Wind power data preprocessing method, wind power forecast method and system

Abstract

The invention provides a wind power data preprocessing method used for wind power forecast, a wind power forecast method and a system. The wind power data preprocessing method comprises the steps of clustering wind power data so as to form clusters and obtain isolated points outside the clusters, and extracting a center point from each cluster; and based on the difference between the power value of the isolated points outside the clusters and the power value of the points in the clusters, screening out one isolated point or more isolated points from the isolated points outside the clusters. The wind power forecast method comprises the steps of selecting partial data from collected wind power data by the wind power data preprocessing method; and forecasting wind power information according to the selected data. According to the method, the forecast efficiency of the wind power is improved due to extraction of special points from original wind power data; and data authenticity is guaranteed in extraction of the special points, so that the forecast precision of the wind power is improved.

Description

technical field [0001] The present invention relates to the field of electric power technology, and more specifically, to a wind power data preprocessing method for wind power prediction, and a wind power prediction method and system. Background technique [0002] As a clean and renewable energy, wind energy has been paid more and more attention by countries all over the world. However, due to the intermittence and volatility of wind energy, the large-scale access of wind power will have a huge impact on the power grid, such as increasing the difficulty of real-time operation scheduling and power flow control of the power system. Wind power forecasting can reduce the impact of wind power on the grid and improve the economy and safety of grid operation. Therefore, accurate wind power forecasting is the guarantee for the healthy development of wind power generation. At present, the steps of wind power forecasting mainly include: collecting wind power data, preprocessing the c...

AI Technical Summary

Problems solved by technology

[0005] To sum up, the existing wind power data preprocessing methods usually ignore or filter out certain abnormal points, which affects the authenticity of wind power data, or the method itself is random, resulting in low prediction accuracy of wind power

In addition, some wind power data preprocessing methods will lead to low forecasting efficiency

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