基于VMD和IBA-LSSVM的短期风电功率预测

为了提高风电功率预测精度，提出了一种基于变分模态分解(VMD)和改进的最小二乘支持向量机(LSSVM)的短期风力发电功率预测新模型。利用VMD将功率历史数据分解成趋势分量、细节分量和随机分量以降低原始数据的复杂性和不平稳性，然后建立IBA-LSSVM预测模型，利用改进蝙蝠算法(IBA)对最小二乘向量机的参数进行优化，并分别对各个子模态进行预测，叠加子模态的预测结果以得到最终的发电功率预测值。对宁夏某风电厂功率预测结果证明了该模型的有效性，通过不同预测模型的对比验证了模型具有较高的预测精度。

Short term prediction of wind power based on VMD and IBA-LSSVM

In order to improve the accuracy of wind power prediction, a new short-term wind power prediction method based on the variational mode decomposition (VMD) and the improved least squares support vector machine (LSSVM) is proposed. Firstly, VMD is used to decompose the historical power data into the trend component, detail component and random component to reduce the complexity and instability of original data. Then the IBA-LSSVM prediction model is established, the parameters of the least squares vector machine are optimized by the improved bat algorithm (IBA), and each sub mode is predicted separately. The final predicted wind power is obtained by superimposing the prediction results of sub modes. Finally, the power of a wind power plant in Ningxia is predicted by the proposed prediction method. The effectiveness of the model is proved by the error analysis of the prediction results. The comparison of different prediction methods verifies that the proposed model has higher prediction accuracy.