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Wind power prediction method based on hybrid cross-correlation entropy long short-term memory network

A technology of wind power forecasting and long-term short-term memory, which is applied in forecasting, neural learning methods, biological neural network models, etc., and can solve the problems of low accuracy and robustness of forecasting results

Pending Publication Date: 2021-09-10
XIAN UNIV OF TECH
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  • Claims
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Problems solved by technology

[0004] The purpose of the present invention is to provide a wind power prediction method based on hybrid cross-correlation entropy long short-term memory network, which solves the problem of low accuracy and robustness of the prediction results of existing prediction methods in the prior art

Method used

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  • Wind power prediction method based on hybrid cross-correlation entropy long short-term memory network
  • Wind power prediction method based on hybrid cross-correlation entropy long short-term memory network
  • Wind power prediction method based on hybrid cross-correlation entropy long short-term memory network

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Embodiment

[0122] Considering that the wind power sequence fluctuates strongly and is affected by seasonal factors. In the implementation case, the relevant data of four typical months (January, April, July, and October) in spring, summer, autumn and winter of a demonstration decentralized wind farm in a certain place were collected at a time interval of 5 minutes.

[0123] Step 1. Taking October data as an example, the original wind power sequence is adaptively decomposed into 16 subsequences;

[0124] Step 2, using the sample entropy function to reconstruct the 16 subsequences into 4 new feature sequences;

[0125] Step 3, normalize the wind speed, new characteristic power sequence, temperature and wind direction cosine;

[0126] Step 4. Construct PMC-LSTM prediction models for the four new feature sequences, and obtain four reconstructed sequence prediction values. The relevant parameters are given in Table 1;

[0127] Table 1 Data-related parameter settings for October

[0128] ...

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Abstract

The invention discloses a wind power prediction method based on a hybrid cross-correlation entropy long short-term memory network. The method comprises the following steps: decomposing an original wind power sequence into K subsequences; carrying out complexity evaluation on the K subsequences, and reconstructing to obtain M reconstructed feature sequences; normalizing the wind speed, the wind direction cosine value, the temperature and the M reconstructed feature sequences; respectively establishing prediction models based on a hybrid cross-correlation entropy long short-term memory network for the processed M reconstructed feature sequences, and optimizing related parameters of the prediction models to obtain predicted values of the M reconstructed feature sequences; and linearly superposing predicted values of the M reconstructed feature sequences to obtain a wind power prediction result. The prediction precision can be effectively improved, and the purposes of high precision and strong robustness are achieved.

Description

technical field [0001] The invention belongs to the technical field of wind power prediction methods, and relates to a wind power prediction method based on a hybrid cross-correlation entropy long-short-term memory network. Background technique [0002] With the advancement of the energy revolution, renewable energy has attracted worldwide attention due to its green, low-carbon, and non-polluting characteristics. Among them, wind energy has become one of the most potential alternative energy sources due to its cleanness and wide distribution. However, wind energy has inherent characteristics such as intermittency, randomness, and volatility, which seriously restrict its utilization efficiency. Developing a high-precision and robust forecasting model is one of the effective measures to improve the efficiency of wind energy utilization. However, there are a large number of outliers in actual wind farms due to the influence of random factors such as meteorological conditions, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/08G06N3/044Y04S10/50
Inventor 段建东王鹏马文涛方帅
Owner XIAN UNIV OF TECH
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