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7118430 
Journal Article 
Electric vehicle load forecasting: a comparison between time series and machine learning approaches 
Buzna, L; De Falco, P; Khormali, S; Proto, D; Straka, M; , 
2019 
IEEE 
NEW YORK 
WOS:000490562000009 
Transport systems are expected to widely shift towards electric propulsion in the next decade. The diffusion of Electrical Vehicles (EVs) however creates great challenges; among the others, EV charging patterns are non-controllable, thus it is mandatory to have at disposal high quality EV load forecasts in order to reach the operational excellence of networks with a wide EV penetration. Relevant literature on EV load forecasting is quite scarce, compared to other load forecasting applications; this paper aims at filling this gap by providing a comparative study between the performances of time series and machine learning approaches. The comparative analysis is performed on actual EV load data, extracted from a dataset collected at 1700 charging stations in the Netherlands. The results of numerical experiments are given in terms of aggregate energy consumption for lead times up to 28 days ahead, in order to fully suit the time horizons typical of distribution systems management. 
Electric vehicles; gradient boosting; load forecasting; random forests; time series analysis 
1st Conference on Energy Transition in the Mediterranean Area (SyNERGY MED) 
Cagliari, ITALY