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Air source heat pump multi-objective optimization design method of non-dominated sorting genetic algorithm assisted by SVR neural network

A multi-objective optimization, air source heat pump technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of low accuracy, single optimization target, long time consumption, etc., achieve accurate optimization design, simplify inner product Calculation and time saving effect

Pending Publication Date: 2019-06-07
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the problems of low precision, long time consumption and single optimization target of the existing heat pump model, the present invention provides a SVR neural network assisted neural network with high precision and capable of quickly realizing multi-objective optimization of system COP and cost. Multi-objective optimization design method of air source heat pump based on non-dominated sorting genetic algorithm

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  • Air source heat pump multi-objective optimization design method of non-dominated sorting genetic algorithm assisted by SVR neural network
  • Air source heat pump multi-objective optimization design method of non-dominated sorting genetic algorithm assisted by SVR neural network
  • Air source heat pump multi-objective optimization design method of non-dominated sorting genetic algorithm assisted by SVR neural network

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Embodiment Construction

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] refer to figure 1 , an SVR neural network-assisted NSGA-II algorithm multi-objective optimization design method for air source heat pumps, including the following steps:

[0036] Step 1. Parameter selection and data processing according to design requirements

[0037] Select the heat transfer temperature difference T of the air cooler gc , Evaporator heat transfer temperature difference T ev , air cooler pressure p gcAs input variables, the system COP and cost cost are used as output variables, and the input training sample data is normalized so that it is between [0,1]. The training sample data can come from literature or measured in experiments , the normalization formula is as follows:

[0038]

[0039] Where k is the normalized value, x is the normalized data, and x min 、x max are the minimum and maximum values ​​in the normalized data, respectivel...

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Abstract

The invention discloses an air source heat pump multi-objective optimization design method of a non-dominated sorting genetic algorithm assisted by a SVR neural network. The method comprises the following steps: step 1, carrying out parameter selection and data processing according to design requirements; step 2, creating, training and testing a neural network; step 3, performing multi-objective optimization on the air source heat pump by using a non-dominated sorting genetic algorithm based on the trained SVR neural network; and step 4, obtaining the parameter value of the input variable of the optimal solution according to the Pareto solution through the above steps, thereby obtaining the design parameter value of each component, and feeding back the design parameter value to a designer.The SVR neural network assisted non-dominated sorting genetic algorithm-based air source heat pump multi-objective optimization design method provided by the invention is relatively high in precisionand can quickly realize COP and cost multi-objective optimization of a system.

Description

technical field [0001] The invention belongs to an air source heat pump and relates to a multi-objective optimal design method for an air source heat pump. Background technique [0002] Air source heat pump is an energy-saving device that uses high-level energy to make heat flow from low-level heat source air to high-level heat source. The heater is released into the heating object. Air source heat pumps have a wide range of applications, low operating costs, no pollution to the environment, and good energy saving and emission reduction effects. They have been widely used in chemical, thermal energy, heating, HVAC and other fields. [0003] In the design of the heat pump, the heat exchange temperature difference T of the air cooler gc , The heat transfer temperature difference T of the evaporator ev and the air cooler pressure P gc For heat pump compressor power W, cooling capacity Q c , The inlet and outlet pressure of the compressor has a greater impact, which in turn...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/04G06N3/08G06N3/12
Inventor 徐英杰陈宁蒋宁许亮峰
Owner ZHEJIANG UNIV OF TECH
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