Multiple Regression Model Based on Weather Factors for Predicting TheHeat Load of A District Heating System in Dalian, China—A Case Study

摘要

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 loadand weather conditions have been researched in order to determine the inputs variables and output variable of the futureheat load prediction model. Using the given data from the obtained database, the multiple regression modelling and analysismethod was carried out so as to establish the corresponding heat load prediction models for the DHS. The resultsshown that the square correlation coefficient between the heat load’s measured value and estimated value are all greaterthan 0.9000, and the mean absolute percentage error (MAPE) between the heat load’s measured value and estimated valueare all less than 4.00%. Moreover, the corresponding maximum absoult relative errors between the heat load’s measuredvalue and estimated value are all less than 8%. The results also indicated that the heat load prediction model’s accuracy isrelatively high. Furthermore, these 5 heat load prediction models can be applied in the real DHS and this multiple regressionmethod can be promoted into the other engineering field.