基于bat算法优化的多输出最小二乘支持向量回归的风力天气预报

Dingcheng Wang, Yiyi Lu, Beijing Chen, Youzhi Zhao
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引用次数: 2

摘要

风能作为一种清洁能源,分布广泛,得到了广泛研究。与其他方法相比,支持向量机算法更具逻辑性。最小二乘支持向量机可以提高训练效率。因此,本文采用多输出最小二乘支持向量回归的方法对风速和风向进行预测。该算法结构简单,易于理解。该方法已应用于求解MSVR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问题。建立了模拟模型,采用不同的算法预测风速和风向值。仿真结果表明,基于蝙蝠优化算法的多输出最小二乘支持向量机预测模型具有较好的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm
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.
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