TY - JOUR
TI - Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine
AU - Wang Tao
AU - Ma Tingyu
AU - Yan Dongsong
AU - Song Jing
AU - Hu Jianshuo
AU - Zhang Guiyong
AU - Zhuang Yuhan
JN - Thermal Science
PY - 2021
VL - 25
IS - 5
SP - 3219
EP - 3228
PT - Article
AB - 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.