基于卡尔曼滤波的风速序列短期预测方法

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (656 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract The prediction mechanism of Kalman filtering for wind speed series is analyzed. And a hysteretic neural network is proposed to predict the wind speed series. The hybrid prediction of wind speed series, combining ARMA model and hysteretic neural network, based on Kalman filtering fusion is completed. Hysteretic property is brought into neural network by changing the convention activation function to hysteretic activation function. The connective weights of network are determined by gradient optimization, and hysteretic parameters are determined by genetic algorithm. State equation is established by ARMA model. The prediction results of hysteretic neural network are taken as measurement values. Hybrid prediction can prevent error accumulation caused by the single prediction mechanism. Simulation results show that the hysteretic neural network has better prediction performance than BP neural network, and the hybrid prediction is superior to each single method.

Key words： Kalman filtering wind speed series neural network ARMA model prediction

Received: 17 July 2013 Published: 18 June 2014

PACS:

TK89

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xiu Chunbo
	Ren Xiao
	Li Yanqing
	Liu Mingfeng

Cite this article:

Xiu Chunbo,Ren Xiao,Li Yanqing等. Short-Term Prediction Method of Wind Speed Series Based on <br/>Kalman Filtering Fusion[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 253-259.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I2/253

[1] 范高峰, 王伟胜, 刘纯, 等. 基于人工神经网络的风电功率预测[J]. 中国电机工程学报, 2008, 28(34): 118-123.
Fan Gaofeng, Wang Weisheng, Liu Chun, et al. Wind power prediction based on artificial neural network[J]. Proceedings of the CSEE, 2008, 28(34): 118-123.
[2] 彭怀午, 刘方锐, 杨晓峰. 基于组合预测方法的风电场短期风速预测[J]. 太阳能学报, 2011, 32(4): 543-547.
Peng Huaiwu, Liu Fangrui, Yang Xiaofeng. Short term wind speed forecast based on combined prediction[J]. Acta Energiae Solaris Sinica, 2011, 32(4): 543-547.
[3] 米增强, 刘兴杰, 张艳青, 等. 基于混沌分析和神经网络的风速直接多步预测[J]. 太阳能学报, 2011, 32(6): 901-906.
Mi Zengqiang, Liu Xingjie, Zhang Yanqing, et al. Direct multi-step forecasting of short-term wind speed based on chaos analysis and neural network[J]. Acta Energiae Solaris Sinica, 2011, 32(6): 901-906.
[4] 武峰雨, 乐秀璠, 南东亮. 相空间重构的极端学习机短期风速预测模型[J]. 电力系统及其自动化学报, 2013, 25(1): 136-141.
Wu Fengyu, Le Xiufan, Nan Dongliang. A short-term wind speed prediction model using phase-space reconstructed extreme learning machine[J]. Proceedings of the CSU-EPSA, 2013, 25(1): 136-141.
[5] 吴栋梁, 王扬, 郭创新, 等. 基于改进GMDH网络的风电场短期风速预测[J]. 电力系统保护与控制, 2011, 39(2): 88-93, 111.
Wu Dongliang, Wang Yang, Guo Chuangxin, et al. Short-term wind speed forecasting in wind farm based on improved GMDH network[J]. Power System Protection and Control, 2011, 39(2): 88-93, 111.
[6] 陈盼, 陈皓勇, 叶荣. 基于多尺度形态学分析的风速预测[J]. 电力系统保护与控制, 2010, 38(21): 12-18.
Chen Pan, Chen Haoyong, Ye Rong. Wind speed forecasting based on multi-scale morphological analysis[J]. Power System Protection and Control, 2010, 38(21): 12-18.
[7] 吕涛, 唐巍, 所丽. 基于混沌相空间重构理论的风电场短期风速预测[J]. 电力系统保护与控制, 2010, 38(21): 113-117.
Lü Tao, Tang Wei, Suo Li. Prediction of short-term wind speed in wind farm based on chaotic phase space reconstruction theory[J]. Power System Protection and Control, 2010, 38(21): 113-117.
[8] 栗然, 陈倩, 徐宏锐. 考虑相关因素的最小二乘支持向量机风速预测方法[J]. 电力系统保护与控制, 2010, 38(21): 146-151.
Li Ran, Chen Qian, Xu Hongrui. Wind speed forecasting method based on LS-SVM considering the related factors[J]. Power System Protection and Control, 2010, 38(21): 146-151.
[9] 罗文, 王莉娜. 风场短期风速预测研究[J]. 电工技术学报, 2011, 26(7): 68-74.
Luo Wen, Wang Lina. Short-term wind speed forecasting for wind farm[J]. Transactions of China Electrotechnical Society, 2011, 26(7): 68-74.
[10] Ergin Erdem, Jing Shi. ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied Energy, 2011, 88(4): 1405-1414.
[11] 刘兴杰, 米增强, 杨奇逊, 等. 一种基于EMD 的短期风速多步预测方法[J]. 电工技术学报, 2010, 25(4): 165-170.
Liu Xingjie, Mi Zengqiang, Yang Qixun, et al. A novel multi-step prediction for wind speed based on EMD[J]. Transactions of China Electrotechnical Society, 2010, 25(4): 165-170.
[12] 孙国强, 卫志农, 翟玮星. 基于RVM与ARMA误差校正的短期风速预测[J]. 电工技术学报, 2012, 27(8): 187-193.
Sun Guoqiang, Wei Zhinong, Zhai Weixing. Short term wind speed forecasting based on RVM and ARMA error correcting[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 187-193.
[13] 陈妮亚, 钱政, 孟晓风, 等. 基于空间相关法的风电场风速多步预测模型[J]. 电工技术学报, 2013, 28(5): 15-21.
Chen Niya, Qian Zheng, Meng Xiaofeng, et al. Multi-step ahead wind speed forecasting model based on spatial correlation and support vector machine[J]. Transactions of China Electrotechnical Society, 2013, 28(5): 15-21.
[14] 潘迪夫, 刘辉, 李燕飞. 基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J]. 电网技术, 2008, 32(7): 82-86.
Pan Difu, Liu Hui, Li Yanfei. A wind speed forecasting optimization model for wind farms based ontime series analysis and Kalman filter algorithm[J]. Power System Technology, 2008, 32(7): 82-86.
[15] 潘迪夫, 刘辉, 李燕飞. 风电场风速短期多步预测改进算法[J]. 中国电机工程学报, 2008, 28(26): 87-91.
Pan Difu, Liu Hui, Li Yanfei. Optimization algorithm of short-term multi-step wind speed forecast[J]. Proceedings of the CSEE, 2008, 28(26): 87-91.
[16] Liu Hui, Tian Hongqi, Li Yanfei. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J]. Applied Energy, 2012, 98(12): 415-424.