基于自适应优化AP聚类与BP加权网络的多区域复合短期风电功率预测

赵飞; 张天祥

doi:10.19912/j.0254-0096.tynxb.2023-0482

PDF(2442 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 634-640. DOI: 10.19912/j.0254-0096.tynxb.2023-0482

基于自适应优化AP聚类与BP加权网络的多区域复合短期风电功率预测

赵飞, 张天祥

作者信息 +

MULTI-REGIONAL COMPOSITE SHORT-TERM WIND POWER PREDICTION BASED ON ADAPTIVE OPTIMIZATION AP CLUSTERING AND BP WEIGHTED NETWORK

Zhao Fei, Zhang Tianxiang

Author information +

文章历史 +

摘要

精准的风电集群区域功率预测对电源侧的竞价上网具有重要意义。由于同一地区多个风电场受气候影响波动程度相近,可看作具有时空相关性的风电场群,并以此进行集群的合理划分。为此,提出一种基于自适应优化近邻传播（AP）聚类与反向传播（BP）加权神经网络的多区域复合短期风电功率预测模型。首先,通过粒子群优化算法（PSO）优化AP聚类方法对风电场群的历史数据进行集群的聚类与划分;然后,根据得到的最优聚类结果构建风电场群子区域样本训练集;最后,利用基于相关系数权重的BP神经网络对各子区域进行功率预测。算例结果表明：所提方法在24 h日前预测相较传统叠加法与单一BP神经网络可提高1.35%和2.62%的精度,可表明该模型具有优越的预测性能。

关键词

风电场 / 聚类分析 / 粒子群算法 / 反向传播 / 相关性理论 / 功率预测

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

赵飞, 张天祥. 基于自适应优化AP聚类与BP加权网络的多区域复合短期风电功率预测[J]. 太阳能学报. 2024, 45(7): 634-640 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0482

Zhao Fei, Zhang Tianxiang. MULTI-REGIONAL COMPOSITE SHORT-TERM WIND POWER PREDICTION BASED ON ADAPTIVE OPTIMIZATION AP CLUSTERING AND BP WEIGHTED NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 634-640 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0482

