分形插值在风速时间序列中的应用

doi:10.11772/j.issn.1001-9081.2020010130

计算机应用 ›› 2020, Vol. 40 ›› Issue (9): 2628-2633.DOI: 10.11772/j.issn.1001-9081.2020010130

分形插值在风速时间序列中的应用

郭秀婷^1,2, 朱昶胜¹, 张生财¹, 赵奎鹏¹

1. 兰州理工大学计算机与通信学院, 兰州 730050;
2. 兰州理工大学理学院, 兰州 730050

收稿日期:2020-02-15 修回日期:2020-04-09 出版日期:2020-09-10 发布日期:2020-04-16
通讯作者: 朱昶胜
作者简介:郭秀婷(1982-),女,山东聊城人,讲师,博士研究生,主要研究方向:数据挖掘、时间序列预测;朱昶胜(1972-),男,甘肃兰州人,教授,博士,主要研究方向:高性能计算、大数据;张生财(1984-),男,甘肃兰州人,副教授,博士研究生,主要研究方向:大数据及云计算;赵奎鹏(1992-),男,甘肃武威人,硕士研究生,主要研究方向:大数据及云计算。
基金资助:
中国博士后科学基金面上一等资助项目（2014M560371）；兰州理工大学红柳杰出人才计划项目（J201304）。

Application of fractal interpolation in wind speed time series

GUO Xiuting^1,2, ZHU Changsheng¹, ZHANG Shengcai¹, ZHAO Kuipeng¹

1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou Gansu 730050, China;
2. School of science, Lanzhou University of Technology, Lanzhou Gansu 730050, China

Received:2020-02-15 Revised:2020-04-09 Online:2020-09-10 Published:2020-04-16
Supported by:
This work is partially supported by the First-class China Postdoctoral Science Foundation (2014M560371), the Project of Rose Willow Distinguished Young Scientists of Lanzhou University of Technology (J201304).

摘要/Abstract

摘要： 针对风电场风速数据中大量连续缺失数据的插值问题，提出了一种基于自适应变异粒子群优化（PSO）的分形插值算法。首先，在粒子群优化算法中引入变异因子，增强粒子的多样性，提高算法搜索精度；其次，通过自适应变异粒子群优化算法来得到分形插值算法中垂直比例因子参数的最佳取值；最后，对两组不同趋势和变化特征的数据集进行分形插值计算分析，并把所提算法与Lagrange插值和三次样条插值方法进行对比。结果表明：分形插值不仅可以保持风速曲线的整体波动特性和局部特征，而且比传统插值方法的精度更高；在基于Dataset A的实验中，分形插值的均方根误差（RMSE）分别比Lagrange插值和三次样条插值减小了66.52%和58.57%；在基于Dataset B的实验中，分形插值的RMSE分别比Lagrange插值和三次样条插值减小了76.72%和67.33%。证明分形插值更适合连续缺失且波动强烈的风速时间序列的插值。

关键词: 风速, 时间序列, 分形插值, 垂直比例因子, 自适应变异, 粒子群优化

Abstract: A fractal interpolation algorithm based on adaptive mutation Particle Swarm Optimization (PSO) was proposed aiming at the interpolation problem of a large number of continuous missing data in wind speed data of wind farms. First, the mutation factor was introduced into the particle swarm optimization algorithm to enhance the diversity of particles and the search accuracy of the algorithm. Second, the optimal value of the vertical scaling factor in the fractal interpolation algorithm was obtained by the adaptive mutation particle swarm optimization algorithm. Finally, two datasets with different trends and change characteristics were analyzed by fractal interpolation, and the proposed algorithm was compared with Lagrange interpolation and cubic spline interpolation. The results show that fractal interpolation is not only able to maintain the overall fluctuation characteristics and local characteristics of wind speed curve, but also is more accurate than the traditional interpolation methods. In the experiment based on Dataset A, the Root Mean Square Error (RMSE) of fractal interpolation was reduced by 66.52% and 58.57% respectively compared with those of Lagrange interpolation and cubic spline interpolation. In the experiment based on Dataset B, the RMSE of fractal interpolation was decreased by 76.72% and 67.33% respectively compared with those of Lagrange interpolation and cubic spline interpolation. It is verified that fractal interpolation is more suitable for the interpolation of wind speed time series with strong fluctuation and continuous missing data.

Key words: wind speed, time series, fractal interpolation, vertical scaling factor, adaptive variation, Particle Swarm Optimization (PSO)

中图分类号:

TP301.6

郭秀婷, 朱昶胜, 张生财, 赵奎鹏. 分形插值在风速时间序列中的应用[J]. 计算机应用, 2020, 40(9): 2628-2633.

GUO Xiuting, ZHU Changsheng, ZHANG Shengcai, ZHAO Kuipeng. Application of fractal interpolation in wind speed time series[J]. Journal of Computer Applications, 2020, 40(9): 2628-2633.

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分形插值在风速时间序列中的应用

Application of fractal interpolation in wind speed time series

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