Prediction Method of Tidal Velocity Based on Fractal Theory and Improved Least Squares Support Vector Machine

Abstract

The purpose of the present invention is to address the shortcomings of the existing tidal flow velocity research, and propose a tidal velocity prediction method based on fractal theory and improved least squares support vector machine, which is characterized by intermittent and uncontrollable tidal energy The randomness of the output power of the generator is caused by the main characteristics of the randomness. The tidal current time series is a nonlinear system with fractal characteristics. Taking historical data as the research object, based on the fractal theory, the Hurst exponent and the R / S analysis method of the time series are calculated. The V statistic evaluates the stability and autocorrelation of the tidal flow velocity, and based on the parameters of the least squares support vector machine optimized by the improved dragonfly algorithm, a tidal flow prediction model is established to predict the tidal flow velocity. The invention can effectively judge the autocorrelation of the tidal current velocity, provide a theoretical basis for the prediction of the tidal current velocity, and effectively improve the prediction accuracy and operation efficiency through the improved prediction model.

Description

Technical field [0001] The present invention relates to a short-term tidal flow rate prediction method for the minimum multiplier support vector for fractal theory and dragonfly algorithm, which belongs to the field of tidal energy flow rate prediction. Background technique [0002] Tidal energy is a renewable clean energy. In recent years, my country has been developed in China, but renewable energy is generally intermittent, uncontrollable, and diversification of topology, and the tidal energy also exists. . Since the tidal flow rate of the tidal flow of the tide flow is small, there is a randomness of the generator power output. Accurate tidal power predictions can provide important guarantees for power scheduling, reliability assessment of power systems, and effectively reduce the impact of tidal energy on the grid. Due to the direct determination of the tidal energy and tidal flow rate, power prediction can be implemented on the basis of tidal flow rate prediction, so accura...

AI Technical Summary

Problems solved by technology

In the process of LSSVM predictive modeling, the parameters that affect the accuracy of the predictive model are mainly the regularization parameter and the width of the kernel function, but these two parameters are generally selected by trial and error, which causes troublesome prediction and large errors, so some scholars propose to use Particle swarm algorithm, genetic algorithm, ant colony algorithm, etc. optimize the least squares support vector machine, but the above-mentioned intelligent algorithm will have slow modeling speed, easy to fall into local optimum, low prediction efficiency and other problems

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