Control method of power station house cold source system based on machine learning and particle swarm optimization

A particle swarm algorithm and machine learning technology, applied in machine learning, heating and ventilation control systems, heating and ventilation safety systems, etc., can solve problems such as large interference, load fluctuations, high efficiency and energy saving, and difficult to accurately control, to improve COP indicators, the effect of reducing total energy consumption

Active Publication Date: 2021-03-16
SHANGHAI ANYO ENERGY SAVING TECH
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Problems solved by technology

The HVAC refrigeration process has the characteristics of nonlinearity, strong coupling, large interference, and load fluctuations, so it is difficult to precisely control its high efficiency and energy saving

Method used

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  • Control method of power station house cold source system based on machine learning and particle swarm optimization
  • Control method of power station house cold source system based on machine learning and particle swarm optimization
  • Control method of power station house cold source system based on machine learning and particle swarm optimization

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Embodiment Construction

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0012] figure 1 It is a schematic diagram of the control block diagram of the power station cooling source system based on machine learning and particle swarm algorithm of the present invention; figure 2 This is the control flow chart of the cold source system of the power station room based on machine learning and particle swarm algorithm of the present invention.

[0013] See figure 1 and figure 2 , the control method of the power station cold source system based on machine learning and particle swarm algorithm provided by the present invention includes the following steps:

[0014] S1. Model the cold source system of the power station room according to the air-conditioning and refrigeration process mechanism. The input is the chiller model, the actual cooling capacity of the chiller, the chilled water outlet temperature of the chiller, the wet bul...

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Abstract

The invention discloses a control method of a power station house cold source system based on machine learning and particle swarm optimization. The control method comprises the following steps that S1, modeling is carried out on the power station house cold source system according to an air conditioning refrigeration process mechanism, a refrigerator model, the actual refrigerating capacity of a refrigerator, the chilled water outlet temperature of the refrigerator, the wet bulb temperature and the cooling water supply and return temperature difference are input, and the cooling water inlet temperature of the single refrigerator is output; S2, modeling prediction is carried out on energy consumption of the power station house cold source system based on historical data; and S3, for predicted load demand data, the control parameters of the air conditioner cold source system are optimized in combination with the particle swarm optimization. According to the control method of the power station house cold source system based on machine learning and the particle swarm optimization, a mechanism model and a data driving model of the cold source system are combined, and the PSO intelligentcontrol algorithm is applied to optimize the air conditioner cold source system, so that the total energy consumption of the cold source system is reduced, and therefore the COP index is improved.

Description

technical field [0001] The invention relates to a control method of a cold source system, in particular to a control method of a cold source system of a power station room based on machine learning and particle swarm algorithm. Background technique [0002] The cold source system of the power station room in the factory is one of the most important components of the central air-conditioning system, and it is the source of the air-conditioning system. It accounts for 20% to 30%, and reasonable control and optimized operation can achieve huge energy saving. The HVAC refrigeration process has the characteristics of nonlinearity, strong coupling, large interference, and load fluctuation, so it is difficult to precisely control its high efficiency and energy saving. The central air-conditioning cold source system equipment includes chillers, chilled water pumps, cooling water pumps, cooling towers and other equipment. The parameters between the equipment are coupled with each ot...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F24F11/88F24F11/64G06F30/27G06N3/00G06N20/00G06F111/10G06F119/08
CPCF24F11/88F24F11/64G06N3/006G06N20/00G06F30/27G06F2111/10G06F2119/08
Inventor 宋晓菲
Owner SHANGHAI ANYO ENERGY SAVING TECH
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