多时空尺度的风力发电预测方法综述

姜兆宇; 贾庆山; 管晓宏

doi:10.16383/j.aas.c180389

多时空尺度的风力发电预测方法综述

doi: 10.16383/j.aas.c180389

姜兆宇^1,,
贾庆山^1, ,,
管晓宏^1,2,

1.
清华大学自动化系智能与网络化系统研究中心, 北京信息科学与技术国家研究中心北京 100084
2.
西安交通大学智能网络与网络安全教育部重点实验室西安 710049

基金项目:

国家自然科学基金 61673229

国家自然科学基金 61222302

国家自然科学基金 61174072

国家自然科学基金 61221063

国家自然科学基金 U1301254

国家重点研究发展计划 2016YFB0901900

国家自然科学基金 91224008

111创新引智计划 B06002

北京市科技新星项目 xx2014B056

详细信息

作者简介:
姜兆宇清华大学自动化系智能与网络化系统研究中心博士研究生.2014年获得清华大学学士学位.主要研究方向为信息物理融合能源系统的数据分析与预测, 系统建模与优化.E-mail:zy-jiang14@mails.tsinghua.edu.cn

管晓宏中国科学院院士, 长江学者特聘教授, 西安交通大学电子信息工程学院院长, 清华大学智能与网络化系统研究中心讲席教授组成员.1982年和1985年获得清华大学控制工程专业学士和硕士学位, 1993年获得美国康涅狄格大学电气工程专业博士学位.主要研究方向为复杂网络系统, 包括智能电网, 生产制造系统以及电力市场的规划和调度.E-mail:xhguan@tsinghua.edu.cn

通讯作者:
贾庆山清华大学自动化系智能与网络化系统研究中心副教授.2006年获得清华大学控制科学与工程专业博士学位.主要研究方向为离散事件动态系统理论与应用, 基于仿真的复杂系统性能评价与优化.本文通信作者.E-mail:jiaqs@tsinghua.edu.cn

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出版历程
- 收稿日期: 2018-06-01
- 录用日期: 2018-10-09
- 刊出日期: 2019-01-20

A Review of Multi-temporal-and-spatial-scale Wind Power Forecasting Method

1.
Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, Beijing National Research Center for Information Science and Technology, Beijing 100084
2.
Key Laboratory of Intelligent Network and Network Security, Ministry of Education, Xi'an Jiao Tong University, Xi'an 710049

Funds:

National Natural Science Foundation of China 61673229

National Natural Science Foundation of China 61222302

National Natural Science Foundation of China 61174072

National Natural Science Foundation of China 61221063

National Natural Science Foundation of China U1301254

National Key Research and Development Program of China 2016YFB0901900

National Natural Science Foundation of China 91224008

111 International Collaboration Project of China B06002

Program for New Star of Science and Technology in Beijing xx2014B056

More Information

Author Bio:
Ph. D. candidate at the Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University. He received his bachelor degree from Tsinghua University in 2014. His research interest covers data analysis and forecasting, system modeling and optimization of cyber physical energy system

Academician of Chinese Academy of Sciences, distinguished professor of Changjiang Scholars, Dean of the School of Electronic and Information Engineering, Xi'an Jiaotong University, members of chair professor group of the Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University. He received his bachelor and master degrees in control engineering from Tsinghua University in 1982 and 1985, and Ph. D. degree in electrical engineering from University of Connecticut, USA, in 1993, respectively. His research interest covers complex networked systems including smart power grids, planning and scheduling of electrical power, manufacturing systems, and electric power markets

Corresponding author: JIA Qing-Shan Associate professor at the Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University. He received his Ph. D. degree in control science and engineering from Tsinghua University in 2006. His research interest covers theories and applications of discrete event dynamic systems, simulation-based performance evaluation, and optimization of complex systems. Corresponding author of this paper

