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A Neural Network-Based Distribution Transformer Load Hierarchical Prediction Method and Device

A distribution transformer and neural network technology, applied in the field of distribution transformer load hierarchical prediction based on neural network, can solve the problems of small power supply area, inaccurate prediction results, large fluctuations in power load, etc., and improve the accuracy of trend judgment or the effect of numerical judgment accuracy

Active Publication Date: 2021-09-03
GUANGDONG POWER GRID CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The embodiment of the present invention provides a distribution transformer load hierarchical prediction method and device based on a neural network, which is used to solve the problem of inaccurate prediction results caused by the existing load prediction method for distribution transformers due to small power supply areas and large fluctuations in power loads. accurate technical questions

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  • A Neural Network-Based Distribution Transformer Load Hierarchical Prediction Method and Device
  • A Neural Network-Based Distribution Transformer Load Hierarchical Prediction Method and Device
  • A Neural Network-Based Distribution Transformer Load Hierarchical Prediction Method and Device

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Embodiment 1

[0042] figure 1 It is a flow chart of the steps of the neural network-based distribution transformer load hierarchical forecasting method described in the embodiment of the present invention.

[0043] Such as figure 1 As shown, the embodiment of the present invention provides a neural network-based distribution transformer load hierarchical forecasting method, including the following steps:

[0044] S10. Data acquisition and processing: Obtain the historical load data of distribution transformers and attribute information data of distribution transformers in the previous 4 years from the metering system of the distribution network; process the historical load data and attribute information data to obtain the maximum annual load Value, annual maximum load change rate and distribution transformer commissioning time mark quantity; attribute information data includes distribution transformer commissioning time;

[0045] S20. Sample classification: The maximum value of the annual...

Embodiment 2

[0069] Figure 4 It is a frame diagram of the neural network-based distribution transformer load stratification forecasting device described in the embodiment of the present invention.

[0070] Such as Figure 4 As shown, the embodiment of the present invention also provides a distribution transformer load hierarchical prediction device based on a neural network, including a data acquisition processing module 10, a sample classification module 20, a first model building module 30, data selection and normalization processing Module 40, second module building module 50 and output prediction result module 60;

[0071] The data acquisition processing module 10 is used to obtain the historical load data of the distribution transformer and the attribute information data of the distribution transformer in the previous 4 years from the metering system of the distribution network; the historical load data and the attribute information data are processed to obtain the annual The maxim...

Embodiment 3

[0081] An embodiment of the present invention provides a computer-readable storage medium. The computer storage medium is used to store computer instructions. When the computer is run on a computer, the computer executes the above neural network-based distribution transformer load hierarchical prediction method.

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Abstract

The embodiment of the present invention relates to a distribution transformer load stratification prediction method and device based on neural network, including data acquisition processing, sample classification, establishment of BP neural network model, data selection and normalization processing, establishment of CNN neural network model and In the six steps of outputting forecast results, the BP neural network model and CNN neural network model constructed by the historical load data of the previous 4 years and the attribute information data of distribution transformers decompose the direct load forecasting into trend forecasting and numerical forecasting, which is convenient in Under different levels of information acquisition, the trend judgment accuracy or numerical judgment accuracy can be improved according to the data quality and information richness, and the existing load prediction method for distribution transformers is solved due to the small power supply area and large power load fluctuations. Inaccurate results due to technical issues.

Description

technical field [0001] The invention relates to the technical field of distribution transformer loads, in particular to a neural network-based distribution transformer load layered prediction method and device. Background technique [0002] In recent years, there have been more and more smart distribution networks. Effective management of various smart devices in the smart distribution network can promote the construction of smart distribution networks and improve the level of power grid operation and management. In the context of implementing the unit system, refined planning, and operation and maintenance of the distribution network, load forecasting based on the low-voltage station area is of great importance for grid load development trend and planning demand analysis, distribution network weak link identification, and power system analysis. Operation mode adjustment and other businesses are of great significance. [0003] With the popularization of on-line monitoring e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06N3/04G06N3/08H02J3/00
CPCG06Q10/04G06Q50/06G06N3/084H02J3/003G06N3/044G06F18/241
Inventor 曹华珍唐俊熙高崇吴亚雄许志恒陈沛东王天霖张俊潇程苒黄烨何璇李浩李阳李耀东刘瑞宽张道路
Owner GUANGDONG POWER GRID CO LTD
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