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发明公开
CN103942621A 采用复合数据源基于Sigmoid核函数支持向量机的风电功率短期预测方法 无效 - 驳回

采用复合数据源基于Sigmoid核函数支持向量机的风电功率短期预测方法
摘要：
本发明公开了采用复合数据源基于Sigmoid核函数支持向量机的风电功率短期预测方法，主要包括：采用基于Sigmoid核函数支持向量机的复合数据源，对待测风电功率进行模型训练；基于待测风电功率的模型训练结果，对待测风电功率进行预测。本发明所述采用复合数据源基于Sigmoid核函数支持向量机的风电功率短期预测方法，可以克服现有技术中预测精度低的缺陷，以实现高精度的风电功率短期预测的优点。
摘要（英）：

The invention discloses a wind power short-term prediction method using a composite data source based on a Sigmoid kernel function support vector machine. The method mainly comprises the steps that the composite data source based on the Sigmoid kernel function support vector machine is adopted, and model training is performed on wind power to be detected; the wind power to be detected is predicted on the basis of the a model training result of the wind power to be detected. The wind power short-term prediction method using the composite data source based on the Sigmoid kernel function support vector machine has the advantages of being capable of overcoming the defect that prediction accuracy is low in the prior art, and achieving high-accuracy short-term prediction of the wind power.

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