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.
From pv magazine Germany
Germany's Energielenker has launched a new energy management system that learns user behavior in buildings to optimize electricity generation and electric vehicle charging schedules. Its Enbas system is designed for both residential and commercial buildings.
Enbas integrates the company's Lobas dynamic load management system, which controls electric vehicle fleet charging and manages power usage 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 forecasts consumption, production, and considers factors like weather forecasts, solar radiation, outside temperature, and calendar data to schedule operations several days in advance. The system also supports time-variable electricity tariffs.
Enbas interfaces with Modbus TCP, Modbus RTU, OCPP, MQTT, and EEBus, and offers configuration and visualization through a dashboard. All data recording and calculation occur 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 through Energielenker's Heat Control module, enabling storage of surplus solar energy as heat when devices lack standard interfaces like “Smart Grid (SG) ready.”