Modeling method for multi-scale forecasting of ultra-short-term wind speed based on generative deep confidence network

A technology of deep belief network and modeling method, which is applied in the field of ultra-short-term wind speed multi-scale forecasting and modeling based on generative deep belief network, which can solve the problems of limited forecasting accuracy and inability to deal with wavelet decomposition of high-frequency components. , to achieve the effects of easy multi-step forecasting, multi-step forecasting, and easy adjustment

Active Publication Date: 2019-04-02
HARBIN INST OF TECH +2
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  • Summary
  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, for fields such as wind power generation that require high accuracy in wind speed prediction, traditional machine learning methods cannot handle the high-frequency components obtained from wavelet decomposition well, resulting in limited prediction accuracy.

Method used

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  • Modeling method for multi-scale forecasting of ultra-short-term wind speed based on generative deep confidence network
  • Modeling method for multi-scale forecasting of ultra-short-term wind speed based on generative deep confidence network
  • Modeling method for multi-scale forecasting of ultra-short-term wind speed based on generative deep confidence network

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Experimental program
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specific Embodiment approach 1

[0021] The ultra-short-term wind speed multi-scale forecast modeling method based on the generative deep belief network includes the following steps:

[0022] Step 1. Record the wind speed time series (X 1 ,X 2 ,...,X k ,X k+1 ,...,X k+l ) is a sample point set with a length of k+l; where (X 1 ,X 2 ,...,X k ) as training sample points, the length is k, (X k+1 ,X k+2 ...,X k+l ) as a test sample point, the length is l; use t layer wavelet decomposition to process wind speed data, and obtain t+1 segments of different frequencies c t , d 1 , d 2 ,... d t The wind speed sequence whose length is k+l; c t Indicates the low frequency component, d 1 , d 2 ,... d t Indicates high-frequency components;

[0023] Step 2. Build training set and test set:

[0024] Construct the low frequency component c t The following training set and test set: low frequency component c t The sequence is denoted as (x 1 ,x 2 ,...,x k ,x k+1 ,...,x k+l ), will (x 1 ,x 2 ,...,x k ...

specific Embodiment approach 2

[0028] The wavelet decomposition described in Step 1 of this embodiment is realized by using the Mallat algorithm.

[0029]Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0030] The specific process of using t-layer wavelet decomposition to process wind speed data described in step 1 of the present embodiment is as follows:

[0031] like figure 2 As shown, the time series (X 1 ,X 2 ,...,X k ,X k+1 ,...,X k+l ) is decomposed into low frequency components c 1 and the high frequency component d 1 ; When further decomposing the sequence, only the low frequency component c 1 decomposed into c 2 and d 2 , and do not decompose the high-frequency components, and decompose the decomposed low-frequency components in turn, and finally obtain the low-frequency component c t and the high frequency component d 1 , d 2 ,...,d t , so as to separate the trend item and the fluctuation item.

[0032] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to a generative deep belief network-based multi-scale forecast modeling method for an ultra-short-term wind speed. In order to improve the precision of short-term wind speed multi-step forecast, the method comprises the steps of firstly processing wind speed data by utilizing t-layer wavelet decomposition to obtain t+1 sections of wind speed sequences under different frequencies; constructing training sets for the wind speed sequences under the different frequencies, training a multi-input multi-output DBN regression forecast model based on a DBN network regression algorithm, and testing training effects by utilizing corresponding test sets to obtain forecast models under the different frequencies; and performing wavelet reconstruction on forecast values of the forecast models under the different frequencies to obtain an actual forecast value of the wind speed, thereby realizing forecast of an actual wind speed of a wind power plant. The method is suitable for the field of ultra-short-term wind speed multi-scale forecast.

Description

technical field [0001] The invention relates to a multi-scale prediction modeling method of ultra-short-term wind speed. Background technique [0002] As a clean and non-polluting new energy, wind power has been widely valued by countries all over the world, and it has developed rapidly. In recent years, the capacity of wind power grid-connected has been increasing day by day. However, the uncertainty and volatility of wind power output has therefore become an important factor affecting the safe, stable and economically efficient operation of the power system. Accurate wind farm power prediction is an important basis for solving this problem, which can help the power grid to formulate a reasonable dispatch plan, determine the spinning reserve, and ensure the operation of the power grid safely and economically. Accurate wind speed prediction is the basis and premise of accurately predicting wind power. [0003] In recent years, for ultra-short-term forecasting of wind speed...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
CPCG06F30/367
Inventor 万杰赵鑫宇任国瑞郝莹刘智初锐刘金福于达仁
Owner HARBIN INST OF TECH
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