Deep generative model for probabilistic wind speed and wind power estimation at a wind farm

Deep generative model for probabilistic wind speed and wind power estimation at a wind farm

Abstract This work introduces a novel method to generate probabilistic hub‐height wind speed forecasts aimed at power output prediction. We employ state‐of‐the‐art convolutional variational autoencoders (CVAEs) trained with historical wind speed observations, multivariable outputs (wind speed, direc...

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Bibliographic Details
Main Authors: Andrés A. Salazar, Jiafeng Zheng, Yuzhang Che, Feng Xiao
Format: Article
Language:English
Published: Wiley 2022-06-01
Series:Energy Science & Engineering
Subjects:
convolutional variational autoencoder
generative model
hub‐height wind
probabilistic forecasting
wind power
Online Access:https://doi.org/10.1002/ese3.1086

Internet

https://doi.org/10.1002/ese3.1086

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