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A partitioned maximum load forecasting method based on MapReduce framework

A mapreduce framework, the technology of the maximum load, applied in the direction of forecasting, structured data retrieval, instrumentation, etc.

Inactive Publication Date: 2019-01-18
STATE GRID JIANGSU ELECTRIC POWER CO LTD NANTONG POWER SUPPLY BRANCH
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Problems solved by technology

[0003] It is not uncommon to apply big data technology to distribution network data analysis at home and abroad, but there is no classification, planning and statistics of distribution network data according to the power supply unit, and the display of statistical analysis indicators of station area load directly related to users is not provided. service precedent

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  • A partitioned maximum load forecasting method based on MapReduce framework
  • A partitioned maximum load forecasting method based on MapReduce framework
  • A partitioned maximum load forecasting method based on MapReduce framework

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Embodiment Construction

[0035] The present invention will be further described now in conjunction with accompanying drawing. These drawings are simplified schematic diagrams only to illustrate the basic structure of the present invention in a schematic way, so they only show the components relevant to the present invention.

[0036] Such as figure 1 As shown, a method for predicting the maximum load of a partition based on the MapReduce framework includes the following steps:

[0037] Step a: build a big data analysis platform;

[0038] Step b: Carry out data preprocessing to raw data;

[0039] Step c: judge the area where the power distribution is located by using the intersection point discrimination method;

[0040] Step d: obtain the maximum load of the public transformer and the specific transformer in the station area in a year;

[0041] Step e: Use the linear regression model to predict the maximum load of the public transformer and the specific transformer in the station area respectively...

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Abstract

The invention relates to a partition maximum load forecasting method based on a MapReduce framework, comprising the following steps: step a: building a big data analysis platform; B, carrying out datapreprocessing on the original data; C, judging the area where the distribution is located by adopting the intersection point judging method; D, obtaining the maximum load of the station area public transformer and the station area special transformer in one year; Step e: predicting the maximum load of the station area public transformer and the station area special transformer by using the linearregression model, and the maximum load of the station area is the sum of the two predicted values. The invention adopts a linear regression model to predict the maximum load of the station area public transformer and the station area special transformer respectively, and carries out the maximum load prediction on the basis of the data, provides data support for distribution network management andplanning, and has important significance for safe and economic operation of the distribution network.

Description

technical field [0001] The invention relates to using computer technology to process big data of distribution network, and belongs to the field of big data mining and analysis of distribution network. Background technique [0002] With the State Grid Corporation's strategic goal of building a strong power grid, the number of smart power consumption terminals and collection terminals is increasing day by day, which makes various types of power automation data grow geometrically, showing "large volume" and "multiple types". , "low density" and "fast growth" typical big data characteristics. In the process of distribution network management and planning, a series of data such as power load statistical indicators and voltage analysis statistical indicators can provide decision-making basis for power system planning, design, and dispatching for distribution network decision-making departments. Most of the existing traditional power system information platforms in China use expen...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06F16/242G06F16/2453G06F16/22
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 周嘉李伟伦贲树俊黄霆徐晓轶吉宇季晨宇张乐张敏杨鸣袁健华叶颖杰潘海玲钱天能钱霜秋罗云马骏吴杰代克丽谈永庆蔡雯雯
Owner STATE GRID JIANGSU ELECTRIC POWER CO LTD NANTONG POWER SUPPLY BRANCH
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