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Seasonality effect analysis and recognition of charging behaviors of electric vehicles: A data science approach

dc.contributor.authorDominguez-Jiménez, Juan A.
dc.contributor.authorCampillo, Javier E.
dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorDelahoz, Enrique
dc.contributor.authorHernández, Jesus C.
dc.date.accessioned2020-11-04T21:49:36Z
dc.date.available2020-11-04T21:49:36Z
dc.date.issued2020-09-20
dc.date.submitted2020-11-04
dc.identifier.citationDominguez-Jimenez, J.A.; Campillo, J.E.; Montoya, O.D.; Delahoz, E.; Hernández, J.C. Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach. Sustainability 2020, 12, 7769.spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9552
dc.description.abstractElectric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehicles’ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons.spa
dc.format.extent18 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceSustainability 2020, 12(18), 7769spa
dc.titleSeasonality effect analysis and recognition of charging behaviors of electric vehicles: A data science approachspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.identifier.urlhttps://www.mdpi.com/2071-1050/12/18/7769
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.doi10.3390/su12187769
dc.subject.keywordsSeasonalityspa
dc.subject.keywordsElectric vehiclesspa
dc.subject.keywordsCharging behaviorspa
dc.subject.keywordsMachine learningspa
dc.subject.keywordsCharging stationsspa
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.type.spahttp://purl.org/coar/resource_type/c_6501spa
dc.audiencePúblico generalspa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa


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