基于FILTERSIM算法的风力发电功率预测

赵坤; 张挺; 杜奕

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DOI:10.3969/j.issn.1006-4729.2019.02.010

上海电力大学学报:2019,35(2):149-152

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基于FILTERSIM算法的风力发电功率预测

赵坤¹, 张挺¹, 杜奕²

(1.上海电力学院计算机技术与科学学院, 上海 200090;2.上海第二工业大学工学部, 上海 201209)

Prediction of Wind Power Generation Based on FILTERSIM

ZHAO Kun¹, ZHANG Ting¹, DU Yi²

(1.School of Computer Technology and Science, Shanghai University of Electric Power, Shanghai 200090, China;2.School of Engineering, Shanghai Polytechnic University, Shanghai 201209, China)

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投稿时间：2018-10-01

中文摘要: 由于风力发电受天气条件的影响，所以其输出功率难以预测。将风力发电功率与气象数据依据一定的排列规则整理为三维图像，通过FILTERSIM算法对训练数据图像降维并提取其特征，然后在已知的气象数据组成的验证点集上预测风力发电功率。试验结果与验证数据的对比表明，该方法能有效预测风电的发电功率。

中文关键词: 风力发电预测 FILTERSIM 过滤器

Abstract:Due to the impact of wind power from the weather conditions, the nonlinear characteristics of the output power is difficult to accurately capture. The wind power and meteorological data are arranged into three-dimensional images according to certain arrangement rules. The training data images are reduced in dimension and their characteristics are extracted by FILTERSIM algorithm. Then the wind power is predicted on the verification point set of known meteorological data. By comparing the experiment results with the validation data, it is shown that the method can effectively predict wind power generation.

keywords: wind power generation forecast FILTERSIM filter

文章编号：20192010 中图分类号：TM9;TM6 文献标志码：

基金项目:国家自然科学基金（41672114，41702148）；上海市自然科学基金（16ZR1413200）。

作者	单位	E-mail
赵坤	上海电力学院计算机技术与科学学院, 上海 200090
张挺	上海电力学院计算机技术与科学学院, 上海 200090	tingzh@shiep.edu.cn
杜奕	上海第二工业大学工学部, 上海 201209

Author Name	Affiliation	E-mail
ZHAO Kun	School of Computer Technology and Science, Shanghai University of Electric Power, Shanghai 200090, China
ZHANG Ting	School of Computer Technology and Science, Shanghai University of Electric Power, Shanghai 200090, China	tingzh@shiep.edu.cn
DU Yi	School of Engineering, Shanghai Polytechnic University, Shanghai 201209, China

引用文本：
赵坤,张挺,杜奕.基于FILTERSIM算法的风力发电功率预测[J].上海电力大学学报,2019,35(2):149-152.
ZHAO Kun,ZHANG Ting,DU Yi.Prediction of Wind Power Generation Based on FILTERSIM[J].Journal of Shanghai University of Electric Power,2019,35(2):149-152.