• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies >A new combination model for short-term wind power prediction
【24h】

A new combination model for short-term wind power prediction

机译：短期风电预测的新组合模型

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Short-term wind power prediction is important to the dispatch and operation of power system. A prediction model based on the rough set, principal component analysis (PCA) and Elman neural network (ElmanNN) is constructed for short-term wind speed forecasting to improve the prediction accuracy of short-term wind power. The wind speed prediction model is established by using ElmanNN, and PCA is used to extract the feature of wind speed data, which optimizes the inputs of ElmanNN. Furthermore, excitation function and the structures of network are improved to search for the optimum solution to function of convergence rate and prediction accuracy. To solve large error and prediction accuracy fluctuations of the ElmanNN model at the peak value of wind speed, the rough set theory is proposed to compensate and correct the predicted values to further improve the forecasted results. Finally, the predictive value of the wind power is obtained by the power conversion. Experiment results show that the new combination model proposed in this paper has higher prediction accuracy compared to another model and has certain application value.
机译：短期风电预测对电力系统的调度和运行至关重要。建立了基于粗糙集，主成分分析（PCA）和艾尔曼神经网络（ElmanNN）的预测模型，用于短期风速预测，提高了短期风电的预测精度。利用ElmanNN建立了风速预测模型，并利用PCA提取了风速数据的特征，从而优化了ElmanNN的输入。此外，改进了激励函数和网络结构，以寻找收敛速度和预测精度函数的最佳解决方案。为解决ElmanNN模型在风速峰值时的较大误差和预测精度波动，提出了粗糙集理论对预测值进行补偿和校正，以进一步改善预测结果。最后，通过功率转换获得风能的预测值。实验结果表明，与其他模型相比，本文提出的组合模型具有更高的预测精度，具有一定的应用价值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号