Reinforcement Learning Control of Static Pressure in Variable Air Volume Air-conditioning System with Demand Response
Demand response(DR)strategies have been proposed to solve problems such as power imbalance in the grid.Energy saving in heating,ventilation and air-conditioning(HVAC)systems is an important part of building energy efficiency.The operation of variable air volume(VAV)air-conditioning systems is a complex dynamic process,and reinforcement learning(RL)algorithms have great potential for dynamic control.This study focused on optimizing the static pressure control of VAV air-conditioning systems based on RL,combined with DR strategies.Based on the above background,a DR-RL-based static pressure optimization control strategy for VAV air-conditioning systems was proposed.A joint TRNSYS+Python simulation platform was established for RL model training,and the DR-RL-based static pressure optimization control strategy was tested on a physical experimental platform.Compared with the traditional static pressure control strategy,the proposed strategy effectively achieves fan load reduction during DR and all-day operation cost saving under the premise of ensuring basic thermal comfort.
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维普
