Method and system for predicting wind speed

A technology for predicting wind speed and wind speed prediction, which is applied in the field of wind speed prediction, and can solve problems such as least squares support vector machine performance dependence, failure to find the optimal value, over-smoothing, etc.

Inactive Publication Date: 2017-10-20
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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AI Technical Summary

Problems solved by technology

[0003] There are three main methods of decomposing wind speed series in the prior art: (1) wavelet decomposition, this decomposition method has good time-frequency localization characteristics, but the decomposition effect depends on the selection of basis functions, and its adaptability is poor
(2) Empirical mode decomposition, this decomposition method has strong adaptability, but there are problems such as endpoint effect and over-envelope
(3) Local mean decomposition. This decomposition method has fewer iterations and lighter endpoint effects, but the conditions for judging pure FM signals need to be tried and tested. If the sliding span is not selected properly, the function will not converge, resulting

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  • Method and system for predicting wind speed

Examples

Experimental program
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Example Embodiment

[0075] Embodiment 1: as figure 1 As shown, a method of predicting wind speed includes:

[0076] Step 11: Obtain the original sequence of wind speed data;

[0077] Step 12: Use the particle swarm optimization algorithm to determine the optimal preset scale parameters and optimal bandwidth parameters of the variational mode decomposition method, and decompose the original sequence into several modal function subunits according to the optimal preset scale parameters and optimal bandwidth parameters sequence;

[0078] Step 13: Using the improved differential evolution algorithm to determine the kernel parameters of the least squares support vector machine model of each modal function subsequence, the variation factor of the mutation operation in the improved differential evolution algorithm decreases with the increase of the evolution algebra, and the variation The mutated individual generated by the operation is related to the optimal individual of the previous generation, and ...

Example Embodiment

[0102] Example 2:

[0103] like image 3 As shown, a system for predicting wind speed includes:

[0104] Data acquisition module 21, used to acquire the original sequence of wind speed data;

[0105] The subsequence determination module 22 is used to determine the optimal preset scale parameter and the optimal bandwidth parameter of the variational mode decomposition method by using the particle swarm optimization algorithm, and according to the optimal preset scale parameter and the optimal bandwidth parameter, the The original sequence is decomposed into several modal function subsequences;

[0106] Kernel parameter determination module 23, is used for adopting improved differential evolution algorithm to determine the kernel parameter of the least squares support vector machine model of each modal function subsequence, the variation factor of mutation operation in the improved differential evolution algorithm increases with the increase of evolution algebra and the mutat...

Example Embodiment

[0129] Embodiment 3: as Figure 5 As shown, methods for predicting wind speed include:

[0130] Step 31: Obtain the short-term wind speed data of the wind farm as the original sequence of wind speed data:

[0131] The original sequence of the actual wind speed is 31 days of hourly average wind speed data, a total of 744 data, a total of 720 sample points of the data in the first 30 days were selected as the training set, the signal was decomposed and the sub-model was established; the data of the 31st day was selected in total 24 The sample points are used as the prediction set to test the prediction accuracy of the model.

[0132] Step 32: Use the particle swarm optimization algorithm to determine the optimal preset scale parameters and optimal bandwidth parameters of the variational mode decomposition method, and decompose the original sequence into modal function subunits according to the optimal preset scale parameters and optimal bandwidth parameters sequence:

[0133]...

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Abstract

The invention discloses a method and system for predicting wind speed. The method includes: obtaining the original sequence of wind speed data; using a particle swarm algorithm to determine the optimal preset scale parameter and optimal bandwidth parameter of the variational mode decomposition method, and converting the original sequence Decompose into several modal function subsequences; use the differential evolution algorithm to determine the kernel parameters of the least squares support vector machine model of each modal function subsequence, the variation factor of the mutation operation decreases with the increase of the evolutionary algebra, and the generated mutant individuals It is related to the optimal individual of the previous generation, and the crossover probability factor of the crossover operation increases with the increase of the evolutionary algebra; according to the autocorrelation of each modal function subsequence and each kernel parameter, determine the least squares of each modal function subsequence Support vector machine wind speed prediction sub-model, and predict the decomposition wind speed of each sub-sequence through each wind speed prediction sub-model; determine the final wind speed prediction value according to each decomposition wind speed. The method and system provided by the invention can accurately predict wind speed.

Description

technical field [0001] The invention relates to the field of time series prediction, in particular to a method and system for predicting wind speed. Background technique [0002] As a renewable and clean energy, wind power has been developed on a large scale in my country in recent years. At the same time, the randomness, intermittence and volatility of wind power have brought security risks to the stability and economic operation of the power grid. Accurate wind power forecasting can provide an important basis for power dispatching and effectively reduce the impact of wind power on the grid. Since wind power has a direct relationship with wind speed, wind power can be predicted through wind speed prediction, so it is very necessary to accurately predict the wind speed of wind farms. Since the wind speed time series is preprocessed by signal analysis method and then predicted separately and then integrated, it can reduce the influence of wind speed non-stationarity on the p...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/12
CPCG06Q10/04G06N3/126G06Q50/06
Inventor 张妍韩璞王东风
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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