Multiple regression model based on weather factors for predicting the heat load of a district heating system in Dalian, China—A case study

The managers need to have a reasonable guide on the operation and management in district heating system (DHS), so it’s very necessary to predicting the heat load for DHS. In this paper, the relationships between the heat load and weather conditions have been researched in order to determine the inputs variables and output variable of the future heat load prediction model. Using the given data from the obtained database, the multiple regression modelling and analysis method was carried out so as to establish the corresponding heat load prediction models for the DHS. The results shown that the square correlation coefficient between the heat load’s measured value and estimated value are all greater than 0.9000, and the mean absolute percentage error (MAPE) between the heat load’s measured value and estimated value are all less than 4.00%. Moreover, the corresponding maximum absoult relative errors between the heat load’s measured value and estimated value are all less than 8%. The results also indicated that the heat load prediction model’s accuracy is relatively high. Furthermore, these 5 heat load prediction models can be applied in the real DHS and this multiple regression method can be promoted into the other engineering field. © Cai et al.