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Spatial load prediction method based on gridding technology and BP neural network

A BP neural network and space load forecasting technology, which is applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as training and failure to fully mine historical load data, so as to improve accuracy, improve economy and accuracy sex, good effect

Inactive Publication Date: 2018-06-19
CHANGCHUN POWER SUPPLY COMPANY OF STATE GRID JILINSHENG ELECTRIC POWER SUPPLY +1
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Problems solved by technology

However, the BP neural network has not been trained based on the load characteristics of the cells, and the existing historical load data has not been fully exploited.

Method used

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  • Spatial load prediction method based on gridding technology and BP neural network
  • Spatial load prediction method based on gridding technology and BP neural network
  • Spatial load prediction method based on gridding technology and BP neural network

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0060] refer to Figure 1-Figure 7 , a kind of space load prediction method based on gridding technology and BP neural network of the present embodiment, comprises the following steps:

[0061] Using the historical load data and power supply range of 10kV feeders in an administrative district of a city in Northeast China from 2005 to 2008, as well as the land use information in the administrative district, the spatial power load of the administrative district in 2009 is predicted.

[0062] 1) Establish a power geographic information system

[0063] ① Registration base map

[0064] Obtain satellite photos of a certain city in Northeast China, and register them according to the actual latitude and longitude in the ground information system environment as a base map;

[0065] ②Establish the 10kV feeder power supply range layer in the area to be predicte...

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Abstract

The invention discloses a spatial load prediction method based on a gridding technology and a BP neural network. The method is characterized by comprising the following steps: a power geographic information system is built; I-class cells are generated according to a power supply range of each 10 kV feeder line in a to-be-predicted area, and data integration is carried out on a historical load value and land use information of each cell; according to the load value, the land use information and a load density equilibrium coefficient of each I-class cell, the load density of each class is solved; II-class cells are generated with equal-sized grids, the already-solved past year class load density index is multiplied by the area of each land use type in each II-class cell and is multiplied bythe corresponding load density equilibrium coefficient to obtain the past year maximum load value of each II-class cell; and by using the advantages of the BP neural network, the BP neural network istrained according to determined training samples and test samples, and the load prediction value of each II-class cell in a target year is obtained.

Description

technical field [0001] The invention relates to the field of spatial electric load prediction in distribution network planning, and is a spatial load prediction method based on grid technology and BP neural network. Background technique [0002] As the basic work of power system planning, spatial load forecasting is used to determine the capacity and optimal location of power supply equipment, which greatly improves the economy of power system construction. Spatial load forecasting is an important research topic in the field of urban power grid planning and construction. Because of its basic nature, it occupies an important position in urban power grid planning and plays a key role in planning quality. [0003] However, the space load forecasting methods used in actual projects are usually directly forecasted on the basis of the annual maximum value of historical load data, and the characteristics of historical load data and space power load are not fully exploited, resultin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06F17/16G06N3/08
CPCG06F17/16G06N3/084G06Q10/04G06Q50/06
Inventor 焦明曦王徭赵晓宁穆冠男张婕肖白
Owner CHANGCHUN POWER SUPPLY COMPANY OF STATE GRID JILINSHENG ELECTRIC POWER SUPPLY
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