Method for simulating fluctuating wind speeds on basis of data drive

A pulsating wind speed, data-driven technology, applied in the direction of electrical digital data processing, special data processing applications, biological neural network models, etc., can solve the problems of time-consuming, low simulation accuracy, etc.

Inactive Publication Date: 2015-09-09
SHANGHAI UNIV
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a data-driven pulsating wind speed data-driven simulation method based on the defects of the prior art, so as to solve the problems of low simulation accuracy and time-consuming of traditional support vector machines or parameter optimization methods

Method used

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  • Method for simulating fluctuating wind speeds on basis of data drive
  • Method for simulating fluctuating wind speeds on basis of data drive
  • Method for simulating fluctuating wind speeds on basis of data drive

Examples

Experimental program
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Embodiment 1

[0055] see figure 1 , the process steps of this data-driven fluctuating wind speed simulation method are as follows:

[0056] 1) Select a super high-rise building and determine the parameters required for numerical simulation of fluctuating wind speed: the simulated building height and the heights of the simulated wind speed points, the average wind speed at a height of 10 meters, surface roughness coefficient, ground roughness index, simulation correlation function;

[0057] 2) A set number of fluctuating wind speed time histories uniformly distributed along the height generated by the numerical simulation of the AR method are used as limited original fluctuating wind speed sample data; and the simulated values ​​of the wind speed power spectral density, autocorrelation function and cross The coincidence degree of the target value is tested to verify the feasibility of simulating the wind speed time history of super high-rise buildings based on the AR model;

[0058] 3) The...

Embodiment 2

[0061] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0062] The AR model is represented by the following formula:

[0063] v ( t ) = - Σ k = 1 p ψ k · v ( t - kΔt ) + N ( t ) - - - ( 1 )

[0064] In the formula: v(t) and v(t-kΔt) are the fluctuating wind speed time history vectors of M points in space at time t and t-kΔt respectively; p is the order of the AR model; Δt is the time step of the simulated wind speed long; k is the AR model autoregressive coefficient matrix, which is a...

Embodiment 3

[0067] Based on the PSO optimized LS-SVM data-driven fluctuating wind speed simulation method, the specific steps are as follows:

[0068] The first step is to select a super high-rise building with a height of 200 meters in a city center, and take points every 10 meters along the height direction as the simulated wind speed points. Other relevant parameters are shown in Table 1:

[0069] Table 1 Related simulation parameters

[0070]

[0071] Indicates the average wind speed at a height of 10m.

[0072]In the second step, a certain number of fluctuating wind speed time histories uniformly distributed along the height generated by numerical simulation of the AR method are used as limited original fluctuating wind speed sample data. The simulated power spectrum adopts Davenport spectrum, and only considers the spatial correlation in the height direction. The correlation function is: C x =C y =0,C z =10. The 4th-order autoregressive model order is taken, that is, p is...

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Abstract

The invention provides a method for simulating fluctuating wind speeds on the basis of data drive. The method includes numerically simulating fluctuating wind speeds of 20 simulation points of super high-rise buildings by the aid of an AR (auto-regression) process to obtain sample data; searching the optimal parameters of LS-SVM (least squares support vector machine) models by the aid of PSO (particle swarm optimization) by means of interpolating, learning and training to minimize prediction errors of the models and building the PSO-LSSVM models by the aid of the optimized parameters; predicting the fluctuating wind speeds of intermediate layers by the aid of fluctuating wind speed samples of upper and lower layers, evaluating the fluctuating wind speeds of the intermediate layers by the aid of evaluation indexes which are average errors, root-mean-square errors and relevant coefficients, and comparing the fluctuating wind speeds of the intermediate layers to BP (back propagation) neural networks and results obtained by the aid of standard SVM data drive technologies.

Description

technical field [0001] The invention relates to a data-driven fluctuating wind velocity simulation method, which is characterized in that the fluctuating wind velocity time history method is simulated based on the particle swarm method PSO optimization least squares support vector machine LS-SVM data-driven technology. Background technique [0002] The standard support vector machine (SVM) method for function fitting is mainly to convert the input samples from the low-dimensional input space to a high-dimensional feature space through nonlinear mapping, and then minimize the loss function in this high-dimensional space to obtain Linear fit function. According to Mercer's theorem, for support vector machines, the function regression fitting problem can be described as solving a constrained quadratic programming problem, the number of constraints is equal to the capacity of the sample, although the related kernel function is used to avoid the explicit solution of high-dimensio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/02
Inventor 王月丹李春祥迟恩楠
Owner SHANGHAI UNIV
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