Nuclear extreme learning machine quantile regression-based wind power interval prediction method

Abstract

The invention discloses a nuclear extreme learning machine quantile regression-based wind power interval prediction method. The method comprises the steps that a wind power plant output power and windspeed data is collected; the data is processed simply, and unreasonable data is deleted; a nuclear extreme learning machine quantile regression model is built; by means of a particle swarm algorithm,nuclear extreme learning machine quantile regression parameters are optimized, and a regression module is determined; test data is put, and a wind power prediction interval is obtained. Accordingly,the nuclear extreme learning machine quantile regression principle and a nuclear extreme learning machine model are effectively combined, and the optimal model parameter is obtained by conducting search and optimization through the particle swarm algorithm, uncertain information in wind power can be effectively grasped, then a better prediction result is obtained, and the basis can be provided forsafe and stable running of wind power integration.

Description

technical field [0001] The invention belongs to the technical field of wind power prediction, and in particular relates to a wind power interval prediction method based on quantile regression of nuclear extreme learning machine. Background technique [0002] As a non-polluting and renewable new energy, wind energy has been widely used. However, due to the strong randomness and volatility of wind, as the proportion of wind power in the grid continues to increase, the randomness and fluctuation of wind power itself The stability will lead to fluctuations in the power grid, which is not conducive to the safe and stable operation of the power grid. For the safe and stable operation of the power grid, we need to make accurate predictions of wind power. Accurate predictions of wind power generation can reduce the dispatching pressure of the power grid and allow the power grid to accept more wind power. The traditional point forecasting method is easily affected by factors such as...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the disadvantages of low reliability of existing prediction methods, complex calculations, and difficult determination of parameters, the present invention proposes a wind power interval prediction method based on quantile regression of nuclear extreme learning machine, which includes the following steps:

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