Predictive control model for variable air volume terminal valve opening based on backpropagation neural network

2020 ◽  
pp. 107485
Author(s):  
Guozeng Feng ◽  
Shuya Lei ◽  
Xinxin Gu ◽  
Yuejiao Guo ◽  
Junyi Wang
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Model ◽  
Backpropagation Neural Network ◽  
Variable Air Volume ◽  
Air Volume ◽  
Valve Opening

Fuzzy Neural Network Based on Genetic Algorithm for Temperature Control of Variable Air Volume Air Conditioning

2014 ◽  
Vol 599-601 ◽  
pp. 952-955
Author(s):  
Jie Jia Li ◽  
Yong Qiang Chen ◽  
Xiao Yan Han
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Air Conditioning ◽  
Fuzzy Neural Network ◽  
Learning Ability ◽  
Variable Air Volume ◽  
Air Volume ◽  
Fuzzy Neural

In this paper, the theory of the fuzzy control and self-learning ability of neural network is combined, joining the genetic algorithm to optimize the fuzzy control rules, so in the light of temperature control system of variable air volume air conditioning puts forward a fuzzy neural network control method based on genetic algorithm,and this paper introduces in detail the structure, algorithm of fuzzy control and neural network. In addition,this paper verifies the superiority of the fuzzy neural network based on genetic algorithm and ordinary fuzzy neural control.

The Modeling and Simulation of VAV System Based on BP Neural Network

2011 ◽  
Vol 374-377 ◽  
pp. 109-112
Author(s):  
Zeng Xi Feng ◽  
Qing Chang Ren ◽  
Jun Qi Yu
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Step Response ◽  
Control Effect ◽  
Air Conditioning System ◽  
Variable Air Volume ◽  
Parameters Tuning ◽  
Air Volume ◽  
Response Method

VAV (variable air volume) system is a kind of all-air air conditioning system that adjusts indoor temperature by adjusting the supply air volume. This paper builds the mathematical model of supply air temperature of VAV using the step response method, and simulates the model with pure time delay according to PID parameters tuning based on BP (back propagation) neural network, and compares the simulation results with that based on traditional PID, which shows the control effect is superior to traditional PID.

Predictive Control of the VAV System Based on Recurrent Wavelet Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 928-933
Author(s):  
Jie Jia Li ◽  
Ben Wang ◽  
Xiao Yan Guo ◽  
Lu Lu Sun
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Method ◽  
Time Lapse ◽  
Wavelet Neural Network ◽  
Control Precision ◽  
Air Volume ◽  
Air Supply ◽  
High Control

An air supply control method of VAV system based on BP neural network is proposed in this paper, which combines with the recurrent wavelet neural network model, predictive control and optimization of parameters. With the proposed method, the air volume of the VAV system can be controlled accurately even if the change of the air is nonlinear and time-lapse. Compared with tradition control method, it has the advantages of rapidly converging, high control precision, strong skills of learning and wide application prospect.

scholarly journals Model predictive control for buildings with active one-pipe hydronic heating

2019 ◽  
Vol 111 ◽  
pp. 04050
Author(s):  
Jiri Dostal ◽  
Tomas Baumelt
Keyword(s):  
Predictive Control ◽  
Model Predictive Controller ◽  
Heating Systems ◽  
Variable Air Volume ◽  
Variable Volume ◽  
Air Volume ◽  
Challenges And Opportunities ◽  
In Series ◽  
Predictive Controller

One-pipe hydronic heating systems in their active (decentralized pumping) form promise great benefits over traditional two-pipe variable volume systems, and even more so over variable air volume systems. The heat exchanger units are connected in series, which presents its challenges and opportunities. This paper presents a model predictive controller capable of harnessing as many benefits as there is in the system when used in a building. A case study on a small office building illustrates the capabilities and validates the concept.

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