Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 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...

Full description

Autores:

Mariano-Hernández, Deyslen
Hernández Callejo, Luis
Solís, Martín
Zorita Lamadrid, Angel Luis
Duque-Perez, Oscar
Gonzalez Morales, Luis Gerardo
Santos Garcia, Felix
Jaramillo Duque, Álvaro
Ospino C., Adalberto
Alonso Gómez, Víctor
Bello, Hugo J.

Tipo de recurso:

Article of journal

Fecha de publicación:

2022

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

eng

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/9472

Acceso en línea:

https://hdl.handle.net/11323/9472
https://doi.org/10.3390/su14105857
https://repositorio.cuc.edu.co/

Palabra clave:

Drift detection
Electrical consumption forecasting
Energy forecasting
Machine learning
Smart buildings

Rights

openAccess

License

Atribución 4.0 Internacional (CC BY 4.0)