LAUSR

The realization of refined management in the heating station can not only meet the comfortable indoor, but also improve the energy efficiency, reduce the heating consumption, and alleviate air pollution. Previous studies ignored the indoor temperature and building thermal inertia (BC), as a result, the prediction models of secondary supply temperature have poor energy saving and thermal comfort. This paper adopts the support vector machine to compare and analyze the influence when adding BC and indoor temperature as input parameters. The results show that with temperature automatic monitor indoor, and when Tout, Tin, Th are taken as input parameters, the maximum error between the actual and predicted is 3% with BC, and 4% without. When there is no temperature monitor indoor and only Tout, Th are taken as input parameters, the BC can be calculated by manual indoor temperature measurement, and the maximum error between the actual and predicted is 3.5% when considering BC, and 4.75% without. To validate the universality of this proposed model, four models are applied to the heat stations in different cities, their performance all show that the BC and indoor temperature has a great impact on the accuracy of predict models, and BC has the greater.