[Client Info]Energy Grid Operator
A utility company managing power distribution, consumption forecasting, and outage response across metropolitan and rural districts.
#1. summary.
The client aimed to improve demand forecasting, outage prediction, and asset maintenance across a distributed energy grid. With weather disruptions increasing and infrastructure aging, they needed real-time foresight into grid stability and load spikes. Xuno implemented predictive models to improve demand accuracy, detect early signs of equipment failure, and automate outage response prioritization.
Technologies used:
#2. challenges.
Legacy forecasting models failed to adjust for weather volatility or usage anomalies, leading to missed demand peaks and outages. Maintenance was reactive, relying on manual inspections. Teams struggled to prioritize outages and dispatch field crews efficiently. Downtime penalties and rising consumer complaints drove urgency to modernize.
#3. solution.
Xuno delivered machine learning models trained on weather, consumption, and sensor data to generate real-time demand forecasts. Predictive maintenance models flagged equipment degradation and pre-failure signals across transformers and substations. Outage routing tools enabled dispatchers to triage faster and deploy field crews based on real-time risk and customer impact.
#4. outcome.
Demand forecasting accuracy rose by 37%, reducing overcompensation spend and power shortages. Field response time during outages improved by 92%, and asset downtime dropped by 44%. The models also prevented five major unplanned outages in the first 90 days. The client extended Xuno’s engagement to expand across additional districts.
