Power distribution network space load prediction method taking region and load property dual differences into consideration

A technology of spatial load forecasting and distribution network, applied in the field of power system, can solve the problems of not considering the impact of load density, unable to follow or learn from, and weak application applicability.

Active Publication Date: 2016-10-12
STATE GRID ZHEJIANG ELECTRIC POWER COMPANY ECONOMIC TECHN INST +1
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Problems solved by technology

[0004] The electricity load is obviously affected by the local economy and industrial development, and the load density reflects large regional differences. All regions cannot follow or learn from the same set of standards. It is extremely time-consuming an

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  • Power distribution network space load prediction method taking region and load property dual differences into consideration
  • Power distribution network space load prediction method taking region and load property dual differences into consideration
  • Power distribution network space load prediction method taking region and load property dual differences into consideration

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Embodiment Construction

[0036] The present invention will be further elaborated below with examples.

[0037] Step 1: Taking Zhejiang Power Grid as an example, conduct extensive research on the load sharing types of each block in 11 cities including Hangzhou and Ningbo. Establish a full sample space that considers regional differences. The full sample space in this embodiment is composed as follows: 1) Regional information of 10 other cities in Zhejiang except Huzhou; 2) 10 cities such as Hangzhou and Ningbo belong to industry, commerce, and residential buildings. The load density and influencing factors information of 2,386 survey samples of four main load types, such as office load, administrative office, etc.

[0038] Taking the 24-point daily load curve of 100 samples on a certain working day in January 2016 as the object, the improved k-means algorithm is used for cluster analysis. The cluster results are shown in the table below.

[0039]

[0040] Step 2: Select 25 samples from each categor...

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Abstract

The invention relates to a power distribution network space load prediction method taking region and load property dual differences into consideration. A conventional space load prediction method does not take influences exerted by regional differences, sample types and quality on load density into consideration, and the applicability is not sufficient. First of all, a full-sample space taking the regional differences into consideration is established, and then, a typical sample is screened by verifying and carefully choosing a load through a typical daily load curve. A region where the load is disposed is clustered through weighted Euclidean distance tolerance, afterwards, a subsample space matching is carried out, a type which the sample belongs to is determined, load density of a plot to be measured is predicted by use of an SVM algorithm, and a total future load amount of the plot is calculated through the load density. The load prediction precision is quite high, and the application is facilitated.

Description

technical field [0001] The invention belongs to the field of power systems, in particular to a space load clustering and forecasting method suitable for distribution networks. Background technique [0002] With the development of the urbanization process and the adjustment of the economic structure, the urban grid load has shown a significant increase, which puts forward higher requirements for the planning and design of the urban grid. [0003] Spatial load forecasting is the basis of urban distribution network planning. Among the various methods of spatial load forecasting, the load density index method is suitable for areas with relatively clear land planning, and is widely used in our country. The key to the application of the method is to determine the load density of each plot in the planning area. [0004] The electricity load is obviously affected by the local economy and industrial development, and the load density reflects large regional differences. All regions ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62G06Q50/06
CPCG06Q10/04G06Q50/06G06F18/2411
Inventor 刘卫东傅旭华钟宇军叶承晋白桦黄晶晶黄民翔刘思马润泽丁嘉涵
Owner STATE GRID ZHEJIANG ELECTRIC POWER COMPANY ECONOMIC TECHN INST
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