current factsand backgrounds

our aiI-based system WICKIE M for optimal energy efficiency in building automation is constantly being expanded. a recent master's thesis aimed at developing a new methodology: the innovative approach of this work lies in the development of several algorithms and models in addition to time series forecasting and the interaction of these models for energy-efficient control of actuators in the heating circuit.

our own ai models have been developed for the required room temperature, heating flow and spread. with these models, the optimal control of the heat generator can be determined very precisely. the artificial neural network calculates when the heating valve is opened or closed for optimal energy consumption at an optimal temperature. based on predicted flow, spread and flow temperature, the required level of filling of the buffer tank is determined. the control of heat generator determined.

the great added value continues to lie in efficient, energy-saving and resource-saving heating control, in the secondary circuit (heat collector) and in the primary circuit (heat generator). the synergies from both regulations exploit the greatest potential. the aim of the master's thesis, to develop suitable machine learning algorithms for controlling the primary control circuit and the heating valve, has been fulfilled. the interaction of the different algorithms leads to even more efficient control. shows that the use of WICKIE M for can lead to a significant reduction in heat demand, a more targeted distribution and regulation of energy and therefore to significant savings in resources even with this new methodology.

after successful completion of this research and development phase, the concrete technical implementation in the product now follows.