Survey of Wind Power Output Power Forecasting Technology

doi:10.3778/j.issn.1673-9418.2205028

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. 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.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

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

CLC Number:

TP391

WU Yuhao, WANG Yongsheng, XU Hao, CHEN Zhen, ZHANG Zhe, GUAN Shijie. Survey of Wind Power Output Power Forecasting Technology[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(12): 2653-2677.

武煜昊, 王永生, 徐昊, 陈振, 张哲, 关世杰. 风电输出功率预测技术研究综述[J]. 计算机科学与探索, 2022, 16(12): 2653-2677.

Figures/Tables 13

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方法	文献	技术特点	优点	缺陷与不足
基于统计的异常值检测技术	HBOS^[12]、统计量分析法^[15]、3σ准则^[17]	假定数据服从某种分布，并根据数据不一致性判断其是否异常	简单、易懂	需提前确定数据的分布特征，应用困难
基于聚类的异常值检测技术	马氏距离与K-means结合^[20]、DPC^[21]、自适应DBSCAN^[22]	使用聚类算法挖掘风电数据中数据间相互关系，并将离群数据标记为异常	较为简单，且避免了人为原因造成的误差现象，迁移能力强	检测耗时长，算法效率低
基于距离的异常值检测技术	AnomalyDetect^[12]、SODRNN^[24]、 KNN^[25,27]、TCM-KNN^[26]	通过计算每个数据间距离来判断数据是否异常	不要求数据满足某种分布，适用于高维数据	参数选取敏感，全局检测，需提前得知数据的先验知识，无法区分数据异常程度
基于密度的异常值检测技术	LOF^[31]、PCA-LOF^[32]、COAS^[33]、 DLC^[34]	通过检测数据局部的密度信息判断是否异常	检测精度高	参数选取敏感，局部检测造成时间复杂度高
基于序列偏差的异常值检测技术	SCREEN^[35]	根据相邻序列中存在的明显偏差判断是否异常	时间复杂度低	波动性强的数据适用性低，搜索范围大，效率低
基于预测偏差的异常值检测技术	LSTM-AE^[10]、DeepAnT^[36]	根据预测值与实际值偏差大小判断是否异常	时间复杂度低	对拟合模型精度要求高，检测效果低于其他算法
基于集成的异常值检测技术	ODCA^[39]、dAE^[40]、LSTM-AE-ADV^[41]、OmniAnomaly^[42]、Fuzzy clustering^[43]、SLADE-TS和SLADE-MTS^[44]	通过组合多种异常检测算法来判断是否异常	精准度高，鲁棒性好	时间复杂度高

方法	文献	技术特点	优点	缺陷与不足
基于统计的异常值检测技术	HBOS^[12]、统计量分析法^[15]、3σ准则^[17]	假定数据服从某种分布，并根据数据不一致性判断其是否异常	简单、易懂	需提前确定数据的分布特征，应用困难
基于聚类的异常值检测技术	马氏距离与K-means结合^[20]、DPC^[21]、自适应DBSCAN^[22]	使用聚类算法挖掘风电数据中数据间相互关系，并将离群数据标记为异常	较为简单，且避免了人为原因造成的误差现象，迁移能力强	检测耗时长，算法效率低
基于距离的异常值检测技术	AnomalyDetect^[12]、SODRNN^[24]、 KNN^[25,27]、TCM-KNN^[26]	通过计算每个数据间距离来判断数据是否异常	不要求数据满足某种分布，适用于高维数据	参数选取敏感，全局检测，需提前得知数据的先验知识，无法区分数据异常程度
基于密度的异常值检测技术	LOF^[31]、PCA-LOF^[32]、COAS^[33]、 DLC^[34]	通过检测数据局部的密度信息判断是否异常	检测精度高	参数选取敏感，局部检测造成时间复杂度高
基于序列偏差的异常值检测技术	SCREEN^[35]	根据相邻序列中存在的明显偏差判断是否异常	时间复杂度低	波动性强的数据适用性低，搜索范围大，效率低
基于预测偏差的异常值检测技术	LSTM-AE^[10]、DeepAnT^[36]	根据预测值与实际值偏差大小判断是否异常	时间复杂度低	对拟合模型精度要求高，检测效果低于其他算法
基于集成的异常值检测技术	ODCA^[39]、dAE^[40]、LSTM-AE-ADV^[41]、OmniAnomaly^[42]、Fuzzy clustering^[43]、SLADE-TS和SLADE-MTS^[44]	通过组合多种异常检测算法来判断是否异常	精准度高，鲁棒性好	时间复杂度高

