Research on the framework and meteorological parameter optimization method of dynamic heating load prediction model for heat-exchange stations

Heat-exchange station is the key mid-link between heat source and heat users in district heating system, playing a role in distributing heat and regulating supply and demand. Accurate load prediction at the level of heat-exchange stations and then regulating the heat supply is the important step to optimize district heating system. Based on real and limited data, the framework of heat-exchange station load prediction method is established in this study, which includes 5 procedures ‘data preprocessing method, input variable selection, solar radiation substitution, prediction model establishment and model transplantation application’. The sliding box plot method was proposed for data preprocessing, and the effects of data preprocessing under the widths of 24 h, 48 h, 72 h, 96 h and 120 h were compared, with 48 h as the optimal sliding window width. Support Vector Regression (SVR) algorithm was used to establish a heating load prediction model for heat-exchange station in residential buildings. And parameter substitution was made for solar radiation data that cannot be obtained in weather forecasts. High accuracy was obtained with a MAPE of 5.80 % and RMSE of 0.18 GJ. The above model was transplanted and applied in three other heat-exchange stations of different building types. The MAPEs were 5.51 %, 7.79 % and 6.54 %, with corresponding RMSEs of 0.017 GJ, 0.04 GJ and 0.04 GJ, respectively, which all achieved good prediction results. The research results provide certain technical support for the operation and regulation of district heating systems.