基于神经网络平均影响值的超短期风电功率预测
发布时间：2018-08-05 10:19
【摘要】：针对动态神经网络风电功率预测模型输入变量较多、模型复杂的问题,将神经网络和平均影响值方法相结合,提出了一种基于神经网络平均影响值的超短期风电功率预测方法。此方法综合考虑了各输入变量对输出变量(风电预测功率)的外部贡献率和内部贡献率,筛选出了对输出变量贡献率最大的输入变量,建立了一个优化的神经网络超短期风电功率预测模型。实验结果表明,所提模型降低了预测模型的复杂度,减少了测量噪声对预测精度的影响,得到了较好的风电功率预测结果。
[Abstract]:In order to solve the problem that the wind power prediction model of dynamic neural network has many input variables and complex model, a super-short-term wind power prediction method based on the average influence value of neural network is proposed by combining the neural network with the average influence value method. In this method, the external and internal contribution rates of the input variables to the output variables (wind power prediction power) are considered synthetically, and the input variables with the largest contribution to the output variables are selected. An optimized neural network model for predicting ultra-short-term wind power is established. The experimental results show that the proposed model reduces the complexity of the prediction model and reduces the influence of measurement noise on the prediction accuracy.
【作者单位】： 中国能源建设集团广东省电力设计研究院有限公司;南瑞集团公司(国网电力科学研究院);国电南瑞南京控制系统有限公司;
【基金】：国家高技术研究发展计划(863计划)资助项目(2013AA050601)~~
【分类号】：TM614;TP183
[Abstract]:In order to solve the problem that the wind power prediction model of dynamic neural network has many input variables and complex model, a super-short-term wind power prediction method based on the average influence value of neural network is proposed by combining the neural network with the average influence value method. In this method, the external and internal contribution rates of the input variables to the output variables (wind power prediction power) are considered synthetically, and the input variables with the largest contribution to the output variables are selected. An optimized neural network model for predicting ultra-short-term wind power is established. The experimental results show that the proposed model reduces the complexity of the prediction model and reduces the influence of measurement noise on the prediction accuracy.
【作者单位】： 中国能源建设集团广东省电力设计研究院有限公司;南瑞集团公司(国网电力科学研究院);国电南瑞南京控制系统有限公司;
【基金】：国家高技术研究发展计划(863计划)资助项目(2013AA050601)~~
【分类号】：TM614;TP183
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