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Charging Station Allocation

Identyfing location factors before expanding to new places

Challenge
  • One of the leading Electric Vehicle rapid charging networks

  • Charger utilization is the most important business KPI

  • Implementing a predictive model to optimally locate chargers was a business priority

  • Traditional consultancies proved expensive, lacked innovation and featured exorbitant on-going maintenance costs

  • Designed a scalable data platform with continuous integration capabilities for new data sources

  • Combined company utilisation data with 500+ data features from open source and private purchased sources (everything from climate to road traffic and car ownership to amenities and socio-demographic factors)

  • Built a comprehensive predictive ML model that successfully explained a large percentage of the target variables

  • Visualised results within existing PowerBI instance to minimize costs and maximize usability

Solution
Value
  • Key business stakeholders have a high degree of confidence in model outputs

  • Model directly informs whether sites are approved for build or not

  • New data sources can be easily added and automatically fed into the model, thanks to the flexible and thoughtful design of the solution

  • 4x cheaper than alternative solution and a better outcome achieved

Roles

Cloud Engineer, Spatial Data Scientist, Machine Learning Engineer, Project Manager

Tools

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Sector

Automotive, Energy & EVs

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