Our client is expected to advance the sustainable energy system, facing high maintenance efforts for a grid that has to cope with a growing amount of distributed energy sources and increasing overall grid complexity. In particular, outage risk caused by environmental factors, such as weather or soil conditions, and higher stress on assets through fluctuating sources pose challenges to the grid. Budget constraints and the desire to postpone infrastructure replacements where feasible characterize the client's general ecosystem challenges while the demand for optimized maintenance operations and a more sustainable grid grows.

By identifying, integrating, enriching and interpreting data, we co-created a decision support system to predict future outages.

• We identified influencing parameters and available data sources
• We integrated and enriched our client's own data with available external data sources, such as weather data
• Working hand-in-hand with our client and leveraging our domain expertise, we created an outage prediction application based on Artificial Intelligence

Learn what made our approach so unique (PDF)

Our data-driven outage prediction model optimized our client's asset management, prioritizing maintenance on assets with highest failure probability. 

The most crucial benefits for our clients are:

1. Prediction accuracy for future failures at named assets >90% 

2. Optimized utilization of maintenance resources due to dynamic inspection schedules 

3. Smart route optimization for workforce crew as spin-off for added value