By collecting data from Greenhouse consumers and weather observations the demo case has derived an offline model structure for load forecasting in greenhouses and established an online setup for load forecasting in greenhouses. In the online setup, the model parameters have been adapted to the actual dynamics.

The demo case has compared/formulated costs when load could be delayed or pushed forward in time. The energy price would influence the distribution profile.

The model was also based predictive control of supply temperatures with the purpose of reducing the heat loss and the pumping costs.