Jun 2021 – Jul 2021 · 1 mo · Remote

• Literature review of the existing state-of-the-art deep learning architectures for wind speed forecasting.
• Came up with an advance novel deep learning architecture (inspired from Wavenet and Graph Neural Network), which can efficiently learn temporal and spatial patterns.
• Our model outperformed the state-of-the-art benchmark models for wind speed forecasting and our work got accepted in IEEE BigData 2021.