The demand for computing capacity is rising every year and so is the energy consumption of data centers.
A big part of this energy is transformed into heat, which can be reused to heat apartments and offices.
To make this as efficient as possible and to provide the necessary amount of heat, the demands for heating and computing need to be forecasted and synchronized.
1. A self-learning forecast algorithm predicts the heat demand for locations that are connected in the system to exchange heat with data centers.
2. An optimization algorithm derives a schedule for the computing job execution that adapts location- and timeslots according to the predicted demands and other parameters, like job priority or energy efficiency of the data center.
3. The load shifter executes computing jobs according to the derived schedule, maximizing the reuse of the data center's heat.
... and many more
