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首页> 外文期刊>International Journal of Embedded Systems >Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm
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Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

机译:基于多输出最小二乘支持的蝙蝠算法优化的风雨天气预报

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摘要

As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.
机译:作为一种清洁能源,风能被广泛传播并已被广泛研究。 与其他方法相比,支持向量机算法更逻辑。 最小二乘支持向量机可以提高训练效率。 因此,使用多输出最小二乘支持向量回归的方法来预测本文的风速和风向。 BAT算法结构简单,易于理解。 它已被应用于解决MSVR的优化问题。 与单输出支持向量机相比,多输出支持向量机易于解决复杂结构的问题。 建立仿真模型来预测使用不同算法来预测风速和风向的值。 仿真结果表明,基于BAT优化算法的多输出最小二乘支持向量机预测模型具有更好的可行性和有效性。

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