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

dc.creatorMariano-Hernández, Deyslen
dc.creatorHernández Callejo, Luis
dc.creatorSolís, Martín
dc.creatorZorita Lamadrid, Angel Luis
dc.creatorDuque-Perez, Oscar
dc.creatorGonzalez Morales, Luis Gerardo
dc.creatorSantos Garcia, Felix
dc.creatorJaramillo Duque, Álvaro
dc.creatorOspino C., Adalberto
dc.creatorAlonso Gómez, Víctor
dc.creatorBello, Hugo J.
dc.date2022-08-24T14:53:53Z
dc.date2022-08-24T14:53:53Z
dc.date2022-05-12
dc.date.accessioned2023-10-03T19:21:39Z
dc.date.available2023-10-03T19:21:39Z
dc.identifierMariano-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
dc.identifierhttps://hdl.handle.net/11323/9472
dc.identifierhttps://doi.org/10.3390/su14105857
dc.identifier10.3390/su14105857
dc.identifier2071-1050
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9169746
dc.descriptionBuildings 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 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.
dc.format14 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI AG
dc.publisherSwitzerland
dc.relationSustainability
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dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.mdpi.com/2071-1050/14/10/5857
dc.subjectDrift detection
dc.subjectElectrical consumption forecasting
dc.subjectEnergy forecasting
dc.subjectMachine learning
dc.subjectSmart buildings
dc.titleAnalysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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