Wavelet transformation and improved firefly-optimized extreme learning machine-based short-term load prediction method

A short-term load forecasting, extreme learning machine technology, used in forecasting, data processing applications, instruments, etc.

Inactive Publication Date: 2016-09-28
WUHAN UNIV
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AI Technical Summary

Problems solved by technology

However, due to the strong randomness and complexity of load changes, various methods have certain applicable occasions, and need to be continuously improved and perfected.

Method used

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  • Wavelet transformation and improved firefly-optimized extreme learning machine-based short-term load prediction method
  • Wavelet transformation and improved firefly-optimized extreme learning machine-based short-term load prediction method
  • Wavelet transformation and improved firefly-optimized extreme learning machine-based short-term load prediction method

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Embodiment Construction

[0074] In order to further illustrate the process and specific steps of the present invention, further description will be given in conjunction with specific examples.

[0075] like figure 1 As shown, a short-term load forecasting method based on wavelet transform and improved firefly-optimized extreme learning machine includes the following steps:

[0076] Step 1. Obtain the original load sequence data;

[0077] Step 2. Perform 3-layer wavelet decomposition on the original sequence. Specifically, for short-term load forecasting, according to the multi-resolution idea proposed by Mallat, the non-stationary discrete load sequence S is decomposed into high-frequency detail sequences d with different frequencies. 1 ,d 2 …d J and a low frequency approximation sequence a J , J is the maximum number of decomposition layers; usually db3 wavelet basis is used for 3-layer decomposition; the decomposition process is as follows:

[0078] a...

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Abstract

The invention relates to a wavelet transformation and improved firefly-optimized extreme learning machine-based short-term load prediction method. The method includes the following steps that: noise reduction is carried out on an original load sequence through wavelet decomposition and reconstruction; (2) in a model training stage, improved firefly algorithm optimized extreme learning machine parameters are utilized to obtain the optimal model of sub sequences; and (3) the final prediction values of the sub sequences, which are obtained superposition, are predicted. According to the method, numerical value calculation is carried out on data sequences of two kinds of time scales, and therefore, problems in short-term load prediction can be solved effectively. Compared with a plurality of classic prediction models such as a traditional ARMA, a BP neural network, a support vector machine and an LSSVM, the model adopted by the method has a prediction effect.

Description

technical field [0001] The invention relates to the field of power system automation, and relates to a short-term load prediction method based on wavelet transform and improved firefly optimization extreme learning machine. Background technique [0002] Load forecasting has always been a key operational and planning approach in the power system, influencing many power system decisions such as economic dispatch, automatic generation control, safety assessment, maintenance dispatch, and energy commercialization [1] . Short-term power load forecasting mainly refers to forecasting the electricity consumption in the next few hours, one day to several days [2] . Accurate power load forecasting can economically and reasonably arrange the start and stop of power system generators, which plays an important role in maintaining the safety and stability of power grid operation, maintaining normal production and life in society, and effectively reducing power generation costs. After l...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 陈思远方必武王佳丽
Owner WUHAN UNIV
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