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    A Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network

    Source: Journal of Energy Engineering:;2024:;Volume ( 150 ):;issue: 006::page 04024035-1
    Author:
    Donglai Wei
    ,
    Zhongda Tian
    DOI: 10.1061/JLEED9.EYENG-5474
    Publisher: American Society of Civil Engineers
    Abstract: Predicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search Algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators.
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      A Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304187
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    contributor authorDonglai Wei
    contributor authorZhongda Tian
    date accessioned2025-04-20T10:11:45Z
    date available2025-04-20T10:11:45Z
    date copyright10/10/2024 12:00:00 AM
    date issued2024
    identifier otherJLEED9.EYENG-5474.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304187
    description abstractPredicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search Algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators.
    publisherAmerican Society of Civil Engineers
    titleA Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network
    typeJournal Article
    journal volume150
    journal issue6
    journal titleJournal of Energy Engineering
    identifier doi10.1061/JLEED9.EYENG-5474
    journal fristpage04024035-1
    journal lastpage04024035-19
    page19
    treeJournal of Energy Engineering:;2024:;Volume ( 150 ):;issue: 006
    contenttypeFulltext
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