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

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 bet...

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Main Authors:	Nantian Huang, Enkai Xing, Guowei Cai, Zhiyong Yu, Bin Qi, Lin Lin
Format:	Article
Language:	English
Published:	MDPI AG 2018-06-01
Series:	Energies
Subjects:	wind speed forecasting low redundancy feature selection complementary ensemble empirical mode de-composition
Online Access:	http://www.mdpi.com/1996-1073/11/7/1638

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record_format	Article
spelling	doaj-76a094e1f5034d51883674d3376c47162020-11-24T23:18:02ZengMDPI AGEnergies1996-10732018-06-01117163810.3390/en11071638en11071638Short-Term Wind Speed Forecasting Based on Low Redundancy Feature SelectionNantian Huang0Enkai Xing1Guowei Cai2Zhiyong Yu3Bin Qi4Lin Lin5School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaEconomic Research Institute, State Grid Xinjiang Electric Power Limited Company, Urumchi 830000, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaWind 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.http://www.mdpi.com/1996-1073/11/7/1638wind speed forecastinglow redundancyfeature selectioncomplementary ensemble empirical mode de-composition
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Nantian Huang Enkai Xing Guowei Cai Zhiyong Yu Bin Qi Lin Lin
spellingShingle	Nantian Huang Enkai Xing Guowei Cai Zhiyong Yu Bin Qi Lin Lin Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection Energies wind speed forecasting low redundancy feature selection complementary ensemble empirical mode de-composition
author_facet	Nantian Huang Enkai Xing Guowei Cai Zhiyong Yu Bin Qi Lin Lin
author_sort	Nantian Huang
title	Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
title_short	Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
title_full	Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
title_fullStr	Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
title_full_unstemmed	Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
title_sort	short-term wind speed forecasting based on low redundancy feature selection
publisher	MDPI AG
series	Energies
issn	1996-1073
publishDate	2018-06-01
description	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.
topic	wind speed forecasting low redundancy feature selection complementary ensemble empirical mode de-composition
url	http://www.mdpi.com/1996-1073/11/7/1638
work_keys_str_mv	AT nantianhuang shorttermwindspeedforecastingbasedonlowredundancyfeatureselection AT enkaixing shorttermwindspeedforecastingbasedonlowredundancyfeatureselection AT guoweicai shorttermwindspeedforecastingbasedonlowredundancyfeatureselection AT zhiyongyu shorttermwindspeedforecastingbasedonlowredundancyfeatureselection AT binqi shorttermwindspeedforecastingbasedonlowredundancyfeatureselection AT linlin shorttermwindspeedforecastingbasedonlowredundancyfeatureselection
_version_	1725582135829463040

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

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