LSSVM (Least Square Support Vector Machine) pulsation wind speed prediction method based on Morlet wavelet kernel

Abstract

The invention provides an LSSVM (Least Square Support Vector Machine) pulsation wind speed prediction method based on a Morlet wavelet kernel. The prediction method comprises the following steps: utilizing an ARMA (Auto-Regressive and Moving Average) model to simulate and generate a vertical spatial point pulsation wind speed sample, dividing the pulsation wind speed sample of each spatial point into two parts including a training set and a test set, and carrying out normalization processing on the two parts; establishing an LSSVM model of the Morlet wavelet kernel; utilizing a Morlet wavelet kernel model subjected to PSO (Particle Swarm Optimization) to transform a pulsation wind speed training sample into a kernel function matrix, and mapping the kernel function matrix into a high-dimensional characteristic space; obtaining a nonlinear model of the pulsation wind speed training sample, and utilizing the model to predict the pulsation wind speed training sample; and comparing the wind sped of the test sample with a predicated pulsation wind speed, and calculating an average error, a root-mean-square error and a relevant coefficient of predicted wind speed and practical wind speed. The accuracy of pulsation wind speed prediction is guaranteed, and new wavelet kernel function selection with high precision and stability is provided.

Description

technical field [0001] The invention relates to a single-point fluctuating wind speed prediction method of a least squares support vector machine using a Morlet wavelet function to construct a wavelet kernel, specifically an LSSVM fluctuating wind speed prediction method based on a Morlet wavelet kernel. 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 has focused on the research of SVM ...

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Problems solved by technology

But the scale parameter in multi-scale analysis, when the selected scale parameter is large, will cause the complexity of the algorithm and affect the execution efficiency of the algorithm; on the contrary, if the scale parameter is small, it will inevitably affect the effect of multi-scale analysis, and then affect Algorithm performance

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