Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection

Nantian Huang, Enkai Xing, Guowei Cai, Zhiyong Yu, Bin Qi and Lin Lin
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Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Enkai Xing: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Guowei Cai: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Zhiyong Yu: Economic Research Institute, State Grid Xinjiang Electric Power Limited Company, Urumchi 830000, China
Bin Qi: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Lin Lin: College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China

Energies, 2018, vol. 11, issue 7, 1-19

Abstract: Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments.

Keywords: wind speed forecasting; low redundancy; feature selection; complementary ensemble empirical mode de-composition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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