AI Based Predictive Analytics

The VoltiGo system has been enhanced with a unique event prediction capability powered by an AI‑based model. This functionality enables early detection of irregularities and potential risks affecting both individual devices and the entire system. By collecting large volumes of operational data, analysing them, and evaluating current performance trends, the algorithm identifies subtle changes that may indicate upcoming technical issues. This proactive approach allows operators to take corrective actions before anomalies begin to impact the reliability of the energy storage system and the supporting infrastructure.

Transitioning from reactive to predictive maintenance reduces the risk of unplanned downtime and increases both operational continuity and cost efficiency.

In traditional alerting systems, events are detected only when a defined threshold is exceeded or when a parameter (such as temperature, current, or voltage) moves outside its acceptable range. This means that the system reacts only after a problem has already occurred.

The AI model works differently: it analyses equipment behaviour patterns, compares them with historical data and operational characteristics, and identifies unusual trends that do not yet trigger alarms but already indicate that a device may be starting to operate abnormally.

Thanks to this capability, the system can predict a failure before it becomes noticeable to the operator or a conventional EMS, enabling:

• minimization of the risk of unexpected downtime,
• lower service costs through optimal scheduling of maintenance activities,
• extended equipment lifespan by detecting early signs of component degradation,
• verification of the correct operation of factory‑installed software.