Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

Accurate estimation of wind and solar energy potentials is crucial for successfully integrating renewable energy into power grids. Traditional numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, often suffer from biases that lead to inaccurate energy foreca...

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Bibliographic Details
Main Authors: Abigail Birago Adomako, Ehsan Jolous Jamshidi, Yusri Yusup, Emad Elsebakhi, Mohd Hafiidz Jaafar, Muhammad Izzuddin Syakir Ishak, Hwee San Lim, Mardiana Idayu Ahmad
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Ecological Informatics
Subjects:
WRF model
Deep learning
Bias correction
Parameterization
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954124004400
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