000168161 001__ 168161 000168161 005__ 20260130122621.0 000168161 0247_ $$2doi$$a10.1109/VTC2025-Fall65116.2025.11310567 000168161 0248_ $$2sideral$$a147675 000168161 037__ $$aART-2025-147675 000168161 041__ $$aeng 000168161 100__ $$aDonate, Pablo 000168161 245__ $$aPredicting Home EV Charging Practices Using Machine Learning 000168161 260__ $$c2025 000168161 5060_ $$aAccess copy available to the general public$$fUnrestricted 000168161 5203_ $$aThe rapid adoption of electric vehicles (EVs) demands advanced residential charging solutions, where user behavior varies widely due to factors like electricity tariffs and weather conditions. Such conditions make charging predictions particularly complex. This study addresses this challenge by predicting the time an EV remains connected to a residential charger using a dataset of 106,260 sessions. To this end, we propose the Connection Time Neural Model (CTNM), a deep neural network designed to model the complex dynamics of domestic charging, and we introduce the Weighted Error Metric (WEM), a novel metric that penalizes overestimations and under-estimations differently to reflect their real-world impacts on both grid management and user experience. Utilizing bidirectional charger data, we focus solely on connection time, bypassing energy prediction. CTNM is benchmarked against state-of-the-art methods (i.e., Random Forest, Dense Neural Network, XGBoost, and Support Vector Regression) using Mean Absolute Error, Root Mean Squared Error, and WEM as performance metrics. Results demonstrate CTNM’s superiority, reducing average error by 18%, and weighted error by 31% compared to Random Forest, thanks to its deep architecture and integration of contextual features, like variable tariffs and weather. 000168161 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T40-23D$$9info:eu-repo/grantAgreement/ES/MCIU/AEI/PID2021-122580NB-I00 000168161 540__ $$9info:eu-repo/semantics/embargoedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000168161 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000168161 700__ $$0(orcid)0000-0001-7657-0075$$aSanguesa, Julio A.$$uUniversidad de Zaragoza 000168161 700__ $$0(orcid)0000-0002-1750-7225$$aGarrido, Piedad$$uUniversidad de Zaragoza 000168161 700__ $$0(orcid)0000-0002-0787-2667$$aTorres-Sanz, Vicente$$uUniversidad de Zaragoza 000168161 700__ $$0(orcid)0000-0001-6945-7330$$aMartinez, Francisco J.$$uUniversidad de Zaragoza 000168161 700__ $$aCalafate, Carlos T. 000168161 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput. 000168161 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000168161 773__ $$g102 (2025), 11310567 [5 pp.]$$pIEEE Vehic. Technol. Conf. (1993)$$tIEEE Vehicular Technology Conference (1993)$$x1090-3038 000168161 8564_ $$s472046$$uhttps://zaguan.unizar.es/record/168161/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2028-01-06 000168161 8564_ $$s3257147$$uhttps://zaguan.unizar.es/record/168161/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2028-01-06 000168161 909CO $$ooai:zaguan.unizar.es:168161$$particulos$$pdriver 000168161 951__ $$a2026-01-30-12:22:23 000168161 980__ $$aARTICLE