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Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

机译：基于LMD和改进的FA优化组合核函数LSSVM的短期风速预测

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摘要

Accurate prediction of wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A new prediction method for short-term wind speed based on local mean decomposition (LMD) and combined kernel function least squares support vector machine (LSSVM) is proposed. The short-term wind speed time series is decomposed into some components by the LMD algorithm. Based on LSSVM, radial basis function and the Polynomial function are used to generate the combined kernel function. The combined kernel function LSSVM combines the advantages of the radial basis function and the Polynomial function, which can achieve better prediction accuracy. The decomposed wind speed time series are predicted separately by the combined kernel function LSSVM model. At the same time, an improved firefly algorithm is proposed to optimize the parameters of the combined kernel function LSSVM. The final predictive value can be obtained by superimposing the predicted value of each combined kernel function LSSVM prediction model. The actual collected short-term wind speed data is chosen as the research object, the simulation experiments with four prediction horizons have been implemented. Compared with state-of-the-art prediction methods, through the comparison result curve between the prediction and actual wind speed, the box-plot results of predictive error distribution, the comparison results of the relative prediction error, the performance indicators, the Pearson's test, the DM test and the Taylor diagram results show that the proposed prediction method has higher prediction accuracy and is able to reflect the laws of wind speed correctly. Furthermore, the simulation results of four new datasets and adding noise to the input data of training set show that the proposed prediction method has strong robustness.
机译：准确预测风速对风电场的运行和维护，涡轮机的优化调度以及电网的安全稳定运行具有重要意义。提出了一种基于局部均值分解（LMD）和核函数最小二乘支持向量机（LSSVM）的短期风速预测新方法。 LMD算法将短期风速时间序列分解为一些分量。基于LSSVM，使用径向基函数和多项式函数生成组合核函数。组合核函数LSSVM结合了径向基函数和多项式函数的优点，可以实现更好的预测精度。分解后的风速时间序列由组合核函数LSSVM模型分别预测。同时，提出了一种改进的萤火虫算法，以优化组合核函数LSSVM的参数。可以通过叠加每个组合核函数LSSVM预测模型的预测值来获得最终预测值。以实际采集的短期风速数据为研究对象，实现了具有四个预测水平的模拟实验。与最新的预测方法相比，通过预测和实际风速之间的比较结果曲线，预测误差分布的箱形图结果，相对预测误差的比较结果，性能指标，皮尔逊方程测试，DM测试和泰勒图结果表明，所提出的预测方法具有较高的预测精度，能够正确反映风速规律。此外，对四个新数据集的仿真结果以及在训练集输入数据中添加噪声的结果表明，该预测方法具有较强的鲁棒性。

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