Physics-Guided Machine Learning for Wind-Farm Power Prediction:: Toward Interpretability and Generalizability

Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability

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Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

@article{7f60d4f825164093b39d7e1d1b7b3b39,

title = "Physics-Guided Machine Learning for Wind-Farm Power Prediction:: Toward Interpretability and Generalizability",

author = "Navid Zehtabiyan-Rezaie and Alexandros Iosifidis and Mahdi Abkar",

year = "2023",

month = mar,

doi = "10.1103/PRXEnergy.2.013009",

language = "English",

volume = "2",

journal = "PRX Energy",

publisher = "APS Press",

number = "1",

}

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - Physics-Guided Machine Learning for Wind-Farm Power Prediction:

T2 - Toward Interpretability and Generalizability

AU - Zehtabiyan-Rezaie, Navid

AU - Iosifidis, Alexandros

AU - Abkar, Mahdi

PY - 2023/3

Y1 - 2023/3

U2 - 10.1103/PRXEnergy.2.013009

DO - 10.1103/PRXEnergy.2.013009

M3 - Journal article

VL - 2

JO - PRX Energy

JF - PRX Energy

IS - 1

M1 - 013009

ER -