Case Study: Project Greenlight

How Awesense delivered a synchronized, cleansed & structured data model 12x faster than industry standards.

Scope Snapshot

To ingest FortisBC’s GIS and time-series data to create an Energy Data Model that the PwC team could easily access to enable to development of algorithms to provide new AI-driven phase and meter-to-transformer connectivity correction algorithms on a specific set of FortisBC feeders.


One substation with six feeders for a total of 10,593 meters.

Awesense Tools Used

The Energy Transition Platform and its subcomponents, including the AI Data Engine, Awesense Energy Data Model APIs & the Awesense True Grid Intelligence (TGI) for asset and grid edge situational awareness.

The Challenge

  1. Low-quality data for meter-to-transformer association and meter-to-phase assignation
  2. Inability to serve and provide cleansed, structured data from a single platform in a collated format for use case development
  3. While seeing the potential of the data, they needed help with developing solutions and use cases through a unified platform using FortisBC GIS and time-series data due to extensive requirements for bringing disparate data sources together.

The Solution & Results

The Awesense data team processed the GIS and time-series data from one substation with six feeders and 10,593 meters through The Energy Transition Platform. The VEE engine algorithmically cleanses, synchronizes, and structures the data resulting in a populated data model.

The Awesense tools provided a structured and organized data model with simple-to-access APIs. The PwC team could initially access a synthetic data set in The Energy Transition Platform Sandbox Environment to learn the EDM APIs within an open data model available through a single access point. The result is highly structured and reliable data for rapid use case development. The PwC data science team could create an algorithm-driven use case to improve the FortisBC meter-to-transformer and meter-to-phase connectivity model. The goal of the PwC algorithm development will be to enhance reliable insight into their customer phase association, which will help FortisBC understand its distribution system more accurately and assist in further system planning and load growth studies.

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