Wind power range short-term prediction method based on variation mode decomposition and relevant vector machine

Abstract

The invention discloses a wind power range short-term prediction method based on variation mode decomposition and a relevant vector machine. The method comprises: variation mode decomposition is carried out on a wind power sequence to obtain a plurality of components having different center frequencies; all components are processed by using a relevant vector machine algorithm to establish range prediction models respectively; and prediction results of all components are superposed to obtain a general range prediction result under a certain confidence level. With the method, the prediction precision and range coverage rate of the model are improved; and the range width is narrowed obviously, so that the short-term prediction result of the wind power range is improved obviously.

Description

technical field

[0001] The invention belongs to the technical field of new energy power generation and smart grid, and relates to a short-term wind power interval prediction method based on variational mode decomposition and correlation vector machine. Background technique

[0002] In today's shortage of fossil energy and severe environmental problems, the development and utilization of clean and pollution-free renewable energy has become a consensus. Among them, wind power generation has received more and more attention and attention because of its cleanness, pollution-free, abundant reserves, and recyclable utilization. Due to the randomness and volatility of natural wind, when wind power is connected to the grid on a large scale, the supply-demand balance and safe and stable operation of the grid will have a huge impact when large power fluctuations occur in wind turbines. Therefore, accurate wind power forecasting is the prerequisite for rationally formulating power gen...

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