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Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder.

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Title: Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder.
Authors: Wang, Lin1 (AUTHOR) wanglin982@gmail.com, Tao, Rui1 (AUTHOR) taoruihust@foxmail.com, Hu, Huanling1 (AUTHOR) huanlinghhl@163.com, Zeng, Yu-Rong1,2 (AUTHOR) zyr@hbue.edu.cn
Source: Renewable Energy: An International Journal. Feb2021, Vol. 164, p642-655. 14p.
Subject Terms: *Wind power, Deep learning, Forecasting, Standard deviations, Wind forecasting
Abstract: Accurate wind power prediction can improve the safety and reliability of power grid operation. In this study, a novel deep learning network stacked by independent recurrent autoencoder (IRAE) is designed according to the characteristics of ultra-short-term wind power data, hereafter called SIRAE (staked independently recurrent autoencoder). This approach accommodates a sheer volume of data in the smart energy era and overcomes the effects of random changes in the natural environment. First, the original sequence is decomposed into sub-sequences through variational mode decomposition techniques. Second, each IRAE extracts the structural features and deep connections of the data through unsupervised pre-training and finds an appropriate initial state. Finally, all IRAEs are stacked into SIRAE and the parameters of each layer for SIRAE are adjusted through supervised training. The results of two comparative experiments show that SIRAE model outperforms the existing popular models. In the extended application, compared with the persistence model, SIRAE shows 18.46%, 31.16%, 9.06% and 34.24% improvements in terms of root mean square error for March, June, September, and December. Therefore, SIRAE is an appropriate tool for ultra-short-term wind power prediction on account of its effective and stable forecasting performance. • Propose a deep learning model (SIRAE) considering characteristics of wind power. • Propose new independently recurrent autoencoder to stack into deep learning network. • Effectively control the calculation cost through the SIRAE model. • Decompose original sequence into subsequences through VMD to remove noise from data. • SIRAE outperforms existing models for ultra-short-term wind power forecasting. [ABSTRACT FROM AUTHOR]
Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Lin%22">Wang, Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wanglin982@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tao%2C+Rui%22">Tao, Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> taoruihust@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Huanling%22">Hu, Huanling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huanlinghhl@163.com</i><br /><searchLink fieldCode="AR" term="%22Zeng%2C+Yu-Rong%22">Zeng, Yu-Rong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zyr@hbue.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Renewable+Energy%3A+An+International+Journal%22">Renewable Energy: An International Journal</searchLink>. Feb2021, Vol. 164, p642-655. 14p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate wind power prediction can improve the safety and reliability of power grid operation. In this study, a novel deep learning network stacked by independent recurrent autoencoder (IRAE) is designed according to the characteristics of ultra-short-term wind power data, hereafter called SIRAE (staked independently recurrent autoencoder). This approach accommodates a sheer volume of data in the smart energy era and overcomes the effects of random changes in the natural environment. First, the original sequence is decomposed into sub-sequences through variational mode decomposition techniques. Second, each IRAE extracts the structural features and deep connections of the data through unsupervised pre-training and finds an appropriate initial state. Finally, all IRAEs are stacked into SIRAE and the parameters of each layer for SIRAE are adjusted through supervised training. The results of two comparative experiments show that SIRAE model outperforms the existing popular models. In the extended application, compared with the persistence model, SIRAE shows 18.46%, 31.16%, 9.06% and 34.24% improvements in terms of root mean square error for March, June, September, and December. Therefore, SIRAE is an appropriate tool for ultra-short-term wind power prediction on account of its effective and stable forecasting performance. • Propose a deep learning model (SIRAE) considering characteristics of wind power. • Propose new independently recurrent autoencoder to stack into deep learning network. • Effectively control the calculation cost through the SIRAE model. • Decompose original sequence into subsequences through VMD to remove noise from data. • SIRAE outperforms existing models for ultra-short-term wind power forecasting. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.renene.2020.09.108
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 642
    Subjects:
      – SubjectFull: Wind power
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Wind forecasting
        Type: general
    Titles:
      – TitleFull: Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder.
        Type: main
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          Name:
            NameFull: Wang, Lin
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            NameFull: Tao, Rui
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            NameFull: Hu, Huanling
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            NameFull: Zeng, Yu-Rong
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          Dates:
            – D: 01
              M: 02
              Text: Feb2021
              Type: published
              Y: 2021
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              Value: 164
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            – TitleFull: Renewable Energy: An International Journal
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