PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting

Key points

• Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics.1
• Improved forecasting of cloud motion, in turn, enhances solar power production forecasts and facilitates the integration of additional photovoltaic (PV) sources into the power grid.1
• In this work, we adopt a graph signal processing perspective and model advection-diffusion particles as signals on a graph to capture the dynamics of these processes.1
• Furthermore, the PING model is successfully coupled with models for forecasting PV power production.1

Sources (1)

• [1]PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 12:33 PM

  We introduce a Physics-Informed Graph Neural Network (PING) for forecasting future advection-diffusion particle concentrations on irregular grids.

  Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics.

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