摘要

摘要: 风能是目前世界上装机量较大的可再生能源之一，风力发电预测的精度直接影响电网的调度与安全运营.由于电网的调度策略存在多个时间点，并与涉及的地域范围有关，本文从多种时间和空间尺度的角度，综述风力发电预测方法.风力发电预测一般针对特定的空间范围和时间尺度，并在有限信息资源的条件下完成，故本文从上述三个方面综述已有研究成果.本文首先根据风力发电空间范围，从单台风力发电机、单一风电场以及风电场群三个空间尺度对研究成果进行梳理.其次在每个空间尺度上，根据风电预测是否使用气象信息将研究成果分类，并根据预测时间尺度将研究成果再次分类.最后在每个时间尺度上，根据风电预测存在的挑战，将已有的研究成果归类.通过上述梳理，本文希望可以帮助研究人员找到适合不同风电预测任务场景的方法.
- 多尺度 /
- 风力发电 /
- 预测方法 /
- 时间尺度 /
- 空间尺度
Abstract: Wind power is one of the most installed renewable energy resources in the world, and the accuracy of wind power forecasting method directly affects dispatching and operation safety of the power grid. Since scheduling strategy of the power grid has multiple points and is relative to the geographical scope, this paper summarizes wind power forecasting methods from multi-temporal and multi-spatial perspective. Wind power forecasting usually focuses on specific spatial scale and temporal scale, and is finished with limited information, so this paper classifies researches from three aspects above. Firstly, this paper separates existing wind power forecasting methods into three spatial scales, namely a single wind turbine, a wind farm and a group of wind farms. In each spatial scale, we classify methods by whether using meteorological information, and afterwards by temporal scales. Lastly, in each temporal scale, we also summarize the challenges and achievements. This paper wishes researchers would find suitable methodology when dealing with different wind power forecasting tasks.
- Multi-scale /
- wind power /
- forecasting method /
- temporal scale /
- spatial scale
注释:

1) 本文责任编委苏宏业

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图 1 单台风机短期风速风电预测方法框架

Fig. 1 Framework of short-term wind power forecasting methods for a single turbine

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图 2 利用数值天气预报预测单台风机短期风电步骤

Fig. 2 Procedure of short-term wind power forecasting methods for a single turbine using numerical weather prediction

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图 3 基于风力信息风电场短期风电预测方法框架

Fig. 3 Framework of short-term wind power forecasting methods for a wind farm with wind information

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图 4 利用信号分解预测风电场风速风电方法框图

Fig. 4 Framework of wind power forecasting methods for a wind farm using signal decomposition

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图 5 利用方法综合思想预测风电场风速风电框架

Fig. 5 Framework of wind power forecasting methods for a wind farm with synthesized thought

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图 6 利用丰富气象数据预测风电场短期风电方法框架

Fig. 6 Framework of short-term wind power forecasting methods for a wind farm with abundant meteorological information

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图 7 利用数值天气预报预测风电场长期风电方法框架

Fig. 7 Framework of long-term wind power forecasting methods for a wind farm using numerical weather prediction

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图 8 测量-关联-预测方法示意图

Fig. 8 Schematic diagram of measure-correlation-prediction methods

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表 1 单台风力发电机的部分发电预测方法对比

Table 1 Comparison among part of wind power forecasting methods for a single turbine

文献	尺度	方法	输入	适用	误差
[25]	5 s	滑动平均	风速	平稳、线性	—
[26]	12 h	多元回归	风电	平稳、波动小	RMSE:比常值法低4.43 %
[27]	24 h	泰勒-克里格	风速	相似序列模式	RMSE:比ARIMA低15.23 %
[28]	10 min	马氏链、经验分布	风电	平稳	RMSE:连续18 h内2.36 m/s
[30]	5 d	指数平滑	风速	长期平稳	MAPE: 49.68 %
[31]	2 d	分数-ARIMA	风速	噪声与频率反比	MSE:比常值法低42 %
[32]	2 d	主成分分析、相空间重构	风电	长期平稳	NMAE:约3 %, 80 %情况下不超过12.5 %
[36]	7.5 h	NWP、分段转化	预报风速	测量准、地表均匀	NRMSE: 2.69 %
[37]	36 h	NWP、线性校正	测风速、预报风速	线性校正、长期相关	RMSE: 1.19 m/s
[38]	8 d	神经网络	风速、风电、温度	预报风速转化风电	NRMSE: 2.01 %
注:误差评价标准一般包括标准均方根误差(Normalized root mean squared error, NRMSE), 均方根误差(Root mean squared error, RMSE), 相对百分比平均绝对误差(Mean absolute percentage error, MAPE), 标准平均绝对误差(Normalized mean absolute error, NMAE), 平均绝对误差(Mean absolute error, MAE), 标准均方误差(Normalized mean squared error, NMSE), 均方误差(Mean squared error, MSE), 下同.

