This paper proposes a heat demand prediction model that analyzes the heat behavior of heat users, and analyzes the heat behavior data of heat users through a clustering algorithm to predict their heat demand. This paper is based on the supply-demand balance strategy to reduce the heat loss during the transmission of heat medium, and then improve the energy-saving efficiency of the boiler. The traditional boiler energy-saving and consumption reduction method is to optimize the boiler combustion parameters, improve the fuel combustion efficiency and waste heat recovery technology through the Internet of Things and big data technology. The method of balancing the heat-using end with the load of the boiler at low usage frequency is seldom considered. Therefore, this paper predicts the heat demand of the heat-using end by analyzing its thermal behavior, and balances the heat demand and boiler heat supply under the condition of meeting the heat demand. Finally, through simulation experiments, the validity of the model is verified, and the trend and data can be well predicted in the short-term heat demand prediction.

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