基于优化FCM聚类的RELM风速预测Wind Speed Forecasting of Regularized ELM Based on Optimized FCM Clustering
潘超;秦本双;何瑶;袁翀;沈清野;
摘要(Abstract):
准确的风速预测对大规模风电并网具有重要意义。提出一种基于互信息属性约简优化聚类的正则化极限学习机短期风速预测方法。首先考虑不同属性特征对风速的不同影响,计算风速特征属性序列与风速序列的互信息,并运用最大相关最小冗余算法进行特征选择,然后采用优化的模糊C均值聚类方法对风速样本进行聚类,再对极限学习机进行优化,进而构建风速组合预测模型。最后结合风电场实测数据进行风速预测实验,结果表明该方法具有较高的预测精度。
关键词(KeyWords): 风速预测;最大相关最小冗余;模糊C均值聚类;正则化;极限学习机
基金项目(Foundation): 国家863高技术基金项目(SS2014AA052502);; 国家自然科学基金项目(51507027)~~
作者(Author): 潘超;秦本双;何瑶;袁翀;沈清野;
Email:
DOI: 10.13335/j.1000-3673.pst.2017.1200
参考文献(References):
- [1]丁藤,冯冬涵,林晓凡,等.基于修正后ARIMA-GARCH模型的超短期风速预测[J].电网技术,2017,41(6):1809-1815.Ding Teng,Feng Donghan,Lin Xiaofan,et al.Ultra-short-term wind speed forecasting based on improved ARIMA-GARCH model[J].Power System Technology,2017,41(6):1809-1815(in Chinese).
- [2]杨正瓴,刘阳,张泽,等.采用最近历史观测值和PLSR进行空间相关性超短期风速预测[J].电网技术,2017,41(6):1816-1823.Yang Zhengling,Liu Yang,Zhang Ze,et al.Ultra-short-term wind speed prediction with spatial correlation using recent historical observations and PLSR[J].Power System Technology,2017,41(6):1816-1823(in Chinese).
- [3]Hu Q,Zhang S,Yu M,et al.Short-term wind speed or power forecasting with heteroscedastic support vector regression[J].IEEE Transactions on Sustainable Energy,2016,7(1):241-249.
- [4]梁琛,王鹏,韩肖清,等.基于间歇性风速的风力发电机功率输出模型研究[J].电网技术,2017,41(5):1869-1876.Liang Chen,Wang Peng,Han Xiaoqing,et al.Intermittent wind speed model and wind turbine output model[J].Power System Technology,2017,41(5):1869-1876(in Chinese).
- [5]Cadenas E,Rivera W,Campos-Amezcua R,et al.Wind speed prediction using a univariate ARIMA model and a multivariate NARX model[J].Energies,2016,9(2):109-118.
- [6]田中大,李树江,王艳红,等.基于小波变换的风电场短期风速组合预测[J].电工技术学报,2015,30(9):113-120.Tian Zhongda,Li Shujiang,Wang Yanhong,et al.Short-term wind speed combined prediction for wind farms based on wavelet transform[J].Transactions of China Electrotechnical Society,2015,30(9):113-120(in Chinese).
- [7]杨薛明,边继飞,朱霄珣,等.基于最大熵混沌时间序列的支持向量机短期风速预测模型研究[J].太阳能学报,2016,37(9):2173-2179.Yang Xueming,Bian Jifei,Zhu Xiaoxun,et al.Short term wind speed prediction model based on support vector machine using maximum entropy of chaotic time series[J].Acta Energiae Solaris Sinica,2016,37(9):2173-2179(in Chinese).
- [8]修春波,任晓,李艳晴,等.基于卡尔曼滤波的风速序列短期预测方法[J].电工技术学报,2014,29(2):253-259.Xiu Chunbo,Ren Xiao,Li Yanqing,et al.Short-term prediction method of wind speed series based on Kalman filtering fusion[J].Transactions of China Electrotechnical Society,2014,29(2):253-259(in Chinese).
- [9]刘兴杰,岑添云,郑文书,等.基于模糊粗糙集与改进聚类的神经网络风速预测[J].中国电机工程学报,2014,34(19):3162-3169.Liu Xingjie,Cen Tianyun,Zheng Wenshu,et al.Neural network wind speed prediction based on fuzzy rough set and improved clustering[J].Proceedings of the CSEE,2014,34(19):3162-3169(in Chinese).
- [10]Shrivastava N A,Lohia K,Panigrahi B K.A multiobjective framework for wind speed prediction interval forecasts[J].Renewable Energy,2016,8(7):903-910.
- [11]Fan S,Liao J R,Yokoyama R,et al.Forecasting the wind generation using a two-stage network based on meteorological information[J].IEEE Transactions on Energy Conversion,2009,24(2):474-482.
- [12]Huang G B.An insight into extreme learning machines:random neurons random features and kernels[J].Cognitive Computation,2014,6(3):376-390.
- [13]Martínez-Martínez J M,Escandell-Montero P,Soria-Olivas E,et al.Regularized extreme learning machine for regression problems[J].Neurocomputing,2011,74(17):3716-3721.
- [14]Wang Jianzhou,Wang Yun,Jiang Ping.The study and application of a novel hybrid forecasting model-a case study of wind speed forecasting in China[J].Applied Energy,2015(143):472-488.
- [15]Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1-3):489-501.
- [16]Sfetsos A.A novel approach for the forecasting of mean hourly wind speed time series[J].Renewable Energy,2002,27(2):163-174.
- [17]李扬,顾雪平.基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J].中国电机工程学报,2013,33(34):179-186.Li Yang,Gu Xueping.Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J].Proceedings of the CSEE,2013,33(34):179-186(in Chinese).
- [18]Peng H,Long F,Ding C.Feature selection based on mutual information:criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2005,27(8):1226-38.
- [19]Mishra N S,Ghosh S,Ghosh A.Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images[J].Applied Soft Computing,2012,12(8):2683-2692.
- [20]Yu Wenkai,Yao Xuri,Liu Xuefeng,et al.Ghost imaging based on Pearson correlation coefficients[J].Chinese Physics B,2015,24(5):344-349.
- [21]Huang G B.What are extreme learning machines?filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle[J].Cognitive Computation,2015,7(3):263-278.