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表 2 只利用风力信息的风电场的部分发电预测方法对比

Table 2 Comparison among part of wind power forecasting methods merely using wind information for a wind farm

文献	尺度	方法	输入	适用	误差
[42]	90 min	贝叶斯推断	风速	间歇性、先验规律	RMSE:比常值法低2.796 %
[43]	60 min	支持向量机、马尔科夫链	风速	间歇性、平稳	MAE: 9.1 %
[44]	10 min	贝叶斯推断、马尔科夫链	风速	平稳、先验规律	RMSE:比常值法低4.57 %
[47]	1 h	受限向量-ARMA	风速、风向	二维平稳	MAE: 0.809 m/s
[48]	15 min	上下界估计网络	风电	平稳	—
[57]	24 h	成组数据推断网络	风速	平稳	MAE: 2.176 m/s
[58]	30 min	贝叶斯推断、神经网络、高斯过程	风速、风电	平稳、高斯性	NRMSE: 16.9 %
[62]	3 h	ARIMA、卡尔曼滤波	风速	稳定、波动小	MAPE: 2.06 %
[68]	3 h	支持向量机、经验小波	风速、风电	非平稳、子序列可预测性	MAPE: 18.41 %
[70]	3 h	小波支持向量机	风速	非平稳	MAPE: 10.76 %
[71]	3 h	经验模态分解、神经网络	风速	非平稳、子序列可预测性	MAPE: 1.87 %
[76]	30 h	自适应小波网络、前馈网络	风速、风电	长期平稳	RMSE: 10.221 %
[77]	1天	ARIMA、卡尔曼滤波、神经网络	风速	长期平稳、相关	MAPE:两测试点分别为16.52 %、8.10 %
[83]	1天/ 1周	小波变换、模糊网络	风电	长期平稳、偏差平稳	MAPE: 11.91 % (1天)/ 15.38 % (1周) %
[85]	1天/ 1月	经验模态分解、神经网络	风速	非平稳、子序列可预测性	MAPE: 17.29 % (1天)/ 14.92 % (1月)
[86]	1天	小波包变换、相空间重构	风速	非平稳、子序列可预测特征	MAPE:约20.37 %
[87]	24 h	主成分分析	风速	可预测特征	MAE:比常值法低8 %$ \sim $11 %
[89]	24 h	广义主成分分析	风速	隐藏可预测特征	RMSE: 2.31$ \sim $3.76 m/s
[91]	24 h	珊瑚礁优化算法、神经网络	风速	隐藏可预测成分	RMSE: 2.272 m/s
[93]	1年/ 1月/ 30天	多尺度数据融合、神经网络	风速	长短期分别平稳	MAE: 1年0.80 m/s, 1月0.17 m/s, 30天0.64 m/s
[92]	48 h	高斯过程、神经网络	风电	短期惯性、长期间歇性	RMSE: 0.175 m/s
[94]	90天	Elman递归网络、季节调整	风速	季节特征、长期平稳	MAPE: 15.32 %
[97]	1天	高斯混合、高斯回归	风速	短期非平稳、季节特征	MAPE: 0.28 %
[98]	5天	径向基神经网络、特征分类	风速、风电	有模式特征、长期非平稳	RMSE: 4.78 %
[99]	3天	ARIMA-神经网络	风速	基本平稳	MAE:某处0.508 m/s

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表 3 利用风力信息及其他气象信息的风电场的部分发电预测方法对比

Table 3 Comparison among part of wind power forecasting methods using wind information and meteorological information for a wind farm

