Wind speed section prediction method and system based on artificial neural network

A technology of artificial neural network and prediction method, which is applied in the field of wind speed prediction, and can solve problems such as the definition of hyperparameters to reduce the effect of interval prediction

Active Publication Date: 2018-05-11
HUAZHONG UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

Improper definition of hyperparameters can greatly reduce the effectiveness of interval prediction

Method used

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  • Wind speed section prediction method and system based on artificial neural network
  • Wind speed section prediction method and system based on artificial neural network
  • Wind speed section prediction method and system based on artificial neural network

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Embodiment

[0206] In order to illustrate the effect of the present invention, the real-time wind speed sequence of 3 wind fields is used as the implementation object of the present invention below. Since the steps and parameters implemented for each wind field are the same, this wind field one is further described below:

[0207] Step 1: Collect the wind speed sequence of wind field 1, collect the real-time wind speed every m minutes, m is taken as 15, and the first len=1008 data of each wind field is taken.

[0208]Step 2: Perform VMD decomposition on the unstable nonlinear wind speed sequence to obtain K sub-time series u with simple structure k (t), k is from 1 to K, and K is 5. This method is based on some relatively mature concepts: such as Wiener filtering, one-dimensional Hilbert transform and signal analysis, heterodyne demodulation, etc. VMD decomposes the input signal into a certain number of sub-signals, so that it reproduces the input while having specific sparsity here Assu...

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Abstract

The invention discloses a wind speed section prediction method and system based on an artificial neural network, and the method and system are used for the short-time wind speed prediction of a wind field. The method comprises the steps: decomposing an initial complex time sequence into a plurality of time sequences in a simple structure through VMD (variational mode decomposition); carrying out the feature selection through GSO (Gram-Schmidt orthogonal); taking a processed wind speed sequence as the input of the ANN (artificial neural network), wherein the output of the ANN is the upper and lower bounds of wind speed at a future moment; finally training the weight and bias of the ANN through an MOGSA (Multi objective gravitational search algorithm), taking two contradictory indexes (coverage rate and section width) as an optimization target, and obtaining an optimal scheme set. The wind speed section predicted through the method has a higher coverage rate for the actual wind speed section, and is small in section width. The above combined model enables the prediction accuracy to be improved to a very high level.

Description

technical field [0001] The invention belongs to the technical field of wind speed prediction, and more specifically relates to a method and system for wind speed interval prediction based on artificial neural network. Background technique [0002] With the increasing demand for energy and the impact of global warming, countries around the world are actively seeking alternative clean energy. Wind power is favored by people because of its cleanness and wide distribution. However, due to the intermittent and uncontrollable nature of wind energy, the randomness of wind power output is bound to bring serious challenges to the safe and reliable operation of the power system. The wind farm output directly depends on the wind speed, and wind speed prediction is the basis for wind turbine control and wind farm output prediction. The point prediction results cannot represent the potential randomness in the actual wind power, which makes the decision-making work face certain risks. I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/08G06Q10/04G06Q50/06
Inventor 李超顺陈新彪邹雯赖昕杰陈昊
Owner HUAZHONG UNIV OF SCI & TECH
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