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Intelligent optimization control method based on dynamic simulation platform of central air-conditioning system

A technology of a central air-conditioning system and a control method, applied in the field of intelligent optimization control, achieves the effects of high accuracy, reduced energy consumption, and improved efficiency

Active Publication Date: 2022-03-11
WUHAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the problem of energy consumption of the central air-conditioning system existing in the prior art, and to provide an intelligent optimization control method based on the dynamic simulation platform of the central air-conditioning system that reduces the energy consumption of the central air-conditioning system

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  • Intelligent optimization control method based on dynamic simulation platform of central air-conditioning system

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Embodiment 1

[0226] An intelligent optimization control method based on a dynamic simulation platform of a central air-conditioning system, the control method is based on a central air-conditioning system, and the central air-conditioning system includes: a cooling device, a cooling tower, a condensate pump and a cooling water pump, and the cooling device is connected through a corresponding piping system To the cooling tower, then connected to the condensate pump, and finally back to the cooling device; the cooling water pump generates cold water through the cooling pipe system to the cooling device to the building that needs to be cooled;

[0227] Described intelligent optimal control method comprises the steps:

[0228] S1. Data preprocessing:

[0229] For data collection, first export all operating data texts from the central air-conditioning control system;

[0230] Import the exported operating data text into Matlab, delete or use the mean value interpolation method to fill in the m...

Embodiment 2

[0239] Embodiment 2 is basically the same as Embodiment 1, and its difference is:

[0240] The S1, data preprocessing:

[0241] For data collection, first export all operating data texts from the central air-conditioning control system;

[0242] Import the exported operating data text into Matlab, delete or use the mean value interpolation method to fill in the missing values ​​in the data text, and use the Pearson product-moment correlation coefficient for calculation. The expression of the relationship coefficient r is:

[0243]

[0244] In the above formula, is the mean value of the parameter X, is the mean value of parameter Y;

[0245] Put the data of all category parameters in the text into formula 1 to calculate the absolute value of the correlation between each parameter; the following table takes some relationships as an example for correlation analysis, and the results are shown in table 1 below :

[0246] Table 1 Correlation between parameters

[0247] ...

Embodiment 3

[0328] Embodiment 3 is basically the same as Embodiment 2, and its difference is:

[0329] The intelligent optimization control method also includes S4, genetic algorithm application solution and analysis:

[0330] Use the genetic algorithm to calculate the system model and optimization model of the central air conditioner that has been modeled, and set the initial value group size N p , the crossover probability P c , the mutation probability P m , terminate the evolutionary algebra G, then perform genetic algorithm calculations, and finally obtain the optimal control strategy, compare the calculation results with the original data at several different time points of energy consumption and efficiency, and verify the optimization of the central air-conditioning system model and optimization model Effect.

[0331] To achieve the lowest total power consumption of the central air-conditioning system, the main way is to control the overall energy consumption of several energy-c...

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Abstract

According to the intelligent optimization control method based on the dynamic simulation platform of the central air-conditioning system, the control method is based on the central air-conditioning system, the central air-conditioning system comprises a cooling device, a cooling tower, a condensate pump and a cooling water pump, and the cooling device is connected to the cooling tower through a corresponding pipe system, then connected to the condensate pump and finally returned to the cooling device; cold water generated by the cooling water pump is transmitted to the cooling device through the cooling pipe system to be transmitted to a building needing to be cooled. The intelligent optimization control method comprises the following steps: S1, data preprocessing; S2, construction of a central air-conditioning system model; and S3, establishment of a central air-conditioning system optimization model. According to the design, a more reasonable intelligent optimization control method can be provided for the central air-conditioning system, the energy consumption of the central air-conditioning system is reduced, and the system efficiency is improved.

Description

technical field [0001] The invention relates to an intelligent optimization control method based on a dynamic simulation platform of a central air-conditioning system, and is particularly suitable for reducing central air-conditioning energy consumption through intelligent modeling. Background technique [0002] With the rapid development of our country's economy, our country's resource and environmental problems are becoming more and more prominent, energy conservation and emission reduction are also valued by the national society, and achieving sustainable green development is also an important goal of our country. At the same time, with the improvement of living standards, more and more large buildings such as shopping malls and office buildings use central air conditioners. Central air-conditioning accounts for about 40%-70% of the total energy consumption of large-scale commercial use in my country. In 2018, building construction and operation accounted for 36% of the t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F24F3/00F24F11/46G06F30/27G06N3/12G06F111/10G06F113/08G06F111/04G06F119/08
CPCF24F3/00F24F11/46G06F30/27G06N3/126G06F2111/10G06F2111/04G06F2113/08G06F2119/08
Inventor 潘林王千逸李奥张舒怡王升卢镜明王雷冲
Owner WUHAN UNIV OF TECH
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