基于NN-PID算法的变风量空调系统空气品质控制

目的针对典型会议室环境,基于需求控制通风策略,对变风量中央空调系统房间空气品质控制进行研究。方法以典型的会议室环境为研究对象,分别建立空调新风系统模型及房间CO2浓度模型;设计NN-PID(神经网络-PID)算法,并进行控制与仿真;在变风量空调实验平台上进行验证。结果所设计的NN-PID算法能有效利用神经网络训练过程,在线自整定PID参数,控制效果优于传统PID算法。结论根据室内CO2浓度变化控制新风量,能很好地适应室内CO2浓度的动态特性,提高室内空气品质。

The indoor-air quality control in VAV air conditioning room based on NN-PID algorithm

Aim The study mainly focuses on the indoor air quality control in VAV air-conditioning system,based on the demand control ventilation(DCV) strategy with the harmful gases detection.Methods Aiming at a typical assembly room environment for the study,models for fresh air control system and indoor CO2 concentration control system were constructed respectively.A dynamic NN-PID(Neural Network-PID) controller was developed to conduct controlling and simulation and then tested on the VAV air-conditioning platform.Results Simulation results show that the combined NN-PID algorithm is more effective than the traditional PID control algorithm and can self-tune the PID control parameter on line by neural network training process.Conclusion The combined control model is more effective than the common PID controller and it has better performance to the indoor CO2 concentration changing by controlling the fresh air reasonably,and consequently improves the indoor air quality on the basis of energy-saving.