方法	文献	技术特点	优点	缺陷与不足
常规处理方法	直接删除法	直接删除数据集中缺失数据	简单、易懂、时间复杂度低	数据信息丢失，缺失率提高，影响后期数据挖掘效果
常规处理方法	均值插补法、零值插补法、上一次观测值插补法等	使用未缺失数据均值、0值或缺失值前一个未缺失数据填补	简单、易懂、时间复杂度低	改变原始数据分布，忽略了不同特征间的相互关联程度，缺乏数据时序性利用
辨别式的插补方法	线性回归插补、非线性回归插补	通过回归分析得出缺失数据	构建简便，计算量小	线性：应用面窄非线性：相较于线性应用面广，不适应于高维数据
	MLP^[55]	通过特殊的前馈神经网络估计数据中的缺失值	可用于线性或非线性插值，整体适用面广泛	参数较多
	递推式非邻均值补全法^[53-54]	由缺失值两侧观测值递推缺失值的方式	构造简单	未充分挖掘数据信息，插补精度低
	三次样条插值法^[57]	通过三次多项式拟合离散数据，以此获得缺失值的估计值	相对于普通插值拟合曲线更为平滑，插补更加准确	离散节点数增加，插补曲线边缘稳定性降低
	MICE^[59]	多次插补降低单次插补造成的标准误差	插补误差较小	整体时长、空间复杂度大幅度提升，应用局限
	MF^[60-61]	通过矩阵分解技术学习时序矩阵的整体特征，进而修补缺失数据	计算复杂度低，可用于处理大规模数据集	模型整体适用受限
	KNN^[52]	通过缺失值周围k个观测值估计缺失值	计算精度高	很少考虑数据间的时序性，计算成本高，空间复杂度高，K值设置
	RNN^[67⇓-69]	通过RNN估计数据中的缺失值	考虑数据间的时序性，插补精度高	插补时仅进行顺序插补，时间复杂度高
生成式的插补方法	EM^[72-73]	迭代计算期望E和最大化M以获得插补数据	简单、插补精度高	对数据集依赖性强，未考虑数据间时序性
	AE^{[74⇓⇓⇓⇓-79]}	通过Encoder和Decoder后重建原始数据	与数据关联性高，插补效果良好	生成数据并非真实数据，训练极端，易记忆训练样本，影响后续工作
	GAN^{[80⇓⇓⇓⇓⇓-86]}	从随机的“噪声”中生成“真实”的样本数据	整体精度高于其他插补模型	网络训练不稳定，训练时需要完整数据，随机噪声
基于物理特性的插补方法	文献[87]、文献[88]	使用临近风场、风机同时刻未缺失数据进行插补	简便，考虑风机、地形等信息特点	需保证风机型号等信息一致，限制性强

