A wind power forecasting method based on variable mode decomposition and long-short memory network

A technology of variable mode decomposition and prediction method, which is applied in the field of wind power prediction combined with variable mode decomposition and long-short memory network, can solve the problem of lack of theoretical foundation, difficulty in obtaining delay time and embedded dimension of phase space reconstruction technology, wind power signal Decompose dependencies and other issues

Inactive Publication Date: 2019-01-18
CHINA UNIV OF MINING & TECH
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Problems solved by technology

However, it is difficult to select the wavelet basis function in the wavelet method, and it is difficult to obtain the delay time and embedding dimension in the phase space reconstruction technology. The empirical mode decomposition method depends on experience and lacks a theoretical basis for the decomposition

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  • A wind power forecasting method based on variable mode decomposition and long-short memory network
  • A wind power forecasting method based on variable mode decomposition and long-short memory network
  • A wind power forecasting method based on variable mode decomposition and long-short memory network

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Embodiment Construction

[0055] The present invention will be further explained below in conjunction with the accompanying drawings.

[0056] A wind power prediction method based on variable model decomposition and long-short memory network, taking the actual wind power data of ELIA company in Belgium as an example, including the following steps:

[0057] Step 1: Measure the historical data P(t) of wind power;

[0058] Step 2: Perform variable mode decomposition on the wind power signal to obtain the multimodal component u k , k takes 1, 2, and 3, respectively representing the long-term component, short-term fluctuation component and ultra-short-term random component in the wind power signal. The specific steps are as follows:

[0059] Step 2.1: For each modal function u k (t), using the Hilbert transform to calculate the corresponding analytical signal, the corresponding one-sided spectrum is obtained as:

[0060]

[0061] In the formula:

[0062] Step 2.2: For each modal function u k (t), ...

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Abstract

The invention discloses a wind power prediction method based on a variable mode decomposition and a long-short memory network. Firstly, the wind power signal is decomposed into three modal componentssuch as a long-term sub-component, a fluctuation number and a random component according to the characteristics of the wind power. Then, the long-short memory network is used to learn the modal components of wind power, and the prediction model is established. Finally, the prediction values are synthesized. The method can obtain the random characteristics of wind power and improve the accuracy andstep size of wind power forecasting.

Description

technical field [0001] The invention belongs to the technical field of electric power system control, and in particular relates to a wind power prediction method combining variable mode decomposition and long-short memory network. Background technique [0002] The uncertainty brought about by the inherent volatility and randomness of wind power will cause the imbalance of power generation and supply, and bring a series of difficulties to the power system. Improving the accuracy of wind power forecasting is an effective means to solve these problems, so it has been a research hotspot for a long time. [0003] Wind power is divided into prediction methods based on physical models and statistical models. The method based on physical model relies on meteorological data, and its implementation is more complicated. The prediction method based on statistical model establishes the linear or nonlinear mapping between the actual value and the predicted value time series by summarizi...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 韩丽王雪松
Owner CHINA UNIV OF MINING & TECH
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