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A spatial load prediction method based on fuzzy information granulation and a support vector machine

A space load forecasting and support vector machine technology, applied in information technology support systems, forecasting, instruments, etc., can solve the problems of ignoring the in-depth mining of measured historical load data, insufficient utilization of historical load data, etc.

Inactive Publication Date: 2019-03-08
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

This type of method has insufficient utilization of historical load data and neglects the in-depth mining of measured historical load data

Method used

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  • A spatial load prediction method based on fuzzy information granulation and a support vector machine
  • A spatial load prediction method based on fuzzy information granulation and a support vector machine
  • A spatial load prediction method based on fuzzy information granulation and a support vector machine

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Embodiment Construction

[0077] The present invention will be further described below using the accompanying drawings and examples.

[0078] In the technical field, the English and abbreviation based on fuzzy information granulation is usually (fuzzy information granulation, FIG); the English and abbreviation of support vector machine is (support vector machine, SVM); the English and abbreviation of spatial load prediction is (spatial load forecasting, SLF); Therefore, the spatial load prediction method based on fuzzy information granulation and support vector machine of the present invention is referred to as FIG-SVM SLF method for short.

[0079] refer to Figure 1-Figure 9 , the space load prediction method based on fuzzy information granulation and support vector machine of the present invention, wherein, comprises the following steps:

[0080] The historical load data of each type I cell from January 1, 2013 to November 30, 2015 in an administrative district of a city in Henan is taken as the mo...

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Abstract

The invention relates to a spatial load prediction method based on fuzzy information granulation and a support vector machine, which is characterized by comprising the following steps of firstly constructing an electric power geographic information system, and respectively generating two types of cells in the electric power geographic information system; secondly distinguishing the thickness of the I-type cellular load granularity according to the length of the time scale, establishing a reasonable fuzzy set by dividing a fuzzy granulation window to carry out fuzzy information granulation on the historical load data under the I-type cellular fine granularity, and further determining a reasonable maximum value of the historical load under the I-type cellular coarse granularity; then predicting the class I cellular load under the coarse granularity by adopting a support vector machine model; and finally determining a class-I cellular load density balance coefficient, solving a classification load density index, and solving each class-II cellular load prediction value by combining land use information, thereby realizing meshing of the spatial power load prediction result. The method has the advantages of being scientific, reasonable, high in applicability, better in prediction effect and the like.

Description

technical field [0001] The invention relates to the field of spatial load prediction in distribution network planning, and is a spatial load prediction method based on fuzzy information granulation and support vector machine. Background technique [0002] Spatial load forecasting (SLF) is the basic work of power system planning, and its results guide the investment planning of future power grids. The accuracy of spatial load forecasting not only affects the transformer capacity, grid structure, voltage level, and conductor cross-section. The choice has a profound impact on the rationality of the entire power network layout, the economy and safety of power grid construction and operation. [0003] Currently, there are four most commonly used SLF methods: multivariate method, land use simulation method, trend method, and load density index method. The multivariate variable method regards the factors affecting the load as relevant variables, and on this basis, establishes a co...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 肖白赵晓宁姜卓
Owner NORTHEAST DIANLI UNIVERSITY
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