郝颖, 车建峰, 冬雷, 王丽婕, 战文华, 郭洪武. 一种基于邻域保持的数值天气预报数据降维可信度评估准则[J]. 太阳能学报, 2022, 43(6): 106-114. DOI: 10.19912/j.0254-0096.tynxb.2020-0951
引用本文: 郝颖, 车建峰, 冬雷, 王丽婕, 战文华, 郭洪武. 一种基于邻域保持的数值天气预报数据降维可信度评估准则[J]. 太阳能学报, 2022, 43(6): 106-114. DOI: 10.19912/j.0254-0096.tynxb.2020-0951
Hao Ying, Che Jianfeng, Dong Lei, Wang Lijie, Zhan Wenhua, Guo Hongwu. A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 106-114. DOI: 10.19912/j.0254-0096.tynxb.2020-0951
Citation: Hao Ying, Che Jianfeng, Dong Lei, Wang Lijie, Zhan Wenhua, Guo Hongwu. A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 106-114. DOI: 10.19912/j.0254-0096.tynxb.2020-0951

一种基于邻域保持的数值天气预报数据降维可信度评估准则

A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION

  • 摘要: 针对以风电、光伏发电为代表的新能源发电功率预测中数值天气预报(NWP)数据降维问题,应用不同特征选择和特征转换算法进行降维后,提出一种基于邻域保持的NWP降维可信度评估准则。该评估准则基于数据降维投影过程中样本点邻域变化,不考虑降维算法本身的降维原理及目标函数,可对不同降维算法在NWP数据降维中的可信度进行有效评估。研究结果可为其他研究提供选取NWP降维算法提供参考。

     

    Abstract: Since the Numerical weather prediction(NWP)data in the power forecasting of renewable power generation is always multidimensional and redundant,dimension reduction of the NWP data is very necessary for data preprocessing before power forecasting. After the feature selection and feature conversion algorithm being applied for NWP dimension reduction,a general reliability assessment of NWP dimension reduction based on neighborhood preservation was proposed. This evaluation criterion is based on the neighborhood changes of sample points in the process of data dimensionality reduction projection,without considering the dimensionality reduction principle and objective function of the algorithm itself,which can effectively evaluate the reliability of different dimensionality reduction algorithms in NWP data dimensionality reduction,thus providing theoretical basis for other researchers to select NWP algorithm.

     

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