Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2653-2677.DOI: 10.3778/j.issn.1673-9418.2205028

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Wind Power Output Power Forecasting Technology

WU Yuhao1,2, WANG Yongsheng1,2,+(), XU Hao1,2, CHEN Zhen1,2, ZHANG Zhe1,2, GUAN Shijie1,2   

  1. 1. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
    2. Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service, Hohhot 010080, China
  • Received:2022-05-07 Revised:2022-08-08 Online:2022-12-01 Published:2022-08-12
  • About author:WU Yuhao, born in 1999, M.S. candidate. His research interests include cloud computing and big data analysis, etc.
    WANG Yongsheng, born in 1976, Ph.D., associate professor, senior engineer, M.S. supervisor. His research interests include cloud computing and big data analysis, new energy application, etc.
    XU Hao, born in 1998, M.S. candidate. His research interests include cloud computing and big data analysis, etc.
    CHEN Zhen, born in 1994, M.S. candidate. His research interests include cloud computing and big data analysis, etc.
    ZHANG Zhe, born in 1998, M.S. candidate. His research interests include cloud computing and big data analysis, etc.
    GUAN Shijie, born in 1999, M.S. candidate. His research interests include cloud computing and big data analysis, etc.
  • Supported by:
    Natural Science Foundation of Inner Mongolia Autonomous Region(2021LHMS06001);Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region(NJZY21321);Key Technologies Research and Development Program of Inner Mongolia Autonomous Region(2020GG0094)

风电输出功率预测技术研究综述

武煜昊1,2, 王永生1,2,+(), 徐昊1,2, 陈振1,2, 张哲1,2, 关世杰1,2   

  1. 1.内蒙古工业大学 数据科学与应用学院,呼和浩特 010080
    2.内蒙古自治区基于大数据的软件服务工程技术研究中心,呼和浩特 010080
  • 通讯作者: +E-mail: wangys@imut.edu.cn
  • 作者简介:武煜昊(1999—),男,河北邢台人,硕士研究生,主要研究方向为云计算与大数据分析等。
    王永生(1976—),男,内蒙古呼和浩特人,博士,副教授,高级工程师,硕士生导师,主要研究方向为云计算与大数据分析、新能源应用等。
    徐昊(1998—),男,江苏南京人,硕士研究生,主要研究方向为云计算与大数据分析等。
    陈振(1994—),男,河南商丘人,硕士研究生,主要研究方向为云计算与大数据分析等。
    张哲(1998—),男,河南郑州人,硕士研究生,主要研究方向为云计算与大数据分析等。
    关世杰(1999—),男,河北张家口人,硕士研究生,主要研究方向为云计算与大数据分析等。
  • 基金资助:
    内蒙古自治区自然科学基金(2021LHMS06001);内蒙古自治区高等学校科学研究项目(NJZY21321);内蒙古自治区科技重大专项项目(2020GG0094)

Abstract:

Uncertainty and volatility of wind power generation, bring some serious challenges for the grid-connected wind power system. Prediction of wind power in advance is an important way to solve the above problems. Due to the existence of uncontrollable factors such as sensor transmission and network communication, the data collected for wind power prediction have abnormal values and missing values. Therefore, corresponding outlier detection and missing value interpolation operations should be performed before wind power prediction. To further promote the development of wind power data cleaning and prediction technology, current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from time series data, this paper first classifies, analyzes and summarizes the research status of outlier detection methods in the field of wind power prediction, summarizes the deficiencies and defects of existing anomaly detection methods, and prospects the research directions that may become the focus in the future development. Secondly, the evaluation indices of the existing missing value treatment methods are described. According to the different treatment methods, the processing techniques are analyzed and summarized according to the conventional treatment methods, discriminative interpolation methods, generative interpolation methods and physical characteristics methods, and the existing problems in the existing research are analyzed. Finally, the current research status of forecasting methods, multi-level forecasting and adaptive forecasting systems in existing research are analyzed and summarized, and the existing challenges and future development directions of existing forecasting are summarized and prospected.

Key words: deep learning, wind power forecasting, outlier detection, missing value imputation, time series data

摘要:

风电具有的波动性、间歇性等特点对并网造成一定程度的影响,提前进行风电功率预测是解决上述问题的一个重要途径。但传感器传输、网络通信等不可控因素的存在,导致采集到用于风电功率预测的数据存在异常值和缺失值,因此在进行风电功率预测前应当进行相应的异常值检测和缺失值插补操作。为进一步促进风电数据清洗及预测技术的发展,对当前现有模型及方法进行分析与总结,并对现有技术进行划分、对比。从时序数据出发,首先,对风电预测领域的异常值检测方法的研究现状进行分类、分析与总结,对现有异常检测方法所存不足与缺陷进行概述,并对未来发展中或将成为重点的研究方向进行展望;其次,将现有的缺失值处理方法的评价指标进行描述,根据处理方式的不同将处理技术按照常规处理方法、辨别式的插补方法、生成式的插补方法及物理特性方法进行分析与总结,并对现有研究中所存问题进行分析;最后,对现有研究中的预测方法、多层级预测及自适应预测系统的研究现状进行分析总结,并对现有预测存在的挑战及未来发展方向进行了总结与展望。

关键词: 深度学习, 风电功率预测, 异常值检测, 缺失值插补, 时间序列数据

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