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

Conference paper (2023)

Authors

Rushil Vohra Student
A. Rajaei Intelligent Electrical Power Grids - EEMCS
Jochen Cremer Intelligent Electrical Power Grids - EEMCS
Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Copyright: © 2023 Rushil Vohra, A. Rajaei, Jochen Cremer
DOI
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https://doi.org/10.1109/PowerTech55446.2023.10202904
Optimal power flow Multi-modal learning End-to-end learning Power forecasting
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

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.