Eneryield apply machine learning for the benefit of the energy sector, implying various of new applications. For example, Eneryield's can perform a so-called root cause analysis of power quality disturbances, which is beneficial in itself, but also lays the foundation of more advanced analytics, such as proactive maintenance, more efficient planning of grids and selective coordination.

Also, Eneryield is working with machine learning driven control of power electronics (drives, active power filters, FACTS, inverters). Eneryield’s solution builds upon novel machine learning algorithms which enables modelling the behaviour of the system, resulting in a significantly reduced response time and increase in precision. This is a groundbreaking steps towards a new generation of predictive power electronics, with an improved accuracy, for power- grids and systems....
Eneryield apply machine learning for the benefit of the energy sector, implying various of new applications. For example, Eneryield's can perform a so-called root cause analysis of power quality disturbances, which is beneficial in itself, but also lays the foundation of more advanced analytics, such as proactive maintenance, more efficient planning of grids and selective coordination.

Also, Eneryield is working with machine learning driven control of power electronics (drives, active power filters, FACTS, inverters). Eneryield’s solution builds upon novel machine learning algorithms which enables modelling the behaviour of the system, resulting in a significantly reduced response time and increase in precision. This is a groundbreaking steps towards a new generation of predictive power electronics, with an improved accuracy, for power- grids and systems.
More information

Employees

Johan Rådemar
Admin
Johan Rådemar Co-founder of Eneryield