End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Vohra, Rushil (Student TU Delft)
Rajaei, A. (TU Delft Intelligent Electrical Power Grids)
Cremer, Jochen (TU Delft Intelligent Electrical Power Grids)

2023

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.

multi-modal learning
end-to-end learning
optimal power flow
power forecasting

http://resolver.tudelft.nl/uuid:951d7cf7-ee9a-4db5-9a53-0ad8df70b95b

https://doi.org/10.1109/PowerTech55446.2023.10202904

IEEE, Piscataway

2024-02-09

978-1-6654-8779-5

Proceedings of the 2023 IEEE Belgrade PowerTech

2023 IEEE Belgrade PowerTech, 2023-06-25 → 2023-06-29, Belgrade, Serbia

2023 IEEE Belgrade PowerTech, PowerTech 2023

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Rushil Vohra, A. Rajaei, Jochen Cremer