Wind power combination predicting method based on fuzzy neural network and support vector machine

Abstract

The invention provides a wind-field power combination predicting method based on a fuzzy neural network and a support vector machine, which comprises the following steps of: acquiring and pre-processing data; setting up a fuzzy neural network model by using a normalized training sample set and a prediction sample set and predicting; setting up a support vector machine model and predicting; linearly combining the prediction results of the two algorithms to obtain a wind speed prediction value; and setting up a wind speed power expert table via historical data, and inquiring the expert table according to the predicted wind speed value so as to obtain a power prediction result. By the method provided by the invention, the short-term prediction of a wind speed sequence can be effectively realized, the power prediction precision is improved, and fewer computing resources are consumed.

Description

technical field [0001] The invention relates to a data modeling and prediction method based on artificial intelligence technology, in particular to a wind power combined prediction method based on a fuzzy neural network and a support vector machine. technical background [0002] With the rapid development of wind power installed capacity, the proportion of wind power in the grid continues to increase. Since wind power is an intermittent and fluctuating energy source, large-scale wind power access has brought severe challenges to the safe and stable operation of the power system and the assurance of power quality. If the wind speed and generating power of the wind farm can be predicted more accurately, the impact of wind power on the entire power grid can be effectively reduced. The prediction of wind power will help the grid dispatching department to formulate a reasonable operation mode in time and adjust the dispatching plan accurately, so as to ensure the reliable, high-...

AI Technical Summary

Problems solved by technology

[0004] The present invention overcomes the defects of the prior art and proposes an intelligent data modeling algorithm for wind farm power generation prediction. Knowledge and experience can also realize accurate short-term prediction of wind speed series when the historical data of wind turbines is insufficient, and only consume less computing resources while improving the accuracy of power prediction

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