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    Automated Identification of “Dunkelflaute” Events: A Convolutional Neural Network–Based Autoencoder Approach

    Source: Artificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    Author:
    Bowen Li
    ,
    Sukanta Basu
    ,
    Simon J. Watson
    DOI: 10.1175/AIES-D-22-0015.1
    Publisher: American Meteorological Society
    Abstract: As wind and solar power play increasingly important roles in the European energy system, unfavorable weather conditions, such as “Dunkelflaute” (extended calm and cloudy periods), will pose ever greater challenges to transmission system operators. Thus, accurate identification and characterization of such events from open data streams (e.g., reanalysis, numerical weather prediction, and climate projection) are going to be crucial. In this study, we propose a two-step, unsupervised deep learning framework [wind and solar network (WISRnet)] to automatically encode spatial patterns of wind speed and insolation, and subsequently, identify Dunkelflaute periods from the encoded patterns. Specifically, a deep convolutional neural network (CNN)–based autoencoder (AE) is first employed for feature extraction from the spatial patterns. These two-dimensional CNN-AE patterns encapsulate both amplitude and spatial information in a parsimonious way. In the second step of the WISRnet framework, a variant of the well-known
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      Automated Identification of “Dunkelflaute” Events: A Convolutional Neural Network–Based Autoencoder Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4290398
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    contributor authorBowen Li
    contributor authorSukanta Basu
    contributor authorSimon J. Watson
    date accessioned2023-04-12T18:52:32Z
    date available2023-04-12T18:52:32Z
    date copyright2022/10/20
    date issued2022
    identifier otherAIES-D-22-0015.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290398
    description abstractAs wind and solar power play increasingly important roles in the European energy system, unfavorable weather conditions, such as “Dunkelflaute” (extended calm and cloudy periods), will pose ever greater challenges to transmission system operators. Thus, accurate identification and characterization of such events from open data streams (e.g., reanalysis, numerical weather prediction, and climate projection) are going to be crucial. In this study, we propose a two-step, unsupervised deep learning framework [wind and solar network (WISRnet)] to automatically encode spatial patterns of wind speed and insolation, and subsequently, identify Dunkelflaute periods from the encoded patterns. Specifically, a deep convolutional neural network (CNN)–based autoencoder (AE) is first employed for feature extraction from the spatial patterns. These two-dimensional CNN-AE patterns encapsulate both amplitude and spatial information in a parsimonious way. In the second step of the WISRnet framework, a variant of the well-known
    publisherAmerican Meteorological Society
    titleAutomated Identification of “Dunkelflaute” Events: A Convolutional Neural Network–Based Autoencoder Approach
    typeJournal Paper
    journal volume1
    journal issue4
    journal titleArtificial Intelligence for the Earth Systems
    identifier doi10.1175/AIES-D-22-0015.1
    treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian