Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings

Deyslen Mariano-Hernández, Luis Hernández-Callejo, Martín Solís, Angel Zorita-Lamadrid, Oscar Duque-Pérez, Luis Gonzalez-Morales, Felix Santos García, Alvaro Jaramillo-Duque, Adalberto Ospino-Castro, Victor Alonso-Gómez and Hugo J. Bello
Additional contact information
Deyslen Mariano-Hernández: Área de Ingeniería, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic
Luis Hernández-Callejo: ADIRE-ITAP, Departamento Ingeniería Agrícola y Forestal, Universidad de Valladolid, 42004 Soria, Spain
Martín Solís: Tecnológico de Costa Rica, Cartago 30101, Costa Rica
Angel Zorita-Lamadrid: ADIRE-ITAP, Departamento de Ingeniería Eléctrica, Universidad de Valladolid, 47002 Valladolid, Spain
Oscar Duque-Pérez: ADIRE-ITAP, Departamento de Ingeniería Eléctrica, Universidad de Valladolid, 47002 Valladolid, Spain
Luis Gonzalez-Morales: Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones–DEET, Facultad de Ingeniería, Universidad de Cuenca, Cuenca 010107, Ecuador
Felix Santos García: Área de Ciencias Básicas y Ambientales, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic
Alvaro Jaramillo-Duque: GIMEL, Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Medellín 050010, Colombia
Adalberto Ospino-Castro: Facultad de Ingeniería, Universidad de la Costa, Barranquilla 080002, Colombia
Victor Alonso-Gómez: Departamento de Física, Universidad de Valladolid, 47011 Valladolid, Spain
Hugo J. Bello: Departamento de Matemática Aplicada, Universidad de Valladolid, 47002 Valladolid, Spain

Sustainability, 2022, vol. 14, issue 10, 1-14

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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