THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine

ABSTRACT
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.
KEYWORDS
PAPER SUBMITTED: 2020-05-29
PAPER REVISED: 2020-07-29
PAPER ACCEPTED: 2020-08-21
PUBLISHED ONLINE: 2020-10-31
DOI REFERENCE: https://doi.org/10.2298/TSCI200529307W
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