Renewable Energy Resources. 2018, (01) Core

Short-term wind speed forecasting of ORELM based on MRMRChinese Full Text

Wang Qi;Guan Tiansheng;Qin Benshuang;Northeast Electric Power Design Institute Co.,Ltd.of China Power Engineering Consulting Group;Electric Power Training Center of Jilin Electric Power Co.of State Grid;School of Electrical Engineering , Northeast Electric Power University;

Abstract: Because of the characteristics of intermittence,randomness and fluctuation,the large-scale wind power integration has important influence on the security and stable operation of power system.This paper puts forward a new method of short-term wind speed prediction of outlier robust extreme learning machine（Outlier Robust Extreme Learning Machine,ORELM）based on maximum correlation and minimum redundancy（Maximum Correlation Minimum Redundancy,MRMR）algorithm. Firstly,the attributes of wind speed are analyzed,and the MRMR algorithm is used to measure the correlation between the characteristics of different wind speed attributes and wind speed. And the input dimension of wind speed attributes is determined. Then,the extreme learning machine（Extreme Learning Machine,ELM）is optimized,and the ORELM wind speed prediction model is constructed. Finally,the wind speed prediction is carried out based on the measured data of a large wind farm in Northeast China. The simulation results show that the proposed method has higher prediction accuracy.

Keywords：

wind speed; short-term forecasting; MRMR; ORELM;

DOI：
10.13941/j.cnki.21-1469/tk.2018.01.013
Series：
(C) Architecture/ Energy/ Traffic/ Electromechanics, etc; (D) Agriculture
Subject：
Electric Power Industry
Classification Code：
TM614

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Short-term wind speed forecasting of ORELM based on MRMRChinese Full Text

Wang Qi;Guan Tiansheng;Qin Benshuang;Northeast Electric Power Design Institute Co.,Ltd.of China Power Engineering Consulting Group;Electric Power Training Center of Jilin Electric Power Co.of State Grid;School of Electrical Engineering , Northeast Electric Power University;

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