Method for predicting fluctuating wind speed based on optimization-based multiple kernel LSSVM (Least Square Support Vector Machine)

Abstract

The invention provides a method for predicting fluctuating wind speed based on an optimization-based multiple kernel LSSVM (Least Square Support Vector Machine). The method comprises the following steps of utilizing an AR (Auto-Regressive) model to simulate and generate fluctuating wind speed samples of vertical spatial points, dividing the fluctuating wind speed sample of each spatial point into two parts, namely a training set and a test set, and carrying out normalization processing on the two parts respectively; establishing a multiple kernel LSSVM model; converting a fluctuating wind speed training sample into a kernel function matrix by utilizing a multiple kernel function optimized by a PSO (Particle Swam Optimization), and mapping to a high-dimensional characteristic space; obtaining a nonlinear model of the fluctuating wind speed training sample and predicting a fluctuating wind speed test sample by utilizing the model; and comparing the test sample with a predicted fluctuating wind speed result, and computing an average error of the predicated wind speed and the actual wind speed, a root-mean-square error and a correlation coefficient. According to the method, the precision for prediction of the fluctuating wind speed is guaranteed, and a choice of two new kernel functions with higher precision and stability is provided for LSSVM machine learning.

Description

technical field [0001] The invention relates to a single-point pulsating wind speed prediction method using a least squares support vector machine combined and optimized with existing kernel functions Lin_kernel, RBF_kernel, and Poly_kernel, specifically a method for predicting pulsating wind speed based on an optimized multi-core LSSVM. Background technique [0002] The excellent learning performance of support vector machines, especially for small sample problems, has always been a research hotspot in machine learning and data mining algorithms. The kernel function is very important in the support vector machine. Its introduction greatly improves the nonlinear processing ability of the learning machine, maintains the intrinsic linearity of the learning machine in the high-dimensional space, and makes the learning process easy to control. Obviously, the performance of SVM depends largely on the quality of the kernel function, so most of the research on SVM in recent years h...

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Problems solved by technology

[0004] In terms of fluctuating wind speed measurement and wind tunnel tests, the actual measurement of wind speed samples not only requires the arrangement of measuring devices, but also increases the cost, while the traditional numerical simulation technology needs to simulate through various wind speed simulation points, which is also very time-consuming

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