基于机器学习的集群式风光一体短期功率预测技术

doi:10.11930/j.issn.1004-9649.201907013

中国电力 ›› 2020, Vol. 53 ›› Issue (3): 1-7.DOI: 10.11930/j.issn.1004-9649.201907013

• 新能源电力系统源网荷储协调运行技术专栏 • 上一篇下一篇

基于机器学习的集群式风光一体短期功率预测技术

崔杨^1,2, 陈正洪^1,2, 许沛华^1,2

1. 湖北省气象服务中心, 湖北武汉 430205;
2. 湖北省气象能源技术开发中心, 湖北武汉 430205

收稿日期:2019-07-01 修回日期:2019-11-04 发布日期:2020-03-10
通讯作者: 陈正洪(1964-),男,通信作者,工程师,研究员,从事风能太阳能资源合理开发利用研究,E-mail:chenzh64@126.com
作者简介:崔杨(1987-),女,硕士,工程师,从事风电、太阳能发电功率预测研究,E-mail:qhcuiyang@126.com
基金资助:
国家重点研发计划资助项目（2018YFB1502801）

Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method

CUI Yang^1,2, CHEN Zhenghong^1,2, XU Peihua^1,2

1. Hubei Meteorological Service Center, Wuhan 430205, China;
2. Meteorological Energy Development Center of Hubei Province, Wuhan 430205, China

Received:2019-07-01 Revised:2019-11-04 Published:2020-03-10
Supported by:
This work is supported by National Key R&D Program of China (No. 2018YFB1502801)

摘要/Abstract

摘要： 针对区域风、光电站群的功率预测，由于各站建站时间不同、单站预报精度残次不齐，导致传统的单站功率累加法预测精度和运行效率不高的问题，采用基于机器学习的二分K均值聚类算法分别对区域内的风电场和光伏电站群进行合理划分，结合区域内各电站历史功率数据及区域总历史功率数据的相关性，选取出各区域的代表电站。在对数值预报要素进行优化订正后，采用BP神经网络法建立基于风电场和光伏电站集群划分的短期功率预测框架模型。结果表明：采用该方法的集群式风电和光伏短期功率预测准确率高于或接近于传统单站累加的预测精度，且该方法在保证预测精度的同时，能够显著提高建模效率。

关键词: 风电场, 光伏电站, 集群划分, 短期功率预测, 二分K均值聚类

Abstract: Regional wind power and PV(photovoltaic) power forecast is an effective way to improve the robustness of power grid, however, the traditional single-station power accumulation method is poor in accuracy and operation efficiency, because the construction time of each station is different and the accuracy of single-station is various. Therefore, this paper proposes a method for short-term regional wind and PV power prediction based on feature clustering. Firstly, the machine learning-based Bisecting K-Means(BKM) clustering algorithm is used to reasonably divide the wind farms and PV stations in the region into clusters; Secondly, based on the correlation between of the historical power data of each power station and the total historical power data in the region, a representative power station is selected for each region; Thirdly, after optimizing and correcting the NWP(numerical weather prediction) model of each representative power station, a short-term power prediction framework model is established using BP neural network based on the cluster division of wind farms and PV power plants. The result shows that the proposed method is higher than or close to the traditional single-station power accumulation method in short-term prediction accuracy, but it can significantly improve the modeling efficiency while ensuring the prediction accuracy.

Key words: wind farm, PV station, cluster division, short-term power forecast, bisecting K-means

崔杨, 陈正洪, 许沛华. 基于机器学习的集群式风光一体短期功率预测技术[J]. 中国电力, 2020, 53(3): 1-7.

CUI Yang, CHEN Zhenghong, XU Peihua. Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method[J]. Electric Power, 2020, 53(3): 1-7.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201907013

