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Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

机译：使用布谷鸟优化算法优化的支持向量回归进行短期风速预测

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摘要

This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
机译：本文开发了一种有效的智能模型来预测短期风速序列。提出了一种基于递归图（RP）和优化支持向量回归（SVR）的混合预测技术。气象系统相互作用引起的风使其自身非常不稳定并且难以预测。为了了解风力系统，使用RP分析了风速序列。然后，利用SVR模型预测风速，其中输入变量由RP选择，并且两个关键参数，包括核函数RBF的惩罚因子和伽马，通过各种优化算法进行了优化。这些优化算法是遗传算法（GA），粒子群优化算法（PSO）和布谷鸟优化算法（COA）。最后，基于一些标准和假设检验，评估了优化的SVR模型，包括COA-SVR，PSO-SVR和GA-SVR。实验结果表明：（1）RP的分析表明风速在短期范围内具有短期可预测性；（2）COA-SVR模型的性能优于PSO-SVR和GA -SVR方法，尤其是用于跳跃采样的方法；（3）COA-SVR方法在多步提前预测中具有统计上的鲁棒性，可应用于实际的风电场应用中。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|619178.1-619178.13|共13页
  • 作者

    Wang Jianzhou; Zhou Qingping; Jiang Haiyan; Hou Ru;

  • 作者单位

    Dongbei Univ Finance & Econ, Sch Stat, Dalian 116025, Peoples R China.;

    Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China.;

    Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China.;

    Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China.;

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