Germany-based Energielenker has developed a ‘self-learning’ energy management system that controls energy flows in buildings with PV systems, using AI algorithms to analyze data from all relevant components.
Germany’s Energielenker has launched a new energy management system that learns user behavior in buildings to optimize electricity generation and charging schedules for electric vehicles. The Enbas system is designed for both residential and commercial buildings.
Enbas integrates the company’s Lobas dynamic charging management system, which controls the charging of the electric fleet and manages energy consumption to avoid costly peak loads. It also manages electricity needs for heat pumps, photovoltaic generation and battery storage.
Using AI algorithms, Enbas provides a comprehensive overview of energy flows in buildings by analyzing data from all relevant components. It predicts consumption, production and takes into account factors such as weather forecasts, solar radiation, outside temperature and calendar data to plan work several days in advance. The system also supports time-varying electricity rates.
Enbas communicates with Modbus TCP, Modbus RTU, OCPP, MQTT and EEBus and offers configuration and visualization via a dashboard. All data recording and calculations take place on-site without cloud storage.
The system is compatible with products from major manufacturers such as ABB, ABL, Mennekes and Schneider. It also integrates heat pumps via Energielenker’s Heat Control module, allowing excess solar energy to be stored as heat when devices do not have standard interfaces, such as ‘Smart Grid (SG) ready’.
This content is copyrighted and may not be reused. If you would like to collaborate with us and reuse some of our content, please contact: editors@pv-magazine.com.
