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
WU Yuhao1,2, WANG Yongsheng1,2,+(), XU Hao1,2, CHEN Zhen1,2, ZHANG Zhe1,2, GUAN Shijie1,2
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.Supported by:
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
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。基金资助:
CLC Number:
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.
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URL: http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2205028
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[ | 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[ | 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[ KNN[ | 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[ DLC[ | 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[ | 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[ | 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[ | 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 |
Table 1 Comparison of wind turbine anomaly detection methods
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[ | 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[ | 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[ KNN[ | 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[ DLC[ | 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[ | 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[ | 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[ | 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 |
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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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 |
Table 2 Comparison of missing value interpolation methods for wind turbines
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 |
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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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 | [ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | — | 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[ | — | 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[ | — | 卷积核参数共享,短期信息提取较好 | 需要调参,计算量大 | ||||
AE[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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 |
Table 3 Comparison of existing wind power forecasting methods
方法 | 文献 | 技术特点 | 优点 | 缺陷与不足 | |||
---|---|---|---|---|---|---|---|
物理模型 | [ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | — | 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[ | — | 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[ | — | 卷积核参数共享,短期信息提取较好 | 需要调参,计算量大 | ||||
AE[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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 |
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