Wind Power Prediction Method Based on Novel Multi-dimensional Power Trend Clustering

被引：0
|
作者：
Shi H. [1 ]
Yan J. [1 ]
Ding M. [1 ,2 ]
Gao F. [1 ]
Zhang Z. [1 ]
Li Y. [1 ]
机构：
[1] School of Electrical and Information Engineering, North Minzu University, Yinchuan
[2] State Grid Ningxia Electric Power, Yinchuan
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Classification modeling; Elman neural network; Short-term prediction; Soft fuzzy C-means clustering; Trend clustering; Wind power prediction;
D O I：
10.13336/j.1003-6520.hve.20201814
中图分类号：
学科分类号：
摘要：
In order to solve the problems that the traditional clustering algorithm is seldom used to mine and exploit the wind power sequence trend characteristics, and the performance of adjustable design for different wind farms is insufficient, a wind power prediction method based on novel multi-dimensional power trend clustering is proposed. Firstly, a novel measure-ment method for multi-dimensional power trend similarity distance is proposed, including wind power sequence fluctuation degree, fluctuation time, and the numerical dimension of fluctuation, which can mine the trend characteristics of wind power data. Secondly, the proposed strict coefficient is used to adjust the degree of participation in each dimension to adapt to the better clustering effect of different wind farm data. Finally, the proposed multi-dimensional power trend distance measurement is combined with the traditional fuzzy C-means soft clustering algorithm and Elman neural network group to build a complete prediction model. The results of the study show that the trend characteristics of power sequence is effectively mined and the prediction accuracy of wind power is improved by the proposed method. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码：430 / 438
页数：8
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