文献	尺度	方法	输入	适用	误差
[41]	10 min	图学习、马尔科夫链	地理信息、风速、风电	间歇性、季节性、相似模式	MAPE: 3.65 %$ \sim $8.22 %
[101]	10 min	洛伦兹扰动-小波神经网络	风速、气压、粗糙度、温度、湿度	预测偏差混沌、洛伦兹特性	RMSE:比小波神经网络高50 %$ \sim $70 %
[102]	30 min	符号回归	风电、湿度、温度、风速	存在隐含相关性	RMSE: 12.6 %
[104]	4 h	谱聚类、回声状态网络	风速、温度	平稳、存在谱特征	MAPE: 8.74 %$ \sim $11.86 %
[105]	3 h	软计算、距离测度	风速、风向、温度	相似天气模式	MAPE: 9.23 %$ \sim $10.77 %
[107]	1.5 h	NWP、自回归、神经网络、卡尔曼滤波	风速、风电、气压、温度、预报风速	预报风速转化风电、多模型筛选、平稳	RMSE:比常值法低7 %$ \sim $65 %, 平均约48 %
[108]	24 h	NWP、神经网络、小波分解	风电、预报风速、温度、气压、湿度	预报风速转化风电、周期性	NMAE: 10.98 %$ \sim $18.88 %
[109]	24 h	NWP、高斯过程	风速、温度、湿度、气压	高斯性、预报风速转化风电	NMAE: 7.6 %$ \sim $11.12 %
[111]	48 h	NWP、神经网络	温度、预报风速	地表均匀	MAE: 1.8 m/s
[112]	24 h	NWP、模式分析	预测风速	季节规律	MAE: 2.6 m/s$ \sim $3.0 m/s
[113]	48 h	NWP、自适应重采样	风电、预报风速	预报风速转化置信区间	NMAE:不超过1.5 %
[114]	36 h	NWP、神经网络、帝国竞争算法	风电、预报的风速、湿度、温度	预报风速转化风电	RMSE: 17.2 %
[115]	24 h	NWP、神经网络、卡尔曼滤波	预报的风速、温度、气压、湿度	预报风速转化风电、短期平稳	NRMSE: 16.47 %
[116]	36 h	NWP、卡尔曼滤波、自动调谐	风电、预报风速	短期平稳、风速转化风电	RMSE:比常值法低约60 %
[117]	24 h	卡尔曼滤波、$k$近邻分类	风速、风向、气温、气压、相对湿度	相似天气模式、长期非平稳	MAPE: 7.083 %
[118]	31 d	混沌序列、k均值聚类	风速、气压、温度、湿度	相似天气模式、长期非平稳	MAPE: 19.21 %
[119]	50 d	软计算、模型融合	风速、温度	非平稳、多模型	NMSE: 0.6515
[123]	36 h	NWP、径向基网络、自组织匹配	风电、预报风速	长期平稳性、预报风速转化风电	NRMSE: 9.77 %$ \sim $13.44 %
[124]	48 h	NWP、粒子群优化、神经网络	风电、预报的风速、温度、湿度	预报风速转化风电、多模型筛选	NRMSE: 16.58 %
[125]	72 h	卡尔曼滤波、分位点回归	风速、风电、地表粗糙度	长期平稳、地表均匀	NMAE:约10 %$ \sim $20 %

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表 4 风电场群的部分发电预测方法对比

Table 4 Comparison among part of wind power forecasting methods for wind farm group

文献	尺度	方法	输入	适用	误差
[35]	4 h	NWP、粒子群优化、神经网络	风电、预报的风速、温度、湿度	预报风速转化风电、多模型筛选	—
[129]	1天	极限学习机、季节ARIMA	风速	惯性、平稳、季节周期	MAPE: 26.68 %
[131]	3 h	自适应神经模糊扰动、小波变换、粒子群优化	风电	非平稳、子序列可预测性	MAPE: 5.81 %不等
[134]	1 h	神经网络、贝叶斯方法	风电	平稳、先验规律	RMSE:比常值法高7 %不等
[136]	1月	神经网络	风速	长期平稳	MAPE: 14.13 %
[138]	12月	神经网络	风速	长期平稳	MAPE: 8.9 %
[139]	12月	测量关联预测、韦伯分布	风速	空间相关性、分布平稳	NMAE: 4.8 %
[141]	60天	测量关联预测	风速	空间相关性	RMSE: 0.78 %
[142]	10 min	神经网络	温度、气压、辐射、风速	平稳	MAPE: 4.55 %
[145]	24 h	嵌套NWP	风电密度、风速分布、预报风速	分布平稳	RMSE: 2 m/s
[146]	24 h	NWP、卡尔曼滤波	风速、预报风速	平稳	MAE: 2.33 m/s
[147]	12 h	NWP、统计升尺度、卡尔曼滤波	风电、风速、预报风速	风速转化风电、局部与总体变化相似	NRMSE: 5.51 %
[148]	12 h	NWP、系数贝叶斯、高斯过程	风速、风电、预测风速	平稳、先验规律、风速转化风电	MAPE: 29.87 %
[149]	6 h	NWP、扭曲高斯分布	风速、风电、预测风速	多模型选择、风速转化风电	MAPE: 10 %
[152]	12月	NWP、测量关联预测	风速、风电、预测风速	空间相关性、风速转化风电	NMAE:风速2.8 %, 风电7.9 %

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