An international research team developed an open-source framework for intraday, national-scale PV forecasting that combines satellite-based deep learning, optical-flow techniques, and numerical weather prediction models.
A research team from Switzerland and the Netherlands presented a novel framework for intraday spatiotemporal PV power prediction at the national scale. The new framework combines satellite-based deep learning and optical-flow approaches together with physics-based numerical weather prediction models.
“Knowing beforehand how much solar energy will be generated can improve short-term power production planning of additional power sources,” author Angela Meyer from Delft University of Technology (TU Delft) said in a statement. “You can act strategically when there are surpluses or shortages of energy and reduce electricity costs. It’s also highly relevant for keeping the energy grid in balance.”
This method, which Meyer developed with Luca Lanzilao from Bern University of Applied Sciences, is publicly accessible to everyone, including companies. “If energy companies know what will be generated, costs can be reduced because they don’t have to take ad‑hoc measures to keep the system balanced,” Meyer explained. “I believe public organizations have an important role in protecting consumers, so they don’t end up paying unnecessarily high prices for their energy.”
