Research On Prediction And Control Of Office Environment Quality Based On Bayesian Network

Indoor air quality and indoor thermal comfort are especially important for indoor environment quality.However,Indoor air quality regulation and ventilation regulation are conflicting in the office building environment with split air conditioning and independent ventilation.The increase of ventilation can reduce indoor thermal comfort and increase the energy consumption of air conditioning.Therefore,how to ensure that under the premise of low energy consumption,it can not only satisfy the indoor thermal comfort in the office,but also ensure that the good air quality is the main research content of the quality control and optimization of the office building indoor environment.Firstly,the paper takes an office building in Xi’an as the research object and uses Bayesian network modeling method.On the basis of analyzing the influence mechanism of air conditioning,ventilation equipment,indoor personnel and related disturbance on indoor environment quality,the structure model of Bayesian network is established.By training and learning from the measured data,a conditional probability table for the interaction of various factors is obtained,and a mathematical model for the quality control of the office indoor environment based on Bias is established.The model verification results show that,under the same indoor and outdoor environment conditions,the calculated output parameters of Bayesian network model are basically consistent with the actual environmental quality parameters collected by the experiment,and the correctness and effectiveness of the model are verified.Secondly,based on the model,the model predictive control method is used to construct the objective function of the prediction control and optimization of indoor environment quality based on Bayesian network,and the prediction time range is determined.The indoor environment quality control and optimization simulationsystem is established by using the simulation simulation experiment environment combined with instantaneous system simulation program TRNSYS and MATLAB.The model prediction control and Optimization Based on Bayesian network and the traditional PID control simulation experiment are completed.Finally,the experimental results show that the predictive control and optimization of indoor environment quality based on Bayesian network can better track the set value of thermal comfort and air quality compared with the traditional PID control method.The overshoot and steady state error are small.At the same time,compared with the traditional control mode,the energy consumption of control optimization based on Bayesian network is greatly reduced.It is proved that the indoor environment quality control and optimization method based on Bayesian network can ensure good indoor comfort and reduce building energy consumption.