Case study
Grid forecasting
Planning platform for an electricity distribution network, covering substations, feeders and switches across the grid.
Demand 20 years outagainst assets in the ground
- Sector
- Electricity distribution
- Year
- 2023
- Role
- Team of six, in a 15-person programme
- Stack
- Angular, C#, .NET, PostgreSQL
The problem
Planning infrastructure development, maintenance, and resource allocation for a power grid requires seeing years ahead of current demand, and a clear picture of the assets already in the ground. Without that, decisions on where to invest and when to maintain are made reactively rather than ahead of need.
What we built
An internal system combining demand forecasting models over a 20-year horizon with asset management tooling, so infrastructure development, maintenance scheduling, and resource optimisation can be planned proactively rather than after the fact. The system provides visualisations and data analysis giving stakeholders a clear overview of current assets and projected needs.
The long version
The core problem this system addresses is horizon: a power grid has to be planned years, not months, ahead, and the people making those calls need forecasting and current-state asset data in the same place rather than reconciled by hand across separate tools.
The forecasting side models demand over a 20-year window. The asset management side tracks what infrastructure exists now. Together they let planners see where the grid is headed and where it currently stands, and make maintenance scheduling and infrastructure investment decisions against that combined picture instead of guesswork or siloed spreadsheets.
Delivery included visualisation and data analysis layers on top of the underlying models, so the forecasts and asset data are usable by stakeholders directly rather than requiring a data team to interpret them on every request.