Optimization method for processing load data

An optimization method and load data technology, applied in data processing applications, instruments, forecasting, etc., can solve the problems of untested clustering results and unapplied processing load data, so as to improve accuracy and scheduling accuracy, and user The effect of economical and safe electricity consumption and low load output cost

Inactive Publication Date: 2018-04-17
STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
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Problems solved by technology

However, the validity of the clustering results has not been tested and has not been applied to processing load data, so these are not ideal for processing load history data

Method used

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  • Optimization method for processing load data
  • Optimization method for processing load data
  • Optimization method for processing load data

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Embodiment Construction

[0039] like figure 1 As shown, the present invention is an optimization method for processing load data, comprising the following steps:

[0040] A: Establish a model to test the validity of clustering results, and obtain the best clustering results for historical load data;

[0041] A1: Establish a model to test the validity of the clustering results:

[0042] Assuming that the total number of samples in the data set is N, the effective search range for the number of clusters is Integers in , select the pseudo F-statistic index, that is, the PFS index, as the evaluation index of different clustering results; the PFS index is a statistic from the analysis of variance, and for a P-dimensional variable sample that is not zero, its definition is as follows:

[0043]

[0044] Among them, k is the number of samples to be determined cluster centers, tr(s Wp ) is the trace of the scatter matrix within the sample class, tr(s B p ) is the trace of the scatter matrix between sa...

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Abstract

The invention discloses an optimization method for processing load data. By calculating a clustering validity index, an optimal cluster number k is given in a cluster number search range, so that theshortcoming that a conventional k-means algorithm cannot give the optimal cluster number is overcome; when a PFS value is maximal, the corresponding cluster number k reaches the optimum, and k types of outputs are selected, namely, historical load data is classified into k types to be processed, so that an optimal load historical data processing method is obtained; an improved k-means clustering algorithm is in the cluster number search range; a clustering center is stable and clustering results are subjected to clustering validity index calculation; and the cluster number corresponding to themaximal PFS index value is selected as the optimal cluster number, so that compared with a clustering algorithm in the past, a great improvement is achieved.

Description

technical field [0001] The invention relates to the technical field of power system engineering, in particular to an optimization method for processing load data. Background technique [0002] Power load forecasting is an important task in the daily operation of the power system. Accurate load forecasting plays a very important role in the economical and reliable load adjustment and management of the power system. However, effective processing of historical load data is a prerequisite for more accurate load forecasting. [0003] As an important unsupervised mode in data mining, cluster analysis can be roughly divided into the following categories: partition-based clustering methods, hierarchical clustering methods, grid-based clustering methods, and density-based clustering methods. class methods etc. Among them, the partition-based clustering algorithm generally adopts the k-means algorithm. The k-means algorithm is widely used in processing load historical data due to it...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 李朝晖杨海晶石光马瑞滕卫军韩伟王骅龚人杰孙亮
Owner STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST
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