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From turbine sensors to client portals: we made EDF's wind data work harder.

EDF Energy develops and manages wind farms for major corporate clients, including Fortune 500 companies, and keeps them informed about how their green energy investments perform.

Wildstream applied time-series modeling to detect early turbine failure patterns and built a self-service client portal that pulls real-time data from turbines and backend systems.

EDF Energy
  • AI Engineering
  • Software Engineering

Fortune 500 trust

The portal strengthened EDF’s reputation as a trusted renewable energy partner for its Fortune 500 corporate clients

Repairs before failure

Maintenance teams schedule repairs before mechanical failures occur, rather than reacting after a turbine has stopped

Real-time visibility

Corporate clients follow energy production, performance and site health whenever they want, instead of waiting for a monthly report

AI Engineering

Predictive Maintenance

Unexpected turbine failures cause costly downtime and lost energy production. Without warning, a failure can keep a turbine out of service until parts and crews are available, and every hour offline is energy that is not produced.

Repairs before failure

Maintenance teams schedule repairs before mechanical failures occur, rather than reacting after a turbine has stopped.

Early failure patterns

Time-series models follow how sensor readings change over time to find the early patterns that come before a failure.

Less downtime

Fewer unexpected turbine failures mean less costly downtime and steadier, more predictable energy production across every site.

What we did

Time-series modeling and anomaly detection on sensor data identify early failure patterns, so maintenance can be scheduled before breakdowns occur.

Sponsored by EDF Energy's product management, Wildstream applied time-series modeling and anomaly detection to turbine sensor data. The models detect early failure patterns, so maintenance teams can plan repairs at a convenient time, with the right parts and crew ready.

How it works
  1. Sensors on each turbine record operating data
  2. Time-series models learn normal turbine behavior
  3. Anomaly detection flags readings that drift from normal
  4. Early failure patterns are reported to maintenance
  5. Repairs are scheduled before a breakdown occurs
Key capabilities

Sensor data analysis

Turbine sensor data is analyzed continuously to give maintenance teams an up-to-date view of each machine's condition.

Time-series modeling

Models follow sensor readings over time, so slow changes that point to wear become visible long before a failure.

Anomaly alerts

Readings that move away from normal behavior are flagged and passed on to maintenance planners as early warnings.

Technology
  • Time-series modeling
  • Anomaly detection
Impact

Turbine sensor data that reveals early signs of failure, so repairs happen before breakdowns

Production protected

Avoiding unexpected failures limits the energy production losses that come with a turbine standing still for repairs.

Planned, not urgent, work

Maintenance becomes planned work instead of emergency call-outs, making crew schedules and spare-part stock easier to manage.

Longer turbine life

Fixing wear early helps prevent small faults from turning into major damage, extending the working life of each turbine.

Bring us a challenge like this

Tell us where your operations still depend on manual work, and we will tell you what is worth building first.