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A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting.

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Title: A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting.
Authors: Liu, Zhi-Feng1 (AUTHOR) liuzhifeng@tust.edu.cn, Liu, You-Yuan1 (AUTHOR) liuyouyuan@mail.tust.edu.cn, Chen, Xiao-Rui1 (AUTHOR) 21021127@mail.tust.edu.cn, Zhang, Shu-Rui1 (AUTHOR) zsr030923@mail.tust.edu.cn, Luo, Xing-Fu1 (AUTHOR) fu406699@mail.tust.edu.cn, Li, Ling-Ling1,2,3 (AUTHOR) lilinglinghebut@126.com, Yang, Yi-Zhou1 (AUTHOR) 22028121@mail.tust.edu.cn, You, Guo-Dong1 (AUTHOR)
Source: Applied Energy. Apr2024, Vol. 360, pN.PAG-N.PAG. 1p.
Subjects: Wind power, Evolutionary models, Deep learning, Energy consumption, Wind power plants, Evolutionary algorithms, Electric power distribution grids
Geographic Terms: France
Abstract: Wind power generation plays a crucial role in promoting the transformation and advancement of the power industry and fostering sustainable development in society. However, wind power generation is susceptible to external factors, exhibiting significant volatility and randomness, which can adversely affect the stable operation of the power grid. The uncertainty associated with wind power generation can be effectively addressed through wind power prediction technology. While numerous wind power prediction methods have been developed, challenges remain, including imperfect data processing mechanisms and the optimization of model parameters, which hinder the effective utilization of wind power generation. To overcome these challenges, this research proposes a novel deep learning-based evolutionary model with a potential attention mechanism and memory decay-enhancement strategy for short-term wind power point-interval forecasting. The proposed model demonstrates high prediction stability and accuracy for both point values and intervals of short-term wind power, even under complex environmental and multi-seasonal conditions. The effectiveness of the proposed methods and strategies are validated using a measured dataset from the La Haute Borne wind farm in France. The results consistently show that, in complex environmental scenarios, the point prediction evaluation index of determination coefficient exceeds 90% and the interval prediction evaluation index of PI coverage probability exceeds 80%. Accurate short-term wind power point-interval forecasting contributes to enhancing the stable operation of the power system and improving the efficiency of wind energy utilization. • A deep learning-based evolutionary model is proposed. • A social grade update strategy-based evolutionary algorithm is presented. • An innovative potential attention mechanism is formulated. • A novel wind power interval prediction method is devised. [ABSTRACT FROM AUTHOR]
Copyright of Applied Energy is the property of Elsevier B.V. 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 175873974
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhi-Feng%22">Liu, Zhi-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuzhifeng@tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+You-Yuan%22">Liu, You-Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuyouyuan@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiao-Rui%22">Chen, Xiao-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 21021127@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shu-Rui%22">Zhang, Shu-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zsr030923@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Xing-Fu%22">Luo, Xing-Fu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fu406699@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ling-Ling%22">Li, Ling-Ling</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> lilinglinghebut@126.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yi-Zhou%22">Yang, Yi-Zhou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 22028121@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22You%2C+Guo-Dong%22">You, Guo-Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+models%22">Evolutionary models</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22France%22">France</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Wind power generation plays a crucial role in promoting the transformation and advancement of the power industry and fostering sustainable development in society. However, wind power generation is susceptible to external factors, exhibiting significant volatility and randomness, which can adversely affect the stable operation of the power grid. The uncertainty associated with wind power generation can be effectively addressed through wind power prediction technology. While numerous wind power prediction methods have been developed, challenges remain, including imperfect data processing mechanisms and the optimization of model parameters, which hinder the effective utilization of wind power generation. To overcome these challenges, this research proposes a novel deep learning-based evolutionary model with a potential attention mechanism and memory decay-enhancement strategy for short-term wind power point-interval forecasting. The proposed model demonstrates high prediction stability and accuracy for both point values and intervals of short-term wind power, even under complex environmental and multi-seasonal conditions. The effectiveness of the proposed methods and strategies are validated using a measured dataset from the La Haute Borne wind farm in France. The results consistently show that, in complex environmental scenarios, the point prediction evaluation index of determination coefficient exceeds 90% and the interval prediction evaluation index of PI coverage probability exceeds 80%. Accurate short-term wind power point-interval forecasting contributes to enhancing the stable operation of the power system and improving the efficiency of wind energy utilization. • A deep learning-based evolutionary model is proposed. • A social grade update strategy-based evolutionary algorithm is presented. • An innovative potential attention mechanism is formulated. • A novel wind power interval prediction method is devised. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. 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.apenergy.2024.122785
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Wind power
        Type: general
      – SubjectFull: Evolutionary models
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Wind power plants
        Type: general
      – SubjectFull: Evolutionary algorithms
        Type: general
      – SubjectFull: Electric power distribution grids
        Type: general
      – SubjectFull: France
        Type: general
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      – TitleFull: A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting.
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              Text: Apr2024
              Type: published
              Y: 2024
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