Germany-based Energielenker has developed a “self-learning” power administration system that controls power flows in buildings with PV programs, utilizing AI algorithms to investigate information. from all related parts.
From pv journal Germany
Germany’s Energielenker has launched a brand new power administration system that learns person conduct in buildings to optimize electrical energy era and electrical energy charging schedules. Its Enbas system is designed for residential and business buildings.
Enbas integrates the corporate’s Lobas dynamic charging administration system, which controls the charging of the electrical automobile fleet and manages the usage of electrical energy to keep away from costly masses. It additionally manages the facility wants for warmth pumps, photovoltaic era, and battery storage.
Using AI algorithms, Enbas supplies a complete view of power flows in buildings by analyzing information from all related parts. It predicts consumption, manufacturing, and considers components akin to climate forecasts, photo voltaic radiation, outdoors temperature, and calendar information to schedule operations a number of days upfront. The system additionally helps time-variable electrical energy tariffs.
Enbas interfaces with Modbus TCP, Modbus RTU, OCPP, MQTT, and EEBus, and affords configuration and visualization by a dashboard. All information recording and calculation takes place on website with out cloud storage.
The system is appropriate with merchandise from main producers akin to ABB, ABL, Mennekes, and Schneider. It additionally integrates warmth pumps by Energielenker’s Heat Control module, which permits the storage of extra photo voltaic power as warmth if the units lack customary interfaces akin to “Smart Grid (SG) prepared.”
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