Fluctuation wind speed prediction method based on extreme learning machine

Abstract

The present invention provides a fluctuation wind speed prediction method based on an extreme learning machine. The method comprises the following steps of: step 1: using an ARMA model to perform simulation to generate fluctuation wind speed samples of vertical spatial points, and dividing the fluctuation wind speed sample of each spatial point into two parts of a training set and a test set; step 2: giving training samples, setting a Gaussian kernel function as a kernel function, and calculating a kernel function matrix K of the training samples; step 3: establishing a limit learning machine algorithm model of the kernel function; and step 4: comparing test samples with results of prediction of fluctuation wind speed by KELM, calculating a mean absolute error, a root-mean-square error and a correlated coefficient of a predicted wind speed and an actual wind speed, and evaluating the effectiveness of the method. The invention provides a prediction method for a complete wind speed time-history curve needed for wind resistance design, thereby reducing a great deal of time costs.

Description

technical field [0001] The invention relates to a single-hidden-layer feed-forward neural network learning algorithm using kernel function mapping to predict fluctuating wind speed at a single point, specifically a method for predicting fluctuating wind speed based on a kernel extreme learning machine. Background technique [0002] When studying wind loads, the wind is usually treated as an average wind speed that does not change with time within a certain time interval and a fluctuating wind speed that varies randomly with time. The average wind speed produces a static response of the structure, while the fluctuating wind speed produces a dynamic response. When the wind acts on a high-rise structure, its positive and negative wind pressure will form a wind load on the structure. At the same time, the flow around the blunt body will also cause structural buffeting, lateral vibration and torsional vibration caused by vortex shedding. Buffeting and chattering under extreme win...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a pulsating wind speed prediction method based on a nuclear extreme learning machine, which solves the problems of difficulty in setting parameters of the traditional data-driven technology prediction model, model instability, etc.

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