Optimal Control Of HVAC Systems Based On Predictive Control

The "bp World Energy Statistical Yearbook" released in June 2020 pointed out that China accounted for more than three-quarters of the net increase in global energy consumption in 2019,and energy saving and consumption reduction are urgent.Building energy consumption exceeds one-third of the total energy consumption of society,and building energy conservation has attracted more and more attention.This article focuses on the control and energy saving of HVAC systems,a major energy consumer in buildings.The main contents include building energy consumption modeling,energy consumption analysis,thermal comfort control,HVAC model predictive control and energy saving,etc.This article aims to explore a practical and easy-to-implement control and energy saving idea of a HVAC system that combines theoretical analysis,control,and optimization.The main work is as follows:Firstly,the research status of the significance of building energy conservation,building energy modeling and analysis,HVAC system control methods,predictive control methods,etc.are elaborated,and it is pointed out that building energy conservation requires a combination of energy consumption simulation,theoretical analysis,advanced control and optimization.;Afterwards,the main content of each chapter of the thesis is briefly explained.Secondly,taking a typical public building space as the research object,relying on the Energy Plus environment,three models of building energy consumption are established: free floating system,ideal air system,and detailed air system;on this basis,models with different HVAC systems are established.Detailed analysis is provided to provide data support for subsequent HVAC system control and optimization.Thirdly,a rule-based HVAC system thermal comfort control method is proposed.Through the design and refinement of thermal comfort and control granularity,the comfort control effect is adjusted.Finally,the effectiveness of the method is verified by simulation.Comfort and energy saving are the two major goals of HVAC system control.Rule-based comfort control provides a reference for subsequent HVAC system control and optimization.Fourth,based on the building space thermal environment data of the model established in the previous two chapters,the state space model of the HVAC system is established by the system identification method.On this basis,the model predictive controller is designed and the model predictive control method of the HVAC system is proposed..The simulation results show that this method comprehensively considers the two factors of HVAC system energy consumption and comfort,and obtains a more optimized control effect.Finally,aiming at the problem of state-space model fitting accuracy,this paper adopts neural network model for approximation and realizes the optimization of HVAC system model predictive control.Through the joint simulation software environment BCVTB,a MATLAB+Energy Plus HVAC system predictive control joint simulation experiment model was constructed,and comparative simulation experiments were carried out.The simulation results show that the HVAC system model predictive control method proposed in this paper has good tracking performance and optimization effects.