Method for obtaining load density index based on cellular historical data

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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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 attributes, mostly horizontal comparison, and insufficient regularity mining of historical load data itself

For the load density index method based on the classification load development curve, the area and scale of each cell generated according to the functional area are not the same, and the load growth trend is also different, or even quite different, so the same type of cells use the same The classification load development curve of the

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