District heating systems are an important part of the future smart energy
system and are seen as a tool to achieve energy efficiency goals in the EU.
In order to achieve the real sense of heating on demand, based on historical
heating load data, first of all, the heating load time series data was
dealing with fuzzy information granulation, and then the cross-validation
was used to explore the advantages of the data potential. Then the support
vector machine regression prediction model was used for the prediction of
the granulation data, finally, the heating load of a district heating system
is simulated and verified. The simulation results show that the prediction
model can effectively predict the trend of heating load, and provide a
theoretical basis for the prediction of district heating load.