基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (994 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract This paper introduces a combination forecasting model which is based on ensemble empirical mode decomposition and least squares support vector machines(LSSVM) into forecasting short-term wind speed, in the view of excavating the nonstationarity and nonlinearity of wind series. Firstly, wind series are decomposed into a group of relatively stable subsequence by ensemble empirical mode decomposition to reduce mutual influences among diverse trend information. Secondly, build forecasting models respectively for each subsequence adopting least squares support vector machines. Adaptive disturbance particle swarm optimization and model learning feedback mechanism is used to jointly optimize the dimension of the learning sample input and the hyper parameters of LSSVM forecasting model to lower the predicting risk. Finally, the predicting results of each subsequence are superposed to obtain wind speed forecasting results. The case study shows that the proposed combination forecasting model is able to excavate wind series features effectively and has relatively high predicting accuracy.

Key words： Wind speed forecasting ensemble empirical mode decomposition(EEMD) least squares support vector machines adaptive disturbance particle swarm optimization learning effect feedback

Received: 10 April 2013 Published: 27 June 2014

PACS:

TM614

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wang He
	Hu Zhijian
	Zhang Yihui
	Li Chen
	Yang Nan
	Wang Zhansheng

Cite this article:

Wang He,Hu Zhijian,Zhang Yihui等. A Hybrid Model for Short-Term Wind Speed Forecasting Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machines[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 237-245.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I4/237

[1] 张丽英, 叶廷路, 辛耀中, 等. 大规模风电接入电网的相关问题及措施[J]. 中国电机工程学报, 2010, 30(25): 1-9.
Zhang Liying, Ye Tinglu, Xin Yaozhong, et al. Problems and measures of power grid accommodating large scale wind power[J]. Proceedings of the CSEE, 2010, 30(25): 1-9.
[2] 袁铁江, 晁勤, 李义岩, 等. 大规模风电并网电力系统经济调度中风电场出力的短期预测模型[J]. 中国电机工程学报, 2010, 30(13): 23-27.
Yuan Tiejiang, Chao Qin, Li Yiyan, et al. Short-term wind power output forecasting model for economic dispatch of power system incorporating large-scale wind farm[J]. Proceedings of the CSEE, 2010, 30(13): 23-27.
[3] Rajesh G. K, Krithika S. Day-ahead wind speed forecasting using f-ARIMA models[J]. Renewable Energy, 2009, 34(5): 1388-1393.
[4] Peiyuan C, Pedersen T, Bak J B, et al. ARIMA-based time series model of stochastic wind power generation [J]. IEEE Transactions on Power Systems, 2010, 25(2): 667-676.
[5] Cadenas E, Rivera W. Wind speed forecasting in three different regions of Mexico using a hybrid ARIMA-ANN model[J]. Renewable Energy, 2010, 35(7): 2732-2738.
[6] Gong L, Shi J, Zhou J. Bayesian adaptive combination of short-term wind speed forecasts from neural network models[J]. Renewable Energy, 2011, 36(7): 352-359.
[7] Monfared M, Rastegar H, Kojabadi H M. A new strategy for wind speed forecasting using artificial intelligent methods[J]. Renewable Energy, 2009, 34(5): 845-848.
[8] 杨锡运, 孙宝君, 张新房, 等, 基于相似数据的支持向量机短期风速预测仿真研究[J]. 中国电机工程学报, 2012, 32(4): 35-41.
Yang Xiyun, Sun Baojun, Zhang Xinfang, et al. short-term wind speed forecasting based on support vector machine with similar data[J]. Proceedings of the CSEE, 2012, 32(4): 35-41.
[9] 杜颖, 卢继平, 李青, 等. 基于最小二乘支持向量机的风电场短期风速预测[J]. 电网技术, 2008, 32(15): 62-66.
Du Ying, Lu Jiping, Li Qing, et al. Short-term wind speed forecasting of wind farm based on least square-support vector machine[J]. Power System Technology, 2008, 32(15): 62-66.
[10] 王丽婕, 冬雷, 廖晓钟, 等, 基于小波分析的风电场短期发电功率预测[J]. 中国电机工程学报, 2009, 29(28): 30-33.
Wang Lijie, Dong Lei, Liao Xiaozhong, et al, Short-term power prediction of a wind farm based on wavelet analysis[J]. Proceedings of the CSEE, 2009, 29 (28): 30-33.
[11] Liu H, Chen C, Tian H Q, etal. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks[J]. Renewable Energy, 2012, 48: 545-556.
[12] Wu Z, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[13] 冬雷, 王丽婕, 高爽. 基于混沌时间序列的大型风电场发电功率预测建模与研究[J]. 电工技术学报, 2008, 23(12): 125-129.
Dong Lei, Wang Lijie, Gao Shuang. Modeling and analysis of prediction of wind power generation in the large wind farm based on chaotic time series[J]. Transactions of China Electrotechnical Society. 2008. 23(12): 125-129.
[14] 尚万峰, 赵升吨, 申亚京. 遗传优化的最小二乘支持向量机在开关磁阻电机建模中的应用[J]. 中国电机工程学报, 2009, 29(4): 65-69.
Sang Wanfeng, Zhao Shengdun, Shen Yajing. Application of LSSVM optimized by genetic algorithm to modeling of switched reluctance motor[J]. 2009, 29(12): 65-69.
[15] 张春晓, 张涛. 基于最小二乘支持向量机和粒子群算法的两相流含油率软测量方法[J]. 中国电机工程学报, 2010, 30(2): 86-91.
Zhang Chunxiao, Zhang Tao. Soft measurement method for oil holdup of two phase flow based on least squares support vector machine and particle swarm optimization[J]. Proceedings of the CSEE, 2010, 30(2): 86-91.
[16] 吴秋波, 王允诚, 赵秋亮, 等, 混沌惯性权值调整策略的粒子群优化算法[J]. 计算机工程与应用. 2009, 45(7): 49-51.
Wu Qiubo, Wang Yuncheng, Zhao Qiuliang, et al. Particle swarm optimization algorithm with chaotic inertia weight adjusting strategy[J]. Computer Engineering and Applications, 2009, 45(7): 49-51.
[17] 杜玉平. 关于粒子群算法改进的研究[D]. 西安: 西北大学, 2008.
[18] 廖志强, 李太福, 余德均, 等. 基于相空间重构的神经网络短期风速预测[J]. 江南大学学报(自然科学版), 2012, 11(1): 14-18.
Liao Zhiqiang, Li Taifu, Yu Dejun, et al. Short-term wind speed forecasting of neural network based on phase space reconstruction[J]. Journal of Jiangnan University (Natural Science Edition), 2012, 11(1): 14-18.