中图分类号： TM614

参考文献

[1] 阎洁, 张永蕊, 张浩. 区域风电场群集中式功率预测系统设计与应用[J]. 分布式能源, 2022, 7(1): 28-36.
YAN J, ZHANG Y R, ZHANG H.Design and application of centralized power forecasting system for regional wind farm cluster[J]. Distributed energy, 2022, 7(1): 28-36.
[2] 刘光辉. 风电不确定性对系统的影响研究[J]. 山东工业技术, 2017(16): 220.
LIU G H.风电不确定性对系统的影响研究[J]. Journal of Shandong industrial technology, 2017(16): 220.
[3] HANIFI S, LIU X L, LIN Z, et al.A critical review of wind power forecasting methods: past, present and future[J]. Energies, 2020, 13(15): 3764.
[4] 杨茂, 杨琼琼, 苏欣. 基于风电场等效平均风速的风电功率日前预测研究[J]. 太阳能学报, 2020, 41(2): 85-92.
YANG M, YANG Q Q, SU X.Research of day-ahead wind power forecast based on wind farm equivalent mean wind speed[J]. Acta energiae solaris sinica, 2020, 41(2): 85-92.
[5] 刘文斌, 谢丽蓉, 张革荣. 基于VMD-HS-LSSVM的风电功率短期预测[J]. 现代电子技术, 2022, 45(11): 176-181.
LIU W B, XIE L R, ZHANG G R.Short-term wind power prediction based on VMD-HS-LSSVM[J]. Modern electronics technique, 2022, 45(11): 176-181.
[6] 李海玲. 基于BP神经网络的风功率预测[J]. 现代信息科技, 2021, 5(15): 119-121, 124.
LI H L.Wind power prediction based on BP neural network[J]. Modern information technology, 2021, 5(15): 119-121, 124.
[7] 张群, 唐振浩, 王恭, 等. 基于长短时记忆网络的超短期风功率预测模型[J]. 太阳能学报, 2021, 42(10): 275-281.
ZHANG Q, TANG Z H, WANG G, et al.Ultra-short-term wind power prediction model based on long and short term memory network[J]. Acta energiae solaris sinica, 2021, 42(10): 275-281.
[8] 栗然, 马涛, 张潇, 等. 基于卷积长短期记忆神经网络的短期风功率预测[J]. 太阳能学报, 2021, 42(6): 304-311.
LI R, MA T, ZHANG X, et al.Short-term wind power prediction based on convolutional long-short-term memory neural networks[J]. Acta energiae solaris sinica, 2021, 42(6): 304-311.
[9] YU R G, GAO J, YU M, et al.LSTM-EFG for wind power forecasting based on sequential correlation features[J]. Future generation computer systems, 2019, 93: 33-42.
[10] HAO Y, TIAN C.A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting[J]. Applied energy, 2019, 238: 368-383.
[11] 韩爽, 孟航, 刘永前, 等. 增量处理双隐层BP神经网络风电功率预测模型[J]. 太阳能学报, 2015, 36(9): 2238-2244.
HAN S, MENG H, LIU Y Q, et al.Study on optimization of BP neural network wind power predicition model with two hidden layers[J]. Acta energiae solaris sinica, 2015, 36(9): 2238-2244.
[12] 张秀春, 蔡敏, 张运杰. 基于改进的AP聚类的图像分割算法[C]// 中国自动化学会控制理论专业委员会, 大连, 中国, 2017.
ZHANG X C, CAI M, ZHANG Y J.An image segmentation algorithm based on improved affinity propagation[C]// Technical Committee on Control Theory,Chinses Association of Automation. Dalian, China, 2017.
[13] GUAN R C, SHI X H, MARCHESE M, et al.Text clustering with seeds affinity propagation[J]. IEEE transactions on knowledge and data engineering, 2011, 23(4): 627-637.
[14] 管霖, 周保荣, 文博, 等. 多风电场功率时间序列的时空相关性统计建模和运行模拟方法[J]. 电网技术, 2021, 45(1): 30-39.
GUAN L, ZHOU B R, WEN B, et al.Spatiotemporal correlation statistic modeling and simulation in multiple wind farm power sequence[J]. Power system technology, 2021, 45(1): 30-39.
[15] 赵海成, 李辉. SA-PSO优化边界最速跟踪微分器在光伏功率平滑中的应用[J]. 电子测量技术, 2022, 45(14): 36-42.
ZHAO H C, LI H.Application of variable boundary tracking differentiator optimized by SA-PSO in photovoltaic power smoothing[J]. Electronic measurement technology, 2022, 45(14): 36-42.
[16] 赖健琼. 自适应AP聚类算法研究[J]. 计算机时代, 2022(4): 38-42.
LAI J Q.Research on adaptive AP clustering algorithm[J]. Computer era, 2022(4): 38-42.
[17] 张慧娥, 刘大贵, 朱婷婷, 等. 基于相似日和动量法优化BP神经网络的光伏短期功率预测研究[J]. 智慧电力, 2021, 49(6): 46-52.
ZHANG H E, LIU D G, ZHU T T, et al.Short-term PV power prediction based on BP neural network optimized by similar daily and momentum method[J]. Smart power, 2021, 49(6): 46-52.
[18] 李根银, 郁冶, 王异成, 等. 风电场功率预测的研究进展及发展趋势[J/OL]. 排灌机械工程学报: 1-8[2023-04-05].
LI G Y, YU Y, WANG Y C, et al.Research progress and development trend of wind farm power prediction[J/OL]. Journal of drainage and irrigation machinery engineering: 1-8[2023-04-05].
[19] ZHANG J A, LIU D, LI Z J, et al.Power prediction of a wind farm cluster based on spatiotemporal correlations[J]. Applied energy, 2021, 302: 117568.
[20] 王佳钰, 郝思鹏, 李森文, 等. 基于ES-GRU-LSTM的风电场群功率预测[J]. 计算技术与自动化, 2022, 41(3): 37-41.
WANG J Y, HAO S P, LI S W, et al.Power prediction of wind farm group based on ES-GRU-LSTM[J]. Computing technology and automation, 2022, 41(3): 37-41.
[21] 武佳卉, 邵振国, 杨少华, 等. 数据清洗在新能源功率预测中的研究综述和展望[J]. 电气技术, 2020, 21(11): 1-6.
WU J H, SHAO Z G, YANG S H, et al.Review and prospect of data cleaning in renewable energy power prediction[J]. Electrical engineering, 2020, 21(11): 1-6.
[22] 姚琦, 柳玉, 白恺, 等. 风电场功率预测水平的多指标综合评价方法研究[J]. 太阳能学报, 2019, 40(2): 333-340.
YAO Q, LIU Y, BAI K, et al.Study of multi-index comprehensive evaluation method for wind farm power prediction level[J]. Acta energiae solaris sinica, 2019, 40(2): 333-340.