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dc.contributor.authorČurčić, Teo
dc.contributor.authorRobin Kalloe, Rajeev
dc.contributor.authorKreszner, Merel Alexandra
dc.contributor.authorVan Luijk, Olivier
dc.contributor.authorPuertas Puchol, Santiago
dc.contributor.authorCaba Batuecas, Emilio
dc.contributor.authorBaldiri Salcedo Rahola, Tadeo
dc.date.accessioned2023-02-13T12:10:23Z
dc.date.available2023-02-13T12:10:23Z
dc.date.issued2022
dc.identifier.issn1848-9257spa
dc.identifier.urihttps://hdl.handle.net/10641/3255
dc.description.abstractMachine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.spa
dc.language.isoengspa
dc.publisherJournal of Sustainable Development of Energy, Water and Environment Systemsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectSmart meterspa
dc.subjectNet-Zero Energy Buildingspa
dc.subjectSupervised machine learningspa
dc.titleGaining Insights into Dwelling Characteristics Using Machine Learning for Policy Making on Nearly Zero-Energy Buildings with the Use of Smart Meter and Weather Data.spa
dc.typearticlespa
dc.description.versionpost-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent1304 KBspa
dc.identifier.doi10.13044/j.sdewes.d9.0388spa
dc.relation.publisherversionhttps://www.sdewes.org/jsdewes/pid9.0388spa


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España