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

Heat supply load prediction energy-saving control method and system based on BP neural network prediction model

A BP neural network and predictive model technology, applied in heating systems, space heating and ventilation details, household heating, etc., can solve problems such as heat loss, low level of automation, lack of predictive technology, etc., to improve comfort , the effect of reducing energy consumption

Pending Publication Date: 2021-12-07
浙江正泰聚能科技有限公司
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Among them, about 40% of the total energy consumption of the building comes from the central heating system, and the existing central heating system cannot meet the needs of users due to the low level of automation and the lack of advanced forecasting technology. heat, leading to a decrease in heating user comfort, and also causing a large amount of heat loss

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
  • Heat supply load prediction energy-saving control method and system based on BP neural network prediction model
  • Heat supply load prediction energy-saving control method and system based on BP neural network prediction model
  • Heat supply load prediction energy-saving control method and system based on BP neural network prediction model

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0050] Example 1, the following is an example of the arrangement of the original data of the heating load influencing factors:

[0051] [a 1 a 2 a 3 …a n ;b 1 b 2 b 3 …b n ; c 1 c 2 c 3 …c n ;d 1 d 2 d 3 … d n ; e 1 e 2 e 3 ...e n ], where a, b, c, d and e each represent a heat load influencing factor, and all factors are arranged in rows.

example 2

[0052] Example 2, the following is another example of arrangement of raw data of heating load influencing factors:

[0053] [a 1 b 1 c 1 d 1 e 1 ;

[0054] a 2 b 2 c 2 d 2 e 2 ;

[0055] a 3 b 3 c 3 d 3 e 3 ;

[0056]

[0057] a n b n c n d n e n ], where a, b, c, d and e each represent a heat load influencing factor, and all factors are arranged in columns.

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 relates to the technical field of central heating, in particular to a heat supply load prediction energy-saving control method and system based on a BP neural network prediction model. The heat supply load prediction energy-saving control method and system predict a heating load Q3 of a heating building in each heating period by utilizing the BP neural network prediction model to obtain a heating load prediction value; and the opening degree of an intelligent balance valve IBV of the heating building is adjusted according to the heating load prediction value, energy saving is facilitated, and the thermal comfort of heating users is improved. The invention further relates to a heat supply load prediction energy-saving control system based on the BP neural network prediction model, and the heat supply load prediction energy-saving control system applies the method.

Description

technical field [0001] The invention relates to the technical field of central heating, in particular to a BP neural network-based heat load prediction and energy-saving control method and system for central heating. Background technique [0002] At present, all parts of the world are facing huge challenges in terms of energy. In my country, energy issues have become the core issue of social development, and efficient use of energy technologies has gradually attracted the attention of researchers. Survey data show that China's building energy consumption accounts for 20% to 40% of the country's total energy consumption, and it is still growing at a rate of more than 10% every year. Among them, about 40% of the total energy consumption of the building comes from the central heating system, and the existing central heating system cannot meet the needs of users due to the low level of automation and the lack of advanced forecasting technology. Heat, resulting in reduced heatin...

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): F24D19/10
CPCF24D19/1015
Inventor 朱冬雪张帆卞志勇葛雪锋
Owner 浙江正泰聚能科技有限公司
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