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Focusing on the phenomenon that many office buildings and student apartments in northern China with vertical single tube series heating systems are of high energy consumption and waste lots of thermal energy because of lacking heating regulation, this paper proposed a system of on-off regulation to optimize the regulation, where on-off values are installed in each vertical riser, indoor temperature controllers are installed in the room with lower temperature, and the on-off values control circulating water automatically according to the difference between the real temperature and the set temperature and finally the indoor temperatures are controlled. The temperature control effect and energy saving effect of this system were discussed in both simulation and measurement. The conclusion shows that: 1) the proposed system can optimize heating regulation and reduce the loss cause of heating non-uniformity.2) the on-off control on the flow of single vertical tube makes temperature of all rooms stringed in the riser meet the requirements.3) the indoor temperature differences in the same riser are controlled in 2 °C.4) in practical, users on-time is only 50% of total time through on-off regulation, the effect of energy saving is significant.

According to the internal mechanism of the formation of heat load, the formation of heat load consists of two parts, the systemic heat load, which is determined by the building envelope and outdoor environmental parameters and random load caused by the users randomness of events and solar radiation etc. Toward systemic heat load, this paper considered the influence of environmental parameters before the prediction time and used the method of stepwise trials and MSE to obtain the optimal solution. Toward random load, it is considered that the day of the same type have the same variation pattern. On this basis, this paper introduced a correction coefficient to obtain random load eventually. This paper selected DeST, the widely used energy simulation software in China, to analysis the case. The result shows that the prediction method is feasible and 50% of the predicted loads have the relative error of less than 5%.