Support-vector-machine-regression-based method for predicting wind speed of wind power plant

A technology of support vector machine and wind speed prediction, which can be used in forecasting, instrumentation, data processing applications, etc., can solve the problems of slow convergence speed and low prediction accuracy, and achieve the effect of improving the convergence speed.
CN106529706AInactive Publication Date: 2017-03-22STATE GRID CORP OF CHINA +2
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
STATE GRID CORP OF CHINA
Publication Date
2017-03-22
Estimated Expiration
Not applicable · inactive patent

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Abstract

Disclosed in the invention is a support-vector-machine-regression-based method for predicting a wind speed of a wind power plant. The invention relates to a support-vector-machine-regression-based method for predicting a wind speed of a wind power plant, thereby solving problems of low prediction precision and slow convergence speed of the existing wind speed prediction method. The method comprises the following steps: step one, selecting sample data collected by a wind power plant; step two, determining a sample training set and a testing set; step three, carrying out pretreatment on the sample data; step four, selecting a support vector machine (SVM) regression kernel function and determining a to-be-optimized parameter of an SVM model; and step five, training the SVM model by using an optimal parameter and predicting a wind speed value at a future time. The method is applied to the wind power prediction field.
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Description

technical field

[0001] The invention relates to a wind speed prediction method of a wind farm based on a support vector machine regression. Background technique

[0002] The development of wind power is of great significance to improving the energy structure, protecting the ecological environment, ensuring clean energy and achieving sustainable economic development, etc., which has become the consensus of the whole world. However, the current output power of wind turbines is characterized by intermittent, non-linear, fast changing speed, and large fluctuation range. Wind power grid connection has a huge impact on power quality and power system. In order to achieve large-scale utilization of wind power, optimize power grid dispatching, and strengthen wind power market competitiveness, wind farms must carry out wind power forecasting and forecasting, and should have daily and real-time forecasting capabilities.

[0003] Therefore, accurate forecasting of wind power output, e...

Claims

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