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Open AccessArticle

Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach

1
Hydrogen Research Institute, Université du Quebec à Trois-Rivieres, Quebec, QC 3351, Canada
2
Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia
3
Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, Colombia
4
Department of Electrical Engineering, University of Jaén, Campus Lagunillas s/n, Edificio A3, 23071 Jaén, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(18), 7769; https://doi.org/10.3390/su12187769
Received: 25 August 2020 / Revised: 14 September 2020 / Accepted: 18 September 2020 / Published: 20 September 2020
(This article belongs to the Special Issue Photovoltaic Power)
Electric 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. View Full-Text
Keywords: seasonality; electric vehicles; charging behavior; machine learning; charging stations seasonality; electric vehicles; charging behavior; machine learning; charging stations
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MDPI and ACS Style

Dominguez-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. https://doi.org/10.3390/su12187769

AMA Style

Dominguez-Jimenez JA, Campillo JE, Montoya OD, Delahoz E, Hernández JC. Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach. Sustainability. 2020; 12(18):7769. https://doi.org/10.3390/su12187769

Chicago/Turabian Style

Dominguez-Jimenez, Juan A.; Campillo, Javier E.; Montoya, Oscar D.; Delahoz, Enrique; Hernández, Jesus C. 2020. "Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach" Sustainability 12, no. 18: 7769. https://doi.org/10.3390/su12187769

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