Authors:
Pietro Faes Belgrado
1
;
Luboš Buzna
2
;
Federica Foiadelli
3
and
Michela Longo
3
Affiliations:
1
University of Zilina and Politecnico di Milano, Slovak Republic
;
2
University of Zilina, Slovak Republic
;
3
Politecnico di Milano, Italy
Keyword(s):
Electric Mobility, Predictability, Energy Consumption, the Netherlands, Statistical Learning.
Abstract:
The overall purpose of our study has been to evaluate the predictability of future energy consumption analysing the electric mobility in the Netherlands. The climate and energy framework, the European energy production and main developments, as well as the European targets and policy objectives to reduce the current CO2 emissions were first assessed. Then, a deeper look was taken at electric mobility and at Electric Vehicles (EVs). The adoption and development of EVs in the European Union and charging infrastructure were taken into account. The Dutch energy production and emissions, as well as, the mobility in the country and its infrastructure were investigated. Previous studies about electric vehicles and charging points have addressed the predictability of future energy consumption in larger areas to only very limited extent, so our research work has concentrated on this gap. A large real-world dataset was used as a basis to create statistical models, in order to study the users’
behaviour within the charging points infrastructure and to evaluate the predictability of future energy consumption of the charging points in selected regions of the Netherlands. Results vary across different regions with the number of charging points, but suggest that statistical models could be useful in the management of energy production to optimize the dispatch of energy sources.
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