Challenge / Goal
Dundee has the second largest fleet of electric vehicles (EV) in the UK and this is rapidly expanding, as is the number of EV charging points available to the public driving within and through the city.
As EV technology is more widely adopted, we need more insight into how users charge their vehicles and consume energy. This was identified as an area where data analysis could provide insight critical to ensuring the service is fit for purpose, efficient, and fully meets the needs of electric vehicle users.
Dundee recognises that Open Data is an important resource for data analysts and policy makers and should be fully utilised as a tool in service design.
Solution
DCC sought to gain a better insight into how EV charge points are being used and to identify any patterns in their usage - i.e. busiest charge points, busiest times of day/days of the week, are any charge points underutilized? Data analysis identified the following:
- Which chargepoint sites are most popular?
- How much energy has been delivered to vehicles?
- What times of day are most popular for charging your car?
We analysed electric vehicle charge point energy consumption, published quarterly in CSV format. The analysis was carried out in Jupyter Notebooks using Pandas – a python data analysis library. The data was accessed via the CKAN datastore API.
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Log inTime period
Planning time
6 months to 1 year
Implementation time
6 months to 1 year
Implementers
Dundee City Council (DCC)
Service providers
Dundee City Council (DCC)
End users
Dundee City Council (DCC)