方法	文献	技术特点	优点	缺陷与不足
常规处理方法	直接删除法	直接删除数据集中缺失数据	简单、易懂、时间复杂度低	数据信息丢失，缺失率提高，影响后期数据挖掘效果
常规处理方法	均值插补法、零值插补法、上一次观测值插补法等	使用未缺失数据均值、0值或缺失值前一个未缺失数据填补	简单、易懂、时间复杂度低	改变原始数据分布，忽略了不同特征间的相互关联程度，缺乏数据时序性利用
辨别式的插补方法	线性回归插补、非线性回归插补	通过回归分析得出缺失数据	构建简便，计算量小	线性：应用面窄非线性：相较于线性应用面广，不适应于高维数据
	MLP^[55]	通过特殊的前馈神经网络估计数据中的缺失值	可用于线性或非线性插值，整体适用面广泛	参数较多
	递推式非邻均值补全法^[53-54]	由缺失值两侧观测值递推缺失值的方式	构造简单	未充分挖掘数据信息，插补精度低
	三次样条插值法^[57]	通过三次多项式拟合离散数据，以此获得缺失值的估计值	相对于普通插值拟合曲线更为平滑，插补更加准确	离散节点数增加，插补曲线边缘稳定性降低
	MICE^[59]	多次插补降低单次插补造成的标准误差	插补误差较小	整体时长、空间复杂度大幅度提升，应用局限
	MF^[60-61]	通过矩阵分解技术学习时序矩阵的整体特征，进而修补缺失数据	计算复杂度低，可用于处理大规模数据集	模型整体适用受限
	KNN^[52]	通过缺失值周围k个观测值估计缺失值	计算精度高	很少考虑数据间的时序性，计算成本高，空间复杂度高，K值设置
	RNN^[67⇓-69]	通过RNN估计数据中的缺失值	考虑数据间的时序性，插补精度高	插补时仅进行顺序插补，时间复杂度高
生成式的插补方法	EM^[72-73]	迭代计算期望E和最大化M以获得插补数据	简单、插补精度高	对数据集依赖性强，未考虑数据间时序性
	AE^{[74⇓⇓⇓⇓-79]}	通过Encoder和Decoder后重建原始数据	与数据关联性高，插补效果良好	生成数据并非真实数据，训练极端，易记忆训练样本，影响后续工作
	GAN^{[80⇓⇓⇓⇓⇓-86]}	从随机的“噪声”中生成“真实”的样本数据	整体精度高于其他插补模型	网络训练不稳定，训练时需要完整数据，随机噪声
基于物理特性的插补方法	文献[87]、文献[88]	使用临近风场、风机同时刻未缺失数据进行插补	简便，考虑风机、地形等信息特点	需保证风机型号等信息一致，限制性强

方法	文献		技术特点		优点		缺陷与不足
物理模型	[87-93]		使用物理方法进行计算		简单、易懂		对原始数据依赖性较强，抗干扰性、可移植性差
统计模型	传统统计模型^[85]		采用历史数据作为未来时刻预测值使用		简单、易懂		仅限于超短期预测使用，且精度极低
	时间序列模型^{[96⇓⇓⇓⇓-101]}		当前时刻观测值由先前若干时刻观测值及干扰项构成		计算简便		难以挖掘非线性数据信息，预测精度不足，数据较大时计算复杂度较大
	其他机器学习模型	SVM^{[104⇓⇓-107]}	利用定义的核函数进行回归估计	高维计算速度快，不易陷入局部最优解		效果与核函数选取有关，严重依赖于使用者经验
		RF^{[108⇓⇓-111]}	重复执行从根节点开始根据阈值判断进入当前节点的左/右节点，直至叶子节点输出预测值的平均值	适用于大量数据集，计算速度高，准确率较高		原始数据质量差时效果较差
		BART^[113-114]	计算单棵树的均值	精度高，不易过拟合		计算时长较高
	深度学习模型	BP^[117]	—	具有较强的容错和泛化能力		学习速度慢，易出现局部最优
		RNN^{[118,122⇓-124]}	—	可用于长时间数据处理		传统RNN存在梯度消失和梯度爆炸问题，模型训练困难
		CNN^{[119,125⇓-127]}	—	卷积核参数共享，短期信息提取较好		需要调参，计算量大
		AE^[130⇓-132]	对原始数据进行重构，以获得预测结果	数据关联性高		训练极端，易出现驯良样本记忆现象
		GAN^[133⇓-135]	通过生成器鉴别器获得尽可能接近原始数据分布的预测数据	数据无需人工标注		传统GAN训练不稳定，计算效率低，无法描述输入数据特征
组合模型	基于多模型加权的组合预测方法^{[138⇓⇓⇓⇓-143]}		对多个模型预测结果进行加权输出	灵活性、适应性、预测精度较高		计算效率低，应用场景较窄
	基于数据预处理的组合预测方法^[15,144-145]		将原始数据分解为多个平稳子序列后进行预测的方式	结构简单，计算效率高，迁移性强		预测精度有限
	基于优化技术的组合预测方法^{[146⇓⇓⇓-150]}		采用优化技术对模型参数进行优化	促使模型更好收敛和更好的泛化误差		无法保证最优解
	基于误差修正的组合预测方法^{[151⇓⇓-154]}		对预测误差进行估计及预测修正	具有较高的预测精度		相较于其他组合模型计算效率较低

Survey of Wind Power Output Power Forecasting Technology

风电输出功率预测技术研究综述

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