[1]王粟,邱春辉,曾亮.自适应变异粒子群优化BP的短期风电功率预测模型[J].华侨大学学报(自然科学版),2020,41(1):90-95.[doi:10.11830/ISSN.1000-5013.201906031]
　WANG Su,QIU Chunhui,ZENG Liang.Short-Term Wind Power Prediction Model of Adaptive Mutation Particle Swarm Optimization BP[J].Journal of Huaqiao University(Natural Science),2020,41(1):90-95.[doi:10.11830/ISSN.1000-5013.201906031]
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自适应变异粒子群优化BP的短期风电功率预测模型()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第41卷
期数:
2020年第1期
页码:
90-95
栏目:
出版日期:
2020-01-20

文章信息/Info

Title:
Short-Term Wind Power Prediction Model of Adaptive Mutation Particle Swarm Optimization BP
文章编号:
1000-5013(2020)01-0090-06
作者:
王粟;  邱春辉;  曾亮
湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室, 湖北 武汉 430068
Author(s):
WANG Su;  QIU Chunhui;  ZENG Liang
Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
关键词:
短期风电预测;  互信息;  自适应惯性权重系数;  变异因子;  反向传播神经网络
Keywords:
short-term wind power prediction;  mutual information;  adaptive inertia weight coefficient;  variation factor;  back propagation neural network
分类号:
TM614;TP183
DOI:
10.11830/ISSN.1000-5013.201906031
文献标志码:
A
摘要:
针对反向传播(BP)神经网络预测模型在风电预测中预测精度低、输入变量多的问题,提出一种基于互信息的自适应变异粒子群优化BP的短期风电功率预测模型.首先,采用互信息筛选出原始数据中与输出功率相关度较大的影响因素,减少冗余信息;然后,引入具有自适应惯性权重系数和变异因子思想的粒子群算法对预测模型进行优化.结果表明:与传统预测模型相比,该预测模型具有收敛速度快、预测精度高等特点.
Abstract:
Aiming at the problem that back propagation(BP)neural network prediction model has low prediction accuracy and large input variables in wind power prediction, a short-term wind power prediction model based on mutual information of the adaptive mutation particle swarm optimization BP is proposed. Firstly, the mutual information is used to screen out the influencing factors of the original data with high correlation in the output power, and the redundant information is reduced. Then, the particle swarm algorithm based on the idea of adaptive inertia weight coefficient and variation factor is introduced to optimize the prediction model. The results show that compared with the traditional prediction model, the proposed model has the characteristics of fast convergence and high prediction accuracy.

参考文献/References:

[1] SAROHA S,AGGARWAL S K.Wind power forecasting using wavelet transforms and neural networks with tapped delay[J].CSEE Journal of Power and Energy Systems,2018,4(2):197-209.DOI:10.17775/CSEEJPES.2016.00970.
[2] 杨茂,周宜.计及风电场状态的风电功率超短期预测[J].中国电机工程学报,2019,39(5):1259-1268.DOI:10.13334/j.0258-8013.pcsee.180873.
[3] WU Yuankang,SU Po’en,WU Tingyi,et al.Probabilistic wind power forecasting using weather ensemble models[J].IEEE Transactions on Industry Applications,2018,54(6):5609-5620.DOI:10.1109/TIA.2018.2858183.
[4] 薛禹胜,郁琛,赵俊华,等.关于短期及超短期风电功率预测的评述[J].电力系统自动化,2015,39(6):141-150.DOI:10.7500/AEPS20141218003.
[5] 马聪.基于BP神经网络优化的风电场短期功率预测研究[D].昆明:昆明理工大学,2017.
[6] WANG Zheng,WANG Bo,LIU Chun,et al.Improved BP neural network algorithm to wind power forecast[J].The Journal of Engineering,2017,2017(13):940-943.DOI:10.1049/joe.2017.0469.
[7] 徐龙博,王伟,张滔,等.基于神经网络平均影响值的超短期风电功率预测[J].电力系统自动化,2017,41(21):40-45.DOI:10.7500/AEPS20170321005.
[8] 张颖超,王雅晨,邓华,等.基于IAFSA-BPNN的短期风电功率预测[J].电力系统保护与控制,2017,45(7):58-63.DOI:10.7667/PSPC16048.
[9] WANG Yun,HU Qinghua,SRINIVASAN D,et al.Wind power curve modeling and wind power forecasting with inconsistent data[J].IEEE Transactions on Sustainable Energy,2018,1(1):16-25.DOI:10.1109/TSTE.2018.2820 198.
[10] 刘红柳,杨茂,于宁,等.风向空间分散性及其对全场风电功率预测误差的影响[J].电测与仪表,2017,54(12):54-59.DOI:10.3969/j.issn.1001-1390.2017.12.009.
[11] 李长松,刘凯,肖先勇,等.基于条件互信息特征选择法和Adaboost算法的电能质量复合扰动分类[J].高电压技术,2019,45(2):579-585.DOI:10.13336/j.1003-6520.hve.20190130031.
[12] 史坤鹏,乔颖,赵伟,等.计及历史数据熵关联信息挖掘的短期风电功率预测[J].电力系统自动化,2017,41(3):13-18.DOI:10.7500/AEPS20160504020.
[13] MARINONI A,GAMBA P.Unsupervised data driven feature extraction by means of mutual information maximization[J].IEEE Transactions on Computational Imaging,2017,3(2):243-253.DOI:10.1109/TCI.2017.2669731.
[14] 李丹,任洲洋,颜伟,等.基于因子分析和神经网络分位数回归的月度风电功率曲线概率预测[J].中国电机工程学报,2017,37(18):34-43.DOI:10.13334/j.0258-8013.pcsee.161368.
[15] WANG Shuangxin,LI Meng,ZHAO Long,et al.Short-term wind power prediction based on improved small-world neural network[J].Neural Computing and Applications,2019,31(7):3173-3185.DOI:10.1007/s00521-017-3262-7.
[16] 王功臣,邓长虹,夏沛,等.考虑机组优化选取的含风电电网滚动优化调度方法[J].电力系统自动化,2017,41(11):55-60.DOI:10.7500/AEPS20160922005.
[17] ROCHA H R O,SILVESTRE L J,CELESTE W C,et al.Forecast of distributed electrical generation system capacity based on seasonal micro generators using ELM and PSO[J].IEEE Latin America Transactions,2018,16(4):1136-1141.DOI:10.1109/TLA.2018.8362148.
[18] 丁明,张超,王勃,等.基于功率波动过程的风电功率短期预测及误差修正[J].电力系统自动化,2019,43(3):8-18.DOI:10.7500/AEPS20180322011.

备注/Memo

备注/Memo:
收稿日期: 2019-06-25
通信作者: 曾亮(1980-),男,教授,博士,主要从事机器视觉与人工智能、优化计算方法、复杂系统建模、调度与优化的研究.E-mail:zengliang@hbut.edu.cn.
基金项目: 国家自然科学基金资助项目(41601394); 湖北工业大学博士科研启动基金资助项目(BSQD2017008)
更新日期/Last Update: 2020-01-20