GTC 2023 Talk - Diffusion on the Clouds: Short-Term Solar Energy Forecasting with Diffusion Models
๐ฅ This video is from the GTC 2023 talk, โDiffusion on the Clouds: Short-Term Solar Energy Forecasting with Diffusion Modelsโ, given by Thomas Capelle of Weights & Biases. โ๏ธ
We present a diffusion modeling pipeline that can generate predictions of cloud movement in the sky. Current models based on recurrent neural networks and optical flow are slow and hard to train. Based on recent work in diffusion models for video generation, we implement a pipeline-based Denoising Diffusion Probabilistic Models (DDPM) (https://arxiv.org/abs/2006.11239) to predict the solar resource in future time-steps aโฆ
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Chapters (21)
Intro
0:14
Motivation
0:30
Solar Energy Forecasting importance
1:19
Steadysun presentation
1:50
Satellite imagery description
3:08
Optical Flow and cloud movement forecasting for satellite imagery
4:10
How to leverage Diffusion models for cloud movement forecasting?
5:04
Denoising Diffusion Probabilistic Models
6:49
Diffusion for next frame prediction and Moving MNIST
9:00
Nvidia hardware and containers.
9:36
Moving MNIST results on W&B
10:59
The W&B dashboard to keep track of everything
12:23
Diffusion models for the clouds
14:25
Organizing results on W&B reports
15:38
Stochastic nature of diffusion models: A great feature!
16:33
Bigger model results
17:15
Stable Diffusion VAE works for clouds also!
17:53
Fully transformer based model on full size images: Simple Diffusion
18:42
Why non deterministic forecast is important
19:35
The modelling chain for solar forecasting.
20:10
Conclusion and future work
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