Method for predicting wind speed and power of wind farm based on wavelet decomposition and support vector machine

A technology of support vector machine and wavelet decomposition, applied in forecasting, instrumentation, biological neural network model, etc., can solve the problems of reducing the accuracy of wind power forecasting, poor model coordination, complex and repeated forecasting steps, etc.

Active Publication Date: 2015-12-23
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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Problems solved by technology

Existing wind power forecasting mostly uses a single model or a simple combination of multiple models to predict the wind power sequence. The models do not cooperate well, and the forecasting steps are complicated and repeated.
Moreover, most of the e

Method used

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  • Method for predicting wind speed and power of wind farm based on wavelet decomposition and support vector machine
  • Method for predicting wind speed and power of wind farm based on wavelet decomposition and support vector machine
  • Method for predicting wind speed and power of wind farm based on wavelet decomposition and support vector machine

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Embodiment 1

[0059] Example 1: Take the short-term prediction of wind speed and power of Shandong Runhai wind farm based on wavelet decomposition and gray support vector machine as an example, such as figure 1 shown, including the following steps:

[0060] Step (1): Collect historical wind speed and power data of the entire wind farm for 150 consecutive days, with a sampling interval of 15 minutes, remove unreasonable data, and obtain the historical wind speed time series W 0 ={w(t-n),w(t-n+1),w(t-n+2),…,w(t)} and historical power time series P 0 ={p(t-n),p(t-n+1),p(t-n+2),...,p(t)}, n=14400; take the time series of the first 140 days as historical data, after taking 10-day time series as test data;

[0061] Step (2): Use multi-wavelet packet decomposition technology to decompose the historical wind speed time series respectively, and obtain the low-frequency component, mid-frequency component and high-frequency component of the historical wind speed time series;

[0062] The multi-wave...

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Abstract

The invention discloses a method for predicting wind speed and power of a wind farm based on wavelet decomposition and a support vector machine. The method comprises: acquiring wind speed and power historical data of a whole wind farm in a preset time, to obtain a historical wind speed time sequence and a historical power time sequence of the wind farm; using a wavelet packet decomposition technology to perform wavelet packet decomposition on the historical wind speed time sequence, to obtain a low-frequency stage component, a middle-frequency stage component, and a high -frequency stage component of the historical wind speed time sequence; using a grey support vector machine prediction model to predict each component of the historical wind speed time sequence, and then using wavelet packet reconstruction to obtain short-period wind speed prediction data; using historical wind electricity power data and numerical weather prediction wind speed data as a training set to establish a grey support vector machine model, predicting wind electricity power; predicting the obtained wind speed prediction data and the wind electricity power prediction data through a RBF neural network, to obtain a final prediction value of the wind electricity power.

Description

technical field [0001] The invention relates to the technical field of new energy power generation, in particular to a method for predicting wind speed and power of a wind farm based on wavelet decomposition and support vector machines. Background technique [0002] The development and utilization of renewable energy, especially wind energy, has been highly valued by countries all over the world. Wind power is currently the most mature technology and the most promising renewable energy for large-scale development. Due to the strong randomness of wind power generation, the accuracy of wind farm power forecasting is not satisfactory. The development of wind power forecasting systems is relatively seldom, and there is a lack of mature practical experience. [0003] The current research and application of wind speed or power generation forecasting in wind farms can be divided into short-term wind power forecasting and ultra-short-term wind power forecasting in terms of forecast...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
Inventor 王瑞琪孙树敏汪东军牛蔚然吕雯张用赵鹏于芃李广磊毛庆波
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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