https://www.electricpower.com.cn/CN/Y2020/V53/I3/1

参考文献

[1] 国家能源局. 2018年风电并网运行情况[EB/OL]. (2019-01-28)[2019-06-04]. http://www.nea.gov.cn/2019-01/28/c_137780779.htm.
[2] 国家能源局. 2018年光伏发电统计信息[EB/OL]. (2019-03-19)[2019-06-04]. http://www.nea.gov.cn/2019-03/19/c_137907428.htm.
[3] 崔杨, 陈正洪, 成驰. 光伏发电功率预测预报系统升级方案设计及关键技术实现[J]. 中国电力, 2014, 47(10): 142–147
CUI Yang, CHEN Zhenghong, CHENG Chi, et al. Upgrade of the PV power prediction system and the implementation of the key technologies[J]. Electric Power, 2014, 47(10): 142–147
[4] 王尤嘉, 鲁宗相, 乔颖, 等. 基于特征聚类的区域风电短期功率统计升尺度预测[J]. 电网技术, 2017, 41(5): 1383–1389
WANG Youjia, LU Zongxiang, QIAO Ying, et al. Short-term regional wind power statistical upscaling forecasting based on feature clustering[J]. Power System Technology, 2017, 41(5): 1383–1389
[5] 张小萌, 白恺, 柳玉, 等. 大规模分布式光伏短期集群功率预测综述[J]. 华北电力技术, 2017(6): 1–7
ZHANG Xiaomeng, BAI Kai, LIU Yu, et al. Overview for large-scale distributed photovoltaic power prediction with clustered method[J]. North China Electric Power, 2017(6): 1–7
[6] SHI B, WU Y, ZHU X, et al. Research on wind power prediction considering the correlation of regional multiple wind farms[C]//2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference(APPEEC), Hong Kong, 2014: 1-5.
[7] LOBO M G, SANCHEZ I. Regional wind power forecasting based on smoothing techniques, with application to the Spanish peninsular system[J]. IEEE Transactions on Power Systems, 2012, 27(4): 1990–1997.
[8] 彭小圣, 熊磊, 文劲宇, 等. 风电集群短期及超短期功率预测精度改进方法综述[J]. 中国电机工程学报, 2016, 36(23): 6315–6326
PENG Xiaosheng, XIONG Lei, WEN Jinyu, et al. A summary of the state of the art for short-term and ultra-short-term wind power prediction of regions[J]. Proceedings of the CSEE, 2016, 36(23): 6315–6326
[9] 陈颖, 孙荣富, 吴志坚, 等. 基于统计升尺度方法的区域风电场群功率预测[J]. 电力系统自动化, 2013, 37(7): 1–5
CHEN Ying, SUN Rongfu, WU Zhijian, et al. A regional wind power forecasting method based on statistical upscaling approach[J]. Automation of Electric Power Systems, 2013, 37(7): 1–5
[10] 别朝红, 安佳坤, 陈筱中, 等. 一种考虑时空分布特性的区域风电功率预测方法[J]. 西安交通大学学报, 2013, 47(10): 68–74
BIE Zhaohong, AN Jiakun, CHEN Xiaozhong, et al. Regional wind power prediction considering temporal and spatial characteristics[J]. Journal of Xi'an Jiaotong University, 2013, 47(10): 68–74
[11] 林芳, 林焱, 吕宪龙, 等. 基于均衡KNN算法的电力负荷短期并行预测[J]. 中国电力, 2018, 51(10): 88–94, 102
LIN Fang, LIN Yan, LV Xianlong, et al. Short-term parallel power load forecasting based on balanced KNN[J]. Electric Power, 2018, 51(10): 88–94, 102
[12] 张军伟, 王念滨, 黄少滨, 等. 二分K均值聚类算法优化及并行化研究[J]. 计算机工程, 2011, 37(17): 23–25
ZHANG Junwei, WANG Nianbin, HUANG Shaobin, et al. Research on bisecting K-means clustering algorithm optimization and parallelism[J]. Computer Engineering, 2011, 37(17): 23–25
[13] 郭艳飞, 程林, 李洪涛, 等. 基于支持向量机和互联网信息修正的空间负荷预测方法[J]. 中国电力, 2019, 52(4): 80–88
GUO Yanfei, CHENG Lin, LI Hongtao, et al. Spatial load forecasting method based on support vector machine and internet information correction[J]. Electric Power, 2019, 52(4): 80–88
[14] 王勃, 刘纯, 冯双磊. 基于集群划分的短期风电功率预测方法[J]. 高电压技术, 2018, 44(4): 1254–1260
WANG Bo, LIU Chun, FENG Shuanglei. Prediction method for short-term wind power based on wind farm clusters[J]. High Voltage Engineering, 2018, 44(4): 1254–1260
[15] SIEBERT N. Development of methods for regional wind power forecasting[D]. Paris: École Nationale Supérieure des Mines de Paris, 2008.
[16] 渠慎明, 王青博, 刘珊, 等. 基于二分K均值聚类和最近特征线的视频超分辨率重建方法[J]. 河南大学学报(自然科学版), 2018, 48(3): 292–298
QU Shenming, WANG Qingbo, LIU Shan, et al. Video super resolution reconstruction method based on bisecting K-means clustering and nearest feature line[J]. Journal of Henan University(Natural Science Edition), 2018, 48(3): 292–298
[17] 崔杨, 陈正洪, 孙朋杰. 弃光限电条件下不同纬度地区短期光伏发电功率预测对比分析[J]. 太阳能学报, 2018, 39(6): 1610–1618
CUI Yang, CHEN Zhenghong, SUN Pengjie. Comparison and analysis of short-term PV power prediction at different latitude under condition of discarding PV power[J]. Acta Energiae Solaris Sinica, 2018, 39(6): 1610–1618
[18] 崔杨, 陈正洪, 刘丽珺. 弃风限电条件下复杂地形风电场短期风功率预测对比分析[J]. 太阳能学报, 2017, 38(12): 3376–3384
CUI Yang, CHEN Zhenghong, LIU Lijun. Short-term wind power prediction analysis of complicated topography in abandoned wind power conditions[J]. Acta Energiae Solaris Sinica, 2017, 38(12): 3376–3384
[19] 梁允, 许沛华, 孙芊, 等. 基于滚动的BP神经网络的光伏发电功率预报[J]. 水电能源科学, 2017, 35(9): 212–214
LIANG Yun, XU Peihua, SUN Qian, et al. Photovoltaic power forecast based on rolling BP neural network[J]. Water Resources and Power, 2017, 35(9): 212–214
[20] 高大启. 有教师的线性基本函数前向三层神经网络结构研究[J]. 计算机学报, 1998, 21(1): 80–86
GAO Daqi. On structures of supervised linear basis function feed forward three-layered neural networks[J]. Chinese Journal of Computers, 1998, 21(1): 80–86

