Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on radial basis function neural network

A multi-objective optimization, air source heat pump technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of model optimization, inability to establish a system, etc., to reduce consumption, reduce greenhouse gas emissions, The effect of increasing the convergence speed

Active Publication Date: 2019-04-16
ZHEJIANG UNIV OF TECH
View PDF4 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional experimental methods often can only predict the established model or only perform single-objective optimization, and cannot directly model the system and perform multi-objective optimization

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on radial basis function neural network
  • Multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on radial basis function neural network
  • Multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on radial basis function neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] refer to figure 1 , a multi-parent genetic algorithm air source heat pump multi-objective optimal control method based on radial basis function neural network, including the following steps:

[0034] Step 1. Input the input and output variables into the system according to user needs;

[0035] Select compressor frequency f, expansion valve opening p, water pump frequency n as input variables, system COP and heating capacity Q h Or carbon dioxide emission m and heating capacity Q hAs an output variable, normalize the input training sample data 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:

[0036]

[0037] 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, respectively; ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on a radial basis function neural network. The method comprises the following steps that 1, input and output variables are input into a system according to user requirements; 2, creating, training and testing a radial basis function neural network; 3, performing multi-objective optimization on an air source heat pump by using a multi-parent genetic algorithm based on the trained radial basis function neural network; and 4, obtaining the parameter value of the input variable of the optimal solution according to the Pareto solution through the above steps, and transmitting the obtained input variable value to the system to adjust the control quantity of the heat pump. The multi-objective optimization of the COP heating capacity Qh or the carbon dioxide release amount m and the heating capacity Qh of the system can be rapidly realized while the precision is high.

Description

technical field [0001] The invention belongs to an air source heat pump and relates to a multi-objective optimal control method for an air source heat pump. Background technique [0002] The air source heat pump absorbs the heat in the air as an energy source, and drives the compressor to operate with a small amount of electric energy to release the heat in the air absorbed by the evaporator to the heating object through the heat exchanger. An energy-saving device where heat source air flows to a high-level heat source. 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 today's world, as a greenhouse gas, carbon dioxide emissions have received widespread attention, and the energy efficiency ratio COP, carbon dioxide emissions and heating Q h It is an important p...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 徐英杰陈宁许亮峰蒋宁
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com