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

Load density index acquiring method based on fuzzy maxiumum approximation degree theory

A technology of load density and proximity, applied in data processing applications, instruments, forecasting, etc., can solve problems such as lack of scientificity and quantitative results

Inactive Publication Date: 2006-08-30
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to many factors affecting the load density index, this approach lacks scientific and quantitative results

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
  • Load density index acquiring method based on fuzzy maxiumum approximation degree theory
  • Load density index acquiring method based on fuzzy maxiumum approximation degree theory

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] The fuzzy maximum closeness method is to make a horizontal comparison between the environment of the predicted area and the environmental factors of other reference areas. Select a number of reference areas, based on a certain main factor, select one of them that is closest to the measured area according to the "nearest selection principle", and compare the main factors of this area with the main factors of the measured area. The corresponding load density index when approaching is the load density index of the measured area.

[0035] Taking the calculation of residential land load density index as an example, the calculation steps are as follows:

[0036] ①Take 8 parameters as the environmental parameters, such as the number of residential floors, residential area, construction standard category, economic development level coefficient of the area, climate coefficient, conservation concept coefficient, demand side management coefficient, and major event coverage coeffic...

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 load density index deriving method based on fuzzy maximum closeness theory, characterized by firstly introducing the fuzzy maximum closeness theory to derive the load density index, refining and quantizing factors of influencing the load density index, and comprising the steps of: forming a sample library according to surveyed and statistic data; dividing a planned region into several functional regions; for some type of functional region, setting main factors of influencing the load density index as environmental parameters; calculating membership grade sets of to-be-measured functional region and primary-screened sample regions; obtaining maximum closeness fuzzy subsets of each sample region and to-be-measured functional region, namely corresponding closeness values; selecting load density index of a sample region with the maximum closeness as a forecasted value of the to-be-measured functional region. And it can derive the forecasted load value relatively fitting the actual conditions.

Description

technical field [0001] The invention relates to a method for obtaining a load density index in urban distribution network planning load forecasting, in particular to a method for obtaining a load density index based on the theory of fuzzy maximum closeness degree. Background technique [0002] The load density index method, that is, the spatial load forecasting method, is a load forecasting method proposed in the 1980s. It can not only predict the total load, but also predict the geographical distribution of future loads. This method is currently used in urban distribution network planning. One of the commonly used methods. Compared with the general method, this method considers more factors, so it is more reliable. The general steps of the load density index method are: by collecting the future land planning scheme of the planning area from the government planning department, the distribution of the load and the corresponding area (occupied area or building area) of each f...

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
IPC IPC(8): G06Q10/00G06Q50/00G06Q10/04G06Q50/06
Inventor 符杨朱兰胡荣
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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