Method for obtaining load density index based on cellular historical data

A technology of load density and historical data, applied in data processing applications, instruments, forecasting, etc., can solve problems such as large differences, strong sample dependence, and insufficient regular mining of historical load data.

Active Publication Date: 2013-08-21
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

[0004] The traditional load density index calculation usually adopts empirical method, simple analogy method, and rough estimation of the average density of classified loads, which is difficult to meet the accuracy requirements in practical applications.
At present, the calculation method of load density index based on intelligent algorithm generally has the problem of strong dependence on samples, and too much emphasis on the influence of

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  • Method for obtaining load density index based on cellular historical data
  • Method for obtaining load density index based on cellular historical data
  • Method for obtaining load density index based on cellular historical data

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Embodiment Construction

[0059] figure 1 and figure 2 Comparison: The predicted results of Chuanying District in Jilin City in 2009 are basically consistent with the load density distribution within the 10kV power supply range of the actual urban power grid, and the load distribution is more detailed, which can better meet the requirements of urban power grid planning.

[0060] refer to figure 1 and figure 2 , a method for obtaining a load density index based on cell historical load data of the present invention, comprising the following steps:

[0061] (1) Generate cells, with the power supply range of 10kV feeder as Class cell, which contains measured data; the power supply area is formed by dividing the power supply area with a square grid of equal size class of cells where the loads are to be predicted.

[0062] (2) Establish the power geographic information system GIS, the English full name is: Geographic Information System,

[0063] build contains The layer of cell-like information a...

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Abstract

The invention discloses a method for obtaining a load density index based on cellular historical data. The method for obtaining the load density index based on the cellular historical data is characterized by comprising the following steps: a cellular is generated, the power supply area of a 10KV feeder line serves as class cellulars, and the class cellulars contain actual measurement data; the power supply area is divided according to square meshes of the same size to form the class cellulars, and a load is to be forecasted. An electric power geographic information system (GIS) is set up, historical loads, power supply areas and land information of the class cellulars are integrated in the GIS, the cooperation index of the load density is determined, the maximum value of the load density of the class cellulars serves as a benchmark, and the load densities of other similar cellulars are normalized. The classified load densities over the years are obtained, a relation equation between the cellular load and the classified load densities is set up, and the load density is obtained through the least square method. A spatial load is forecasted, the size of the spatial load in a target year is forecasted according to the obtained index of the classified load densities, and the load value of each class cellular is further worked out.

Description

technical field [0001] The invention relates to the field of spatial load prediction in urban distribution network planning, and is a method for obtaining a load density index based on cell historical data. Background technique [0002] As the basis of urban distribution network planning, spatial load forecasting (Spatial Load Forecasting, SLF) not only needs to determine the future load size of each district in the urban distribution network planning area, but also needs to predict the distribution of the load. Only by improving the accuracy of space load forecasting can we more accurately guide the construction and use of substations, feeders, switchgear, etc., and make the development and operation of the power grid more reasonable and economical. [0003] Spatial load forecasting methods are mainly divided into four categories: multivariate method, trend method, land use simulation method and load density index method (classification and zoning method). Among them, the ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 肖白穆钢杨修宇聂鹏
Owner NORTHEAST DIANLI UNIVERSITY
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