraining for the decision tree models, integrating change detection methods,
helped them to better adapt to changes in the whole building’s electrical consumption. However, for
deep learning models, this was not the case, as constant retraining with small volumes of data only
worsened their performance. These results may lead to the option of using tree decision models in
buildings where electricity consumption is constantly changing.
Keywords:
drift detection; electrical consumption forecasting; energy forecasting; machine learning;
smart buildings
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
Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857 https://www.mdpi.com/journal/sustainability