基于机器学习的集群式风光一体短期功率预测技术

Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	宋许鹏, 杨小洋, 樊征臻. 基于电流合成矢量轨迹特性的双馈风电场主变压器差动保护[J]. 中国电力, 2025, 58(2): 9-21.
[2]	沃建栋, 潘武略, 李跃辉, 吴佳毅. 基于综合谐波驱动的光伏场站集电线路保护新方案[J]. 中国电力, 2025, 58(2): 133-139.
[3]	王泽嘉, 韩民晓, 范溢文. 基于SVG附加电流反馈阻抗重塑的双馈风电场次/超同步振荡抑制策略[J]. 中国电力, 2024, 57(8): 55-66.
[4]	叶婧, 蔡俊文, 张磊, 周广浩, 何杰辉, 翟学. 考虑海缆实际载流量的海上风电集电系统拓扑优化[J]. 中国电力, 2024, 57(7): 173-181.
[5]	周于清, 李大虎, 姚伟, 宗启航, 周泓宇, 文劲宇. 受端近区光伏电站对LCC-HVDC系统稳定性影响分析[J]. 中国电力, 2024, 57(3): 170-182.
[6]	覃海, 陈胜, 张洪略, 夏天, 马覃峰, 段展宇, 颜伟. 并网光伏电站关口无功调节能力的分时段等值方法[J]. 中国电力, 2024, 57(2): 27-33.
[7]	李丹, 梁云嫣, 缪书唯, 方泽仁, 胡越, 贺帅. 基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法[J]. 中国电力, 2024, 57(12): 17-29.
[8]	杨鹏伟, 赵丽萍, 陈军法, 甄钊, 王飞, 李利明. 基于相似时段匹配与Transformer网络建模的分布式光伏超短期功率预测方法[J]. 中国电力, 2024, 57(12): 60-70.
[9]	张江丰, 柯松, 陈文进, 王天宇, 郑可轲, 杨军. 光伏电站自备用可调的虚拟同步控制调频策略[J]. 中国电力, 2024, 57(11): 108-118.
[10]	吴晓刚, 阎洁, 葛畅, 唐雅洁, 倪筹帷, 季青锋. 基于改进GRU-CNN的风光水一体化超短期功率预测方法[J]. 中国电力, 2023, 56(9): 178-186,205.
[11]	董淑文, 刘宝柱, 胡俊杰. 计及线路优化投切限流的含风电电网扩展规划方法[J]. 中国电力, 2023, 56(8): 117-125,142.
[12]	徐艳春, 范钟耀, 孙思涵, MI Lu. 基于去趋势分析的大规模双馈风电场送出变压器差动保护[J]. 中国电力, 2023, 56(7): 186-197.
[13]	叶婧, 周广浩, 张磊, 杨莉, 翟学, 蔡俊文. 考虑馈线交叉规避的海上风电场海缆路径优化[J]. 中国电力, 2023, 56(6): 167-175.
[14]	梁伟, 吴林林, 赖启平, 李东晟, 徐曼, 沈沉. 风电直流送出系统送端交流故障下风机过电压研究[J]. 中国电力, 2023, 56(4): 28-37.
[15]	叶畅, 伊华茂, 朱炯达, 赵晶晶, 吴炼. 考虑灵活性供需平衡及响应速度的分布式电源集群划分方法[J]. 中国电力, 2023, 56(2): 150-156.

模态框（Modal）标题

基于机器学习的集群式风光一体短期功率预测技术

Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics