Code: EDI-2019-22-VPS

Domain: Energy & Environment


To develop a benchmark tool that Energy Retailers will use to improve customer engagement and satisfaction.

Proposed by


VPS is an innovative, market leader in the design and operation of dynamic connected platforms, providing real-time granular data to consumers, network operators and utilities.
We minimise consumption by increasing energy efficiency, optimising the time of use and monetising loads.
Our team of experts has a strong and proven track record in providing significant benefits to all stakeholders in the modern energy network. We aim to be the largest European Virtual Power Plants’ builder and operator.


The smart meter rollout brought new challenges and opportunities for the Energy Retailers and Energy Services Companies. We have now the chance to know what happens on any point of the grid with unprecedented detail, but what will we do with this data on an increasingly complex and competitive market?

The liberalisation of energy markets made possible the concurrence between electricity retailers that struggle for the loyalty of their clients. On the other hand, rising energy tariffs and regulations haste a conscious consumption: the ever-growing investments in energy conservation measures and in distributed generation reflects this reality.

Our challenge is in tune with the above scenario, to develop a benchmark tool that Energy Retailers will use to improve customer engagement and satisfaction, that also could be used by ESCO, to easily find new opportunities and quickly and accurately estimate their savings potential.



The challenge has the following sample datasets available for download

Expected outcomes

  • Clustering – A data model that determines meaningful metrics for cross-sectional benchmark energy performance in business consumers, especially commercial buildings.
  • Adaptability – The same model, having the right metadata as input, will perform well independently of the business vertical; for example, we can use the same model to benchmark schools, hospitals or bank branches.
  • Multi-utility – Although focused on electricity, the solution will also be able to benchmark gas and water consumption.
  • Evolutionary – The model will be open and malleable; it will be auto-learning with the data that is feeding in constantly.
  • Scalability – The solution must be scalable.

How do we apply?

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