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    Zeros-MTL-CLDBN: A Robust Multitask Deep-Learning Framework via Deep-Belief Network with Correntropy Loss for Ultrashort-Term Multistep Wind Power Forecasting

    Source: Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 004::page 04026028-1
    Author:
    Ma, Wentao
    ,
    Dai, Jiahui
    ,
    Chen, Xi
    ,
    Dong, Yuzhuo
    DOI: 10.1061/JLEED9.EYENG-6260
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate ultrashort-term multistep wind power forecasting (WPF) remains a critical challenge in renewable energy systems due to the inherent stochasticity, non-Gaussian fluctuations, and volatility of wind energy. To address this challenge, this ...
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      Zeros-MTL-CLDBN: A Robust Multitask Deep-Learning Framework via Deep-Belief Network with Correntropy Loss for Ultrashort-Term Multistep Wind Power Forecasting

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4312008
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    • Journal of Energy Engineering

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    contributor authorMa, Wentao
    contributor authorDai, Jiahui
    contributor authorChen, Xi
    contributor authorDong, Yuzhuo
    date accessioned2026-08-20T11:18:35Z
    date available2026-08-20T11:18:35Z
    date copyright2026/04/27
    date issued2026
    identifier otherJLEED9.EYENG-6260.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312008
    description abstractAbstractAccurate ultrashort-term multistep wind power forecasting (WPF) remains a critical challenge in renewable energy systems due to the inherent stochasticity, non-Gaussian fluctuations, and volatility of wind energy. To address this challenge, this ...
    publisherAmerican Society of Civil Engineers
    titleZeros-MTL-CLDBN: A Robust Multitask Deep-Learning Framework via Deep-Belief Network with Correntropy Loss for Ultrashort-Term Multistep Wind Power Forecasting
    typeJournal Article
    journal volume152
    journal issue4
    journal titleJournal of Energy Engineering
    identifier doi10.1061/JLEED9.EYENG-6260
    journal fristpage04026028-1
    journal lastpage04026028-15
    page15
    treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 004
    contenttypeFulltext
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