![]() Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R 2 was about 0.9 in three buildings. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. ![]()
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