Power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing

A parallel computing, peak load technology, applied in computing, computer components, electrical and digital data processing, etc., can solve problems such as poor clustering effect and slow operation speed

Pending Publication Date: 2019-07-30
SHENYANG POLYTECHNIC UNIV
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Problems solved by technology

[0007] The present invention overcomes the defects of traditional clustering techniques, and proposes a load curve clustering algorithm based on Pearson's correlation

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  • Power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing
  • Power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing
  • Power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing

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Embodiment Construction

[0080] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0081] In order to achieve the above-mentioned purpose of the invention, a network peak load clustering extraction algorithm based on Pearson coefficient and MapReduce parallel calculation of the present invention is characterized in that it comprises the following steps:

[0082] Data acquisition and preprocessing:

[0083] By collecting historical data, preprocessing the collected data, cleaning abnormal data, taking into account the sudden increase or drop of load power, when the difference between adjacent points of the load curve power is large, use Neville based on Lagrange interpolation Algorithm pair curve X={x 1 ,x 2 ,...,x n} Perform interpolation repair, denoise the data, and finally form a sample data set that can be used for load forecasting;

[0084] Dimensio...

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Abstract

A power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing comprises the following steps: 1, conducting data cleaning and abnormal data restoration on a load data set, and then conducting dimensionality reduction on the load data set; 2, storing the data set subjected to dimension reduction in a Hadoop distributed file system line by line, anddividing the data set into slices to form sub-data sets; 3, reading each slice sub-data set in the step 2 by using a MapReduce computing architecture, and selecting an initial clustering center by using a Pearson correlation coefficient as a similarity criterion through a parallel model; and 4, allocating the clustering calculation task to the Map task node in the MapReduce, and combining the initial clustering center in the step 3 to complete parallel clustering of the data set. The method is more suitable for power load big data processing derived from more and more vigorous development information times.

Description

technical field [0001] The invention relates to a large data clustering algorithm based on a Map Reduce parallel framework, and is especially suitable for large-scale power grid load data sets during peak hours with complex calculations. Background technique [0002] With the vigorous development of smart grids and energy storage, massive power data are continuously generated in the operation, maintenance and management of the power grid, among which demand-side big data accounts for a large proportion. The planning and operation of smart grids require a good data foundation, so big data processing, load extraction and forecasting based on demand-side response need to be studied urgently. The demand-side big data includes high-dimensional and massive user daily / monthly load curves. Accurate analysis and research on these power consumption information data and obtaining corresponding load patterns can provide an important basis for decision-making of power grid companies. ...

Claims

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Application Information

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IPC IPC(8): G06F16/182G06F16/215G06K9/62
CPCG06F16/182G06F16/215G06F18/23213
Inventor 崔嘉刘思彤杨俊友葛维春张宇献于仁哲刘云飞郭海宇
Owner SHENYANG POLYTECHNIC UNIV
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