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Ensemble learning for wind profile prediction with missing values

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Abstract

In this paper, we aim to develop computational intelligence approaches for wind profile prediction. Specifically, we focus on two aspects in this work. First, we investigate the missing value recovery for wind data. Due to the complexity of data collection in such processes, wind data normally include missing values. Therefore, how to effectively recover such missing values for learning and prediction is an important aspect for wind profile prediction. Second, we develop an ensemble learning approach based on multiple neural network models. Our proposed method uses a new strategy based on the temporal information to assign the weights for each model dedicated for wind profile prediction to achieve better prediction performance. Various simulation studies and statistical testing demonstrate the effectiveness of our approach.

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Acknowledgments

This work was supported in part by the National Science Foundation (NSF) under CAREER Grant ECCS 1053717 and National Natural Science Foundation of China (NSFC) under grant 50937002.

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Authors and Affiliations

  1. Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Haibo He

  2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yuan Cao

  3. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, Jiangsu, 210042, People’s Republic of China

    Yi Cao

  4. College of Electrical and Electronic Engineering (CEEE), Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Jinyu Wen

Authors
  1. Haibo He
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  2. Yuan Cao
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  3. Yi Cao
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  4. Jinyu Wen
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Corresponding author

Correspondence to Haibo He.

Additional information

This work was performed when Yi Cao was a Visiting Scholar at the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island.

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He, H., Cao, Y., Cao, Y. et al. Ensemble learning for wind profile prediction with missing values. Neural Comput & Applic 22, 287–294 (2013). https://doi.org/10.1007/s00521-011-0708-1

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  • DOI: https://doi.org/10.1007/s00521-011